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Pectin Extraction from Lemon By-product with Acidified Date Juice: Rheological Properties and Microstructure of Pure and Mixed Pectin Gels M. Masmoudi, S. Besbes, I. Ben Thabet, C. Blecker and H. Attia Food Science and Technology International 2010 16: 105 originally published online 5 February 2010 DOI: 10.1177/1082013209353093 The online version of this article can be found at: http://fst.sagepub.com/content/16/2/105

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Pectin Extraction from Lemon By-product with Acidified Date Juice: Rheological Properties and Microstructure of Pure and Mixed Pectin Gels M. Masmoudi,1,* S. Besbes,1,* I. Ben Thabet,1 C. Blecker2 and H. Attia1 1

Unite´ Analyses Alimentaires, Ecole Nationale d’Inge´nieurs de Sfax, Route de Soukra 3038 Sfax, Tunisia 2 Unite´ de Technologie des Industries Agro-alimentaires, Faculte´ des Sciences Agronomiques de Gembloux, passage des De´porte´s 2, 5030 Gembloux, Belgium The microstructure and the rheological properties of lemon-pectin mixtures were studied and compared to those of pure lemon (high methoxyl: HM) and date (low methoxyl: LM) pectins. Rheological properties were carried out in the presence of 30%, 45% and 60% sucrose, and increasing calcium concentrations (0—0.1%). The presence of date with lemon pectin led to a gel formation at 45% sucrose and in the presence of calcium, which was not the case for lemon pectin alone under the same conditions. It is suggested that lemon and date pectins interacted, leading to gel formations at different gelling temperatures, which were strongly dependant on degree of methylation. These results were confirmed by scanning electron microscopy, which revealed inhomogeneous gels where dense aggregated network and loose, open network areas were present. Addition of calcium to pectin mixture gels led to stronger and faster gel formation. Key Words: lemon pectin, date pectin, pectin mixtures, gels, rheology, microstructure

INTRODUCTION Pectins are polysaccharides, mainly present in the primary cell wall and in the middle lamella of plants. They are composed of an a-1,4-linked galacturonic acid (GalA) backbone (smooth regions) (Guillotin, 2005). This homogalacturonan may be interrupted in places by alternating rhamnose/Gal A sequences, where neutral sugars such as galactan, arabinan or arabinogalactans are substituted to the rhamnose moieties (hairy regions) (De Vries et al., 1986; Guillotin, 2005). The galacturonic acid residues could be partly methyl esterified at C-6 and the hydroxyl groups partly acetyl-esterified at O-2 and/or O-3 (Pilnik and Voragen, 1970; Rombouts and Thibault, 1986). The degree of methylation (DM) divides pectin into two types. In high methoxyl (HM) pectin more than 50% of the carboxyl groups are methylated and in low methoxyl (LM) pectin less than 50% of the carboxyl groups are methylated (Lo¨fgren et al., 2002). *To whom correspondence should be sent (e-mail: manel_masmoudi@yahoo.fr; besbes.s@voila.fr). Received 7 January 2009; revised 20 March 2009. Food Sci Tech Int 2010;16(2):0105—10 ß SAGE Publications 2010 Los Angeles, London, New Delhi and Singapore ISSN: 1082-0132 DOI: 10.1177/1082013209353093

The degree of substitution of methyl ester determines the mechanism of formation of pectin gels, their conformation and rheological properties (Fishman et al., 1984). In HM pectins, gel formation is governed by hydrophobic interactions and hydrogen bonds at pH  3.5 and in the presence of a cosolute, such as sucrose at a concentration higher than 55% by weight (Oakenfull and Scott, 1984). The cosolute reduces the water activity and the low pH reduces the ion dissociation, thereby enabling the formation of junction zones between the pectin chains (Lo¨fgren et al., 2002). On the other hand, LM pectins form gels by the ‘egg box’ mechanism in the presence of Ca2þ ions over a wide range of pH values, with or without sugars (Grant et al., 1973). LM pectin may be used as a gelling agent in low-sugar products such as low calorie jams and jellies, confectionary jelly products and other food applications (Iglesias and Lozano, 2004). Gelling properties of pectins may be affected by many factors. Increased DM results in higher setting temperature and so more rapid gel formation for HM pectins (Lo¨fgren et al., 2005). Rolin and Devries (1990) reported that calcium addition also influences gel formation behavior of HM pectin. Moreover, gelling temperature increases in the presence of Ca2þ. Calcium content influences also the rheological behavior of LM pectin gels by increasing G0 , but at Ca2þ levels that are too high, syneresis may occur. Contrary to HM pectin, the gel temperature increases with decreasing DM. In addition, LM pectins with a blockwise distribution of free carboxyl

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groups are very sensitive to calcium (May, 1990; Thibault and Ralet, 2003). Interactions between mixed biopolymer systems, of which pectin is one component, have been largely studied, such as pectin/alginate (Walkenstro¨m et al., 2003), pectin/starch (Evageliou et al., 2000) and pectin/gelatine (Al-Ruqaie et al., 1997) mixtures. However, few studies exist on the behavior of mixed pectin/pectin gels. Mixtures of pectins are widely used in food applications to obtain products with better properties. Large variations in rheological behavior and microstructure have been reported in mixtures of HM and LM pectin gels by altering the gel formation conditions (Lo¨fgren et al., 2002). These authors found that at pH 3, in the presence of both 60% sucrose and calcium ions, the gel mechanisms of both HM and LM pectins, are favored and the resulting network is inhomogeneous with dense network as well as loose and sparse regions (Lo¨fgren et al., 2002). In a recent work, Lo¨fgren and Hermansson (2007) studied the same interaction at pH 3.5, with the presence of different sucrose concentrations they reported that a strong synergistic effect in the storage modulus occurred in mixed HM/LM pectin gels. In a recent work (Masmoudi et al., 2008) we tried to optimize pectin extraction from lemon by-product with acidified date juice using different extraction conditions. These conditions can affect the quality of the obtained pectin. Then, it is necessary to study its chemical and gelling properties in order to evaluate the validity of the extraction method. Therefore, in this present study, pure lemon and date pectin as well as their mixtures were collected. The objective was to test effect of extraction conditions on gelling properties of the obtained date-lemon pectin mixtures, compared with pure lemon and date pectin having high and low DM, respectively.

of second category (hard texture) collected at the ‘Tamr’ stage (full ripeness). Dates were pitted, washed in running tap water and dried 12 h in a drying oven at 45  C. Then, the collected pulp was milled to obtain date paste.

MATERIALS AND METHODS

Methods

Pectin Samples Lemon pectin was extracted with acidified date juice using different extraction conditions as described in a previous work (Masmoudi et al., 2008). Pectin samples composed of mixtures of lemon and date pectin were precipitated and collected from the obtained extract for analysis of their gelling properties. In addition, date pectin was precipitated and purified directly from date juice, whereas lemon pectin was extracted by acidified water (pH 2.8). Both pure pectins were also subjected to the same analysis. Table 1 represents pectin sample designations and their respective experimental extraction conditions. In addition, some chemical characteristics of pectin samples are presented in the same table. These analyses were carried out in a previous study (Masmoudi, 2009). Degree of methylation (DM) was determined according to Voragen et al. (1986) method. Methoxy groups were separated and quantified by HPLC on an ion exchange column. DM was expressed as the molar ratio of methanol to galacturonic acid. Individual neutral sugars were determined according to Blakney et al. (1983) method. Separation and quantification of alditol acetate derivatives were performed by gas chromatography using a high performance capillary column. Average molecular weight (Mw) of the extracted pectins was determined by high performance size exclusion chromatography (HPSEC) method, coupled online with three detectors: a differential refractometer (RI), a right angle laser light scattering detector and a differential viscometer (Masmoudi, 2009).

Date Juice Preparation Materials The date juice was prepared as described by Masmoudi et al. (2007) and stored at 20  C until use.

Lemon By-product One batch of 25 kg of lemon by-product (Citrus limon L.) was supplied by a fruit beverage industry (Zina, Sfax, Tunisia) using mixed lemon varieties from Nabeul region (Tunisia). After removal of the pips, the remaining matter (pulp and peel) was lyophilized, milled and sieved (60-mesh size screen). The obtained powder was stored at 20  C. Dates Dates (Phoenix dactylifera L.) of ‘Deglet Nour’ variety were provided by the National Institute of arid zone (Degach, Tunisia). We used a batch of 50 kg of dates

Rheology and Microstructure of Pectin Gels Preparation of Gels Dispersions containing 1% of pectin were prepared by dissolving pectin samples in distilled water by magnetic stirring until complete dissolution. Sucrose was added in the desired levels: 30% (for date and mixture of pectins), 45% (for lemon and mixtures of pectins) or 60% (for lemon pectin). The pH was lowered to 3 (for lemon pectin) or 3.5 (for lemon, date and mixtures of pectins) using 0.1 N NaOH, and if necessary, adjusted to these values with 0.1 N HCl (Grosso and Rao, 1998). For date

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Table 1. Pectin samples designation, corresponding experimental extraction conditions and some chemical characteristics. Sample Pd Pdl1 Pdl2 Pdl3 Pdl5 Pdl9 Pl1

Pectin origin and extraction conditions Date pectin (T ¼ 100  C, pH ¼ 5.62, 5 min) Pectin mixture (T ¼ 80  C, pH 2.8, 4 h) Pectin mixture (T ¼ 80  C, pH 2.8, 1 h) Pectin mixture (T ¼ 80  C, pH 3.4, 4 h) Pectin mixture (T ¼ 40  C, pH 2.8, 4 h) Pectins mixture (T ¼ 84.34  C, pH 2.8, 3.57 h) Lemon pectin (T ¼ 80  C, pH 2.8, 4 h)

Degree of methylation (%) 16.16±0.55 31.1±0.00 30.7±0.48 29.3±0.56 34.5±0.16 34.9±1.03 58.91±0.28

Neutral sugar content (%)

a b b b c c d

5.60±1.40 6.70±0.56 8.30±0.42 5.10±0.70 — 9.2±0.42 7.10±0.14

Molecular weight (kDa)

ab abc c a d bc

66.98±7.62 a 132.04±10.42 b 123.53±14.57 b 113.87±12.36 b 214.83±11.41 c 243.41±13.91 c 328.90±8.71 d

Means (mean±SD, n ¼ 2) followed by the same superscript within a column are not significantly different (a ¼ 0.05).

and mixture pectins, dispersions were heated to 80  C and a solution of 2% CaCl2.2H2O was added drop wise under continuous stirring, giving final concentrations of 0.04%, 0.07% and 0.1% CaCl2.2H2O. These calcium concentrations were selected according to a preliminary study (unpublished results). Solutions were heated continuously until the required weight was obtained by water evaporation, to achieve the desired pectin concentration. Temperature was maintained at 80  C before they were poured into the rheometer plate. Rheological Study Dynamic rheological experiments were conducted using a Bohlin CVO 120 rheometer (Bohlin Instruments) equipped with a circulating water bath temperature controller. Rheology of the pectin gels was characterized by small deformation oscillatory measurements of storage modulus (G0 ), loss modulus (G00 ) and phase angle () at 1.0 Hz with cone-and-plate geometry (4 cm diameter, 4 ). After preparation, the hot pectin dispersion was poured on the rheometer plate, set at 80  C and a few drops of thin paraffin oil were placed at the cone’s edge to avoid evaporation of the sample during measurements. Gel formation of the samples was investigated and gel point (Tg) was determined under controlled cooling from 80  C to 20  C. The cooling rate was 1  C/min, and measurements were made each minute. In food gels, the temperature where the total phase angle variation is halved (0) can be regarded as a good criterion to define the gel point (Barfod and Pederson, 1990). Oscillatory measurements were also conducted on the same gels, using the frequency sweep method in the range of 0.01—10 Hz at 20  C to study variations of G0 and G00 as a function of frequency. Formerly, an amplitude sweep to ascertain the linear viscoelastic range was performed on selected samples. Scanning Electron Microscopy Pectin gels were prepared for scanning electron microscopy (SEM) using the technique described by

Attia et al. (1993). The observations were performed with SEM Philips XL 30 (Philips, France). Statistical Analysis Chemical characteristics of pectin samples were determined in duplicate. Values of different parameters were expressed as the mean±standard deviation. Statistical analysis was performed using the Statistical Package for the Social Sciences ‘SPSS’ (version 13). Duncan test was performed to evaluate the significance of differences between mean values at the level of p < 0.05.

RESULTS AND DISCUSSION Rheology and Microstructure of Pure Pectins Lemon Pectin Rheological Study The gel formation behavior of the high methylated lemon pectin (DM  59%), was investigated in the presence of 60% sucrose at pH values of 3 and 3.5, respectively and 45% sucrose, 0.1% CaCl2. 2H2O at pH 3.5. Figure 1 shows the semi-logarithmic plots of G0 and G00 , and the phase angle () versus temperature obtained from small amplitude oscillatory measurements at constant frequency during controlled cooling. In the presence of 60% sucrose, a cross-linked network was formed, which was characterized by the temperature dependence of G0 and G00 on cooling. During cooling, from 80  C to 20  C, the pectin changes from a predominantly liquid-like structure (G0 < G00 ) to a typically gel-like structure (G0 > G00 ). The same Figure 1 also shows the variation of the phase angle during cooling. Temperature at which  ¼ 45 was assumed as the gelling temperature Tg. This latter corresponds to the temperature at which G0 ¼ G00 . Results indicated that Tg increased from 64  C to 72  C, with decreasing the pH from 3.5 to 3, respectively.

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Figure 2. Frequency sweeps of lemon pectin (Pl1) gels prepared with: (a) 60% (w/w) sucrose, 0% CaCl2. 2H2O, pH 3.5 (¨), (b) 60% (w/w) sucrose, 0% CaCl2. 2H2O, pH 3 (#). G0 : (filled symbols), G00 (empty symbols).

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Figure 1. Semi-logarithmic plots of G0 (N), G00 (¨) and the phase angle (#) vs temperature obtained at constant frequency (1 Hz) during controlled cooling (1  C/min) of lemon pectin (Pl1) gels prepared with: (a) 60% (w/w) sucrose and 0% CaCl2. 2H2O, pH 3.5, (b) 60% (w/w) sucrose and 0% CaCl2. 2H2O, pH 3, (c) 45% (w/w) sucrose and 0.1% CaCl2. 2H2O, pH 3.5.

Properties of lemon pectin gels prepared at the same conditions were also studied by frequency sweep. The log—log representation of the elastic (G0 ) and the dynamic modulus (G00 ) as a function of frequency shows a gel-like behavior with G0 > G00 , and also a slight frequency dependence of moduli G0 and G0 especially at pH 3.5 (Figure 2). In addition, the storage modulus G0 reached higher values at pH 3 indicating the formation of stronger gel, which confirms the previous results. Lo¨fgren et al. (2005) reported that at low pH values the majority of the carboxylic groups are not dissociated, which is more favorable for HM pectin gel formation with hydrogen bonds and hydrophobic interactions. When sucrose concentration was reduced to 45%, no gel formation occurred, even if 0.1% CaCl2. 2H2O was added (Figure 1c). In fact, HM pectins require at least 55% of sugar to gel. The water activity was not reduced enough to promote hydrophobic interactions between

pectin methyl esters. Similar results were found by Lo¨fgren and Hermansson (2007) for two HM pectins studied at the same experimental conditions. Microscopy It is interesting to speculate about a relation between the rheological effects caused by the nature of the pectin samples and microstructure of the obtained gels (Walkenstro¨m et al., 2003). Microstructures of lemon pectin gels are shown in Figure 3. At 60% sucrose, the gel formed at pH 3.5 was based on an open network of pectin in which water was immobilized in large pores (Figure 3a). These pores are formed by a thin web structure denoting structural weakness. In fact, Stading et al. (1993) reported that large pores are the weakest part of a gel network. When the pH was decreased to 3, the network structure was denser and was characterized by more interconnected chains and smaller pores (Figure 3b), indicating stronger gel, which confirms the rheological results. Presence of less amount of sugar (45% sucrose) resulted in a loss of the network-like structure, indicating absence of gel (Figure 3c). Date Pectin Rheological Study Date pectin gels (DM  16%) prepared at pH 3.5 in the absence or presence of increased calcium concentrations were analyzed by controlled cooling. Figure 4a shows that calcium free pectin samples had liquid-like behavior with G00 > G0 and no occurrence of crossover, whereas gel-like structures were obtained (G0 > G00 ) when calcium was added (Figures 4b and 4c). Increasing calcium concentration from 0.07% to 0.1% increased the gelling temperature from 54  C to 64  C, respectively. Similar results were found by Lootens et al. (2003) for

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Figure 4. Semi-logarithmic plots of G0 (N), G00 (¨) and the phase angle (#) vs temperature obtained at constant frequency (1 Hz) during controlled cooling (1  C/min) of date pectin (Pd) gel prepared with: (a) 30% (w/w) sucrose and 0% CaCl2. 2H2O, pH 3.5, (b) 30% (w/w) sucrose and 0.07% CaCl2. 2H2O, pH 3.5, (c) 30% (w/w) sucrose and 0.1% CaCl2. 2H2O, pH 3.5.

Figure 3. Microstructure of lemon pectin gels (Pl1) prepared with 60% (w/w) sucrose without Ca 2þ: (a) at pH 3.5, (b) at pH 3 and (c) with 45% (w/w) sucrose and 0.1% CaCl2. 2H2O, pH 3.5.

LM pectin gels. Calcium is responsible for the formation of cross-links between pairs of carboxyl groups belonging to two different chains in close contact. The junction zone of the gels shows an egg-box shape in which calcium ions are trapped inside (Matia-Merino et al., 2004; Thakur et al., 1997). Higher calcium concentrations will generate stronger interactions and firmer gels (Whistler and BeMiller, 1997). However, even at the highest calcium concentration, the obtained gel could be considered as a weak gel as illustrated by the frequency dependence of the moduli G0 and G00 . This result was confirmed by Power Law model as follows:

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G0 ¼ 34:04 W 0:2819 ðR2 ¼ 0:9683Þ

ð1Þ

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Figure 5. Frequency sweeps of date pectin (Pd) gel prepared with 30% (w/w) sucrose, 0.1% CaCl2. 2H2O, pH 3.5. G0 (¨), G00 (#).

G00 ¼ 24:02 W 0:3579 ðR2 ¼ 0:9667Þ

ð2Þ

where W is the frequency. Gel weakness was also illustrated by a small difference in magnitude between G0 and G00 (Figure 5). In fact, this behavior is often observed for weak cross-linked network structures (Lo¨fgren and Hermansson, 2007). Microscopy In the absence of calcium, no gel network connections were present, and only low aggregations could be seen (Figure 6a). Addition of calcium apparently resulted in stronger aggregation of pectin showing a network structure (Figures 6b and 6c). We can also see that the calcium concentration influences the network density. The gel containing 0.1% CaCl2.2H2O was composed of denser and more compact network. This result is in accordance with higher value of G0 , in comparison with the gel prepared at 0.07% CaCl2.2H2O composed of more open network. Rheology and Microstructure of Pectin Mixtures The gel formation behavior was investigated for five samples of lemon-date pectin mixtures (Pdl1 to Pdl9) by study of their rheological properties and microstructure. These pectin samples were obtained after lemon extraction in date juice at different experimental conditions (Masmoudi et al., 2008; Table 1). However, Pdl1 and Pdl9 are more discussed for their rheological and microstructural properties. In fact, these samples presented the highest extraction yields. Particularly, Pdl9 was obtained using the optimal extraction conditions fixed in a previous study (Masmoudi et al., 2008). Interaction between Lemon and Date Pectin Gel preparations were carried out at pH 3.5 in the presence of 30% and 45% sucrose, and at different

Figure 6. Microstructure of date pectin gels (Pd) prepared with 30% (w/w) sucrose at pH 3.5: (a) without Ca 2þ, (b) with 0.07% CaCl2. 2H2O, (c) with 0.1% CaCl2. 2H2O.

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Pectin Extraction from Lemon By-product with Acidified Date Juice

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Figure 7. Semi-logarithmic plots of G0 (N), G00 (¨) and the phase angle (#) vs temperature obtained at constant frequency (1 Hz) during controlled cooling (1  C/min) of mixture of lemon-date pectin (Pdl1) gel prepared with: (a) 30% (w/w) sucrose and 0.07% CaCl2. 2H2O, pH 3.5, (b) 45% (w/w) sucrose and 0.04% CaCl2. 2H2O, pH 3.5, (c) 45% (w/w) sucrose and 0.07% CaCl2. 2H2O, pH 3.5, (d) 45% (w/w) sucrose and 0.1% CaCl2. 2H2O, pH 3.5.

calcium concentrations: 0%, 0.04%, 0.07% and 0.1% CaCl2.2H2O. Figure 7 shows the rheological properties of Pdl1 pectin gels. Despite their low DM (31.1%), no gel formation (G0 < G00 ) occurred in the presence of 30%, 0.1% Calcium (Figure 7a), whereas a gel was formed (G0 > G00 ) when sucrose concentration was increased to 45% and only with the presence of calcium (Figures 7b and 7d). The same tendency was also observed for the other lemon-date pectin mixtures. Consequently, both presence of calcium and at least 45% sucrose were necessary for gel formation. This could be attributed to the nature of the pectin samples. In fact, in this study, pectin samples are mixtures of date and lemon pectins having low (LM) and high (HM) degrees of methylation, respectively (Table 1). Mixtures could be observed also for commercial pectins. Guillotin (2005) showed that commercial pectins in general are mixtures of several populations having various DM. In the presence of 30% sucrose and 0.1% CaCl2.2H2O, conditions that are favorable for LM pectin gel formation, the presence of lemon pectin (HM) seems to affect negatively the gel formation of date pectin (LM), which formed gel under the same conditions (see Figure 4c). In the absence of sufficient amount of sugar (30%), gel formation of only date pectin was probably favored, leading to no gelling ability of the pectin mixture. This result was different to that found by Lo¨fgren et al. (2002) who reported that at pH 3, addition of HM to LM pectin in the presence of 30% sugar and 0.15% calcium had a minor influence on the gel

formation. This could be attributed to difference in pectin characteristics such as molecular weight, neutral sugar content, DM and especially methyl ester distribution (BeMiller, 1986; May, 1990). In fact, Lo¨fgren et al., (2005) reported that blockwise distribution of methyl esters can modify the gelling properties of HM pectin in a calcium environment. Effect of date pectin (LM) on lemon pectin (HM) was, however, different: at 45% sucrose, 0.1% CaCl2.2H2O, gelation was promoted for pectin mixture, but not for only lemon pectin. This behavior shows that presence of date pectin fraction is responsible for the gel formation of the pectin mixture. From the results cited above, two hypotheses could explain gel formation at 45% sucrose and in the presence of calcium. The first one is that date pectin probably contributed to the gel formation of lemon pectin as stated before. The second one is that the negative effect of HM on LM gelation was deleted by increasing sugar concentration to 45%. An explanation could be that the lemon-date pectin mixture is probably composed of different populations of high and low methyl esterified galacturonic acid blocks distributed in the pectin molecule and corresponding respectively to lemon and date pectin fractions. At 30% sucrose, 0.1% CaCl2.2H2O, the high methylated blocks could not form a true network since the low sugar content promotes solvent-pectin interactions rather than pectin-pectin interactions. These pectin regions may function as a hindrance and thus prevent

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Figure 8. Microstructure of lemon-date pectin gels (Pdl9) prepared with 45% (w/w) sucrose at pH 3.5: (a and c) 0.07 % CaCl2. 2H2O, (b) 0.04% CaCl2. 2H2O, (d) 0.1% CaCl2. 2H2O.

aggregation of pectin mixture chains, which explain absence of gel under these conditions. When sugar concentration increased to 45%, more hydrophobic interactions were promoted, leading probably to some connections between high methylated chains and contribute to the gel formation of the pectin mixture. This was also illustrated by microstructure of pectin mixture which revealed inhomogeneous structure. At low magnification (Figure 8a), the network structure of Pdl9 gel was composed of dense network regions, as well as thin, open network regions with large pores. Details of these two areas are shown at a higher length scale (Figure 8c). This result indicate that the dense network area correspond probably to LM pectin gel (date pectin), and that the weak pectin network with large pores to HM pectin (lemon pectin). Similar structures were found by Lo¨fgren et al. (2002) for mixed HM and LM pectin gel. Influence of Calcium Addition on Pectin Mixture Gels Study of the gel properties as affected by calcium concentration on pectin mixture at 45% sucrose is shown in Figures 7 and 9. As cited previously, in the absence of

calcium, no gel formation occurred for Pdl1 sample for example, whereas calcium addition enabled the dispersion to gel. Increasing calcium concentration from 0.04% to 0.1% CaCl2. 2H2O resulted in an increase in the gelling temperature from 58  C to 75  C, respectively (Figures 7b—7d). In addition, the gel properties vary considerably with the amount of added Ca2þ as illustrated in Figure 9. The log—log representation of the elastic (G0 ) and dynamic (G00 ) moduli as a function of frequency, shows the mechanical spectra of Pdl9 pectin gels prepared with different calcium concentrations. The spectrum obtained at the lowest concentration (0.04% CaCl2. 2H2O) is typical of a gelling system where the degree of cross-linking is just sufficient to give a continuous network: Pectin showed high frequency dependence and low difference between moduli values, indicating a weak gel. The response changed gradually into a permanent gel network and the gel-like behavior becomes more pronounced at 0.1% CaCl2. 2H2O, with greater separation of G0 and G00 and less variation with frequency, which indicates a strong gel (Lopes Da Silva and Rao, 1999). These results agree well with the microstructure of pectin mixture gels formed with different calcium concentrations shown in Figure 8 for Pdl9 sample. At the lowest

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Frequency (Hz)

Figure 9. Frequency sweeps of lemon-date pectin (Pdl9) gel prepared with: (a) 45% (w/w) sucrose, 0.04% CaCl2. 2H2O, pH 3.5 (¨), (b) 45% (w/w) sucrose, 0.07% CaCl2. 2H2O, pH 3.5 (#), (c) 45% (w/ w) sucrose, 0.1% CaCl2. 2H2O, pH 3.5 (N). G0 (filled symbols), G00 (empty symbols). Ca2þ concentration, a weak gel was formed with thin network regions and large pores (Figure 8b). As Ca2þ concentration increased, gels were characterized by denser network with more interconnected chains and smaller pores, suggesting better retention of the aqueous phase and consequently stronger gel (Figures 8c—8d). The cited results could be related essentially to the LM pectin fraction represented by the date pectin. Calcium probably contributes to an increase in the density of calcium-mediated junctions. Furthermore, the higher the calcium content available in the solution, the greater the formation of a three dimensional network resulting in stronger gels. This result agrees with previous works reported for low methoxyl pectins (Diaz-Rojas et al., 2004; Grosso and Rao, 1998). Comparative Study between Gelling Behavior of the Different Date-lemon Pectin Mixtures We turn now to the study of the gelling properties of the pectin mixture samples obtained in a previous work at different extraction conditions: Pdl1—Pdl9 (Masmoudi et al., 2008). These samples have different DM (Table 1). Pectin mixture gels prepared at equal calcium concentration (0.07% CaCl2. 2H2O) showed gelling temperatures (Tg) ranging from 52  C to 67  C, respectively for Pdl9 and Pdl3. Higher values were obtained for samples having lower DM. If we consider these mixtures as low methylated pectins, (DM < 50%), the increase of Tg could be attributed to their DM. In fact, the lower the DM of LM pectins, the greater it is the reactivity and sensibility to calcium (Iglesias and Lozano, 2004). The presence of higher number of methyl esters in Pdl9 (DM ¼ 34.9%, Tg  52  C) and Pdl5 (DM ¼ 34.5%, Tg  55  C), for example, probably give rise to more hydrophobic interactions and can

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constitute a sterical hindrance preventing the complete Ca2þ mediated aggregation (Lo¨fgren et al., 2006). However, in the case of the other samples having lower DM, gel formation happens quickly after calcium chloride addition. In a previous work, Pdl9 sample was extracted at the optimal extraction conditions, giving the highest yield (Masmoudi et al., 2008). Study of its rheological behavior was necessary to determine its gelling properties for further use in jelly products. As cited above, Pdl9 had the lowest Tg and so it needs more time to gel. This property is interesting to avoid pre-gel formation. In fact, very rapid kinetic behavior can result in undesirable air bubbles entrapped in the gel product (Lo¨fgren et al., 2006). This pectin can be used for the preparation of some kinds of low sugar jellies having a clear aspect or for confectionary products. However, pectin mixture (Pdl3) having the highest Tg (67  C) could be used for the preparation of marmalades or reduced calorie jams with whole fruits, in order to ensure a uniform distribution of fruit particles in the continuous jelly phase (Thibault and Ralet, 2003).

CONCLUSIONS This study focused on the microstructure and rheology of extracted lemon and date pectins as well as their mixtures. For the pure lemon pectin (HM), gelification occurred only at high sucrose concentration (60%). Gelling temperature Tg increased by lowering pH value to 3. Date pectin (LM) showed higher Tg with increasing calcium concentration. These results were confirmed by microstructure observations, which showed more interconnected and denser network at favorable conditions. However, both microstructure and rheological behavior were different for date-lemon pectin mixtures. At pH 3.5 and in the presence of 30% sucrose and 0.1% calcium, conditions that are adequate only for date pectin, no gelification occurred. However, increasing sucrose content to 45%, allowed pectin mixtures to gel, and showed better gelling properties than for pure lemon pectin at higher sucrose content (60%). This behavior was ameliorated by calcium addition. Inhomogeneous gel structures were observed for pectin mixtures, indicating the presence of date pectin rich aggregation areas and weak lemon network regions. Comparative study of the different pectin mixtures, showed different gelling temperature which increased with decreasing DM. These results demonstrate the possibility of using these pectin mixtures for the preparation of many jelly products with reduced sugar content. In addition, their different gelling temperature is an important factor in the manufacture of many food products with different characteristics such as whole fruit jams or clear jellies.

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ACKNOWLEDGMENTS We thank Mr Fakhfakh Zouheir responsible of U.S.C.R. Microscopy (FSS, Sfax, Tunisia) and Mr Claude Deroanne responsible of the laboratory of Food science and Technology (FUSAGx, Gembloux, Belgium) for their scientific support.

REFERENCES Al-Ruqaie I., Kasapis S. and Abeysekera R. (1997). Structural properties of pectin-gelatin gels. Part II: Effect of sucrose/glucose syrup. Carbohydrate Polymers 34: 309—321. Attia H., Bennasar M., Lagaude A., Hugodo B., Rouviere J. and Tarodo De La Fuente B. (1993). Ultrafiltration with microfiltration membrane of acid skimmed and fat-enriched milk coagula: hydrodynamic, microscope and rheological approaches. Journal of Dairy Research 60: 161—174. Barfod N.M. and Pederson K.S. (1990). Determining setting temperature of high-methoxy pectin gels. Food Technology 44: 139—148. BeMiller J.N. (1986). An introduction to pectins: Structure and properties. In: Fishman M.L. and Jen J.J. (eds), Chemistry and Function of Pectins. Washington: American Chemical Society, pp. 2—12. Blakney A.B., Harris P.J., Henry R.J. and Stone B.A. (1983). A simple and rapid preparation of aldithol acetates for monosaccharide analysis. Carbohydrate Research 113: 291—299. De Vries J.A., Voragen A.G.J., Rombouts F.M. and Pilnik W. (1986). Structural studies of apple pectins with pectolytic enzymes. In: Fishman M.L. and Jen J.J. (eds), Chemistry and Function of Pectins. Washington: American Chemical Society, pp. 38—48. Diaz-Rojas E.I., Pacheco-Aguilar R., Lizardi J., Argu¨elles-Monal W., Valdez M.A., Rinaudo M. and Goycoolea F.M. (2004). Linseed pectin: Gelling properties and performance as an encapsulation matrix for shark liver oil. Food Hydrocolloids 18: 293—304. Evageliou V., Richardson R.K. and Morris E.R. (2000). Co-gelation of high methoxy pectin with oxidized starch for potato maltodextrin. Carbohydrate Polymers 42: 233—243. Fishman M.L., Pfeffer P.E., Baford R.A. and Doner L.W. (1984). Studies of pectin solution properties by high performance size exclusion chromatography. Journal of Agricultural Food Chemistry 32: 372—378. Grant G.T., Morris E.R., Rees D.A., Smith P.J.C. and Thom D. (1973). Biological interactions between polysaccharides and divalent cations: The egg-box model. FEBS Letters 32: 195—198. Grosso C.R.F. and Rao M.A. (1998). Dynamic rheology of structure development in low-methoxyl pectin þ Ca2þ þ sugar gels. Food Hydrocolloids 12: 357—363. Guillotin S.E. (2005). Studies on the intra- and intermolecular distributions of substituents in commercial pectins. PhD Thesis, Wageningen University,The Netherlands. Iglesias M.T. and Lozano J.E. (2004). Extraction and characterisation of sunflower pectin. Journal of Food Engineering 62: 215—223. Lo¨fgren C., Guillotin S., Evenbratt H., Schols H. and Hermansson A.M. (2005). Effects of calcium, pH, and blockiness on kinetic rheological behavior and microstructure of HM pectin gels. Biomacromolecules 6: 646—652. Lo¨fgren C., Guillotin S. and Hermansson A.M. (2006). Microstructure and kinetic rheological behaviour of amidated and nonamidated LM pectin gels. Biomacromolecules 7: 114—121.

Lo¨fgren C. and Hermansson A.M. (2007). Synergistic rheological behaviour of mixed HM/LM pectin gels. Food hydrocolloids 21: 480—486. Lo¨fgren C., Walkenstrom P. and Hermansson A.M. (2002). Microstructure and rheological behavior of pure and mixed pectin gels. Biomacromolecules 3: 1144—1153. Lootens D., Capel F., Durand D., Nicolai T., Boulenguer P. and Langendorff V. (2003). Influence of pH, Ca concentration, temperature and amidation on the gelation of low methoxyl pectin. Food hydrocolloids 17: 237—244. Lopes da Silva J.A. and Rao M.A. (1999). Reological Behaviour of Food Gel Systems. Rheology of Fluid and Semi-solid Foods. Maryland: Aspen Publishers Inc., pp. 319—368. Masmoudi M. (2009). Contribution a` la valorisation des fractions glucidiques et pectiques de co-produits de dattes (Phoenix dactylifera L.) et de citron (Citrus limon L.). PhD Thesis, E´cole Nationale D’inge´nieurs De Sfax, Tunisie. Masmoudi M., Besbes S., Blecker C. and Attia H. (2007). Preparation and characterization of osmodehydrated fruits from lemon and date by-products. Food Science Technology International 13: 405—412. Masmoudi M., Besbes S., Chaabouni M., Robert C., Paquot M., Blecker C. and Attia H. (2008). Optimization of pectin extraction from lemon by-product with acidified date juice using response surface methodology. Carbohydrate Polymers 74: 185—192. Matia-Merino L., Lau K. and Dickinson E. (2004). Effect of low-methoxyl, amidated pectin and ionic calcium on rheology and microstructure of acid-induced sodium caseinate gels. Food Hydrocolloids 18: 271—281. May C. (1990). Industrial pectins: sources, production and applications. Carbohydrate Polymers 12: 79—99. Oakenfull D.G. and Scott A. (1984). Hydrophobic interaction in the gelation of high methoxyl pectins. Journal of Food Science 49: 1093—1098. Pilnik W. and Voragen A.G.J. (1970). Pectin substances and other uronides. In: Hulme A.C. (ed.), The Biochemistry of Fruits and their Products. New York: Academic Press, pp. 53—87. Rolin C. and Devries J. (1990). Pectin. In: Harris P. (ed.), Food Gels. New York: Elsevier, pp. 401—434. Rombouts F.M. and Thibault J.-F. (1986). Sugar beet pectins: chemical structure and gelation through oxidative coupling. In: Fishman M.L. and Jen J.J. (eds), Chemistry and Function of Pectins. Washington: American Chemical Society, pp. 49—60. Stading M., Langton M. and Hermansson A.M. (1993). Microstructure and rheological behaviour of particulate b-lactoglobulin gels. Food Hydrocolloids 7: 195—212. Thakur B.R., Singh R.K. and Handa A.K. (1997). Chemistry and uses of pectin - a review. Critical Reviews in Food Science and Nutrition 37: 47—73. Thibault J.F. and Ralet M.C. (2003). Physico-chemical properties of pectins in the cell walls and after extraction. In: Voragen F., Schols H. and Visser R. (eds), Advances in Pectin and Pectinase Research. Heidelberg: Springer, pp. 91—105. Voragen A.G.J., Schols H.A. and Pilnik W. (1986). Determination of the degree of methylation and acethylation of pectins by HPLC. Food Hydrocolloids 1: 65—70. Walkenstro¨m P., Kidman S., Hermansson A.M., Rasmussen P.B. and Hoegh L. (2003). Microstructure and rheological behaviour of alginate/pectin mixed gels. Food hydrocolloids 17: 593—603. Whistler R. L. and BeMiller J.N. (1997). Pectins. In: St. Paul M.N. (ed.), Carbohydrate Chemistry for Food Scientists,American Association of Cereal Chemists, St. Paul, MN: Eagan Press, pp. 203—210.

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Effects of Cell Lysis Treatments on the Yield of Coenzyme Q10 Following Agrobacterium tumefaciens Fermentation Yuting Tian,1 Tianli Yue,1,* Jinjin Pei,1 Yahong Yuan,1 Juhai Li1 and Y. Martin Lo1,2 1

College of Food Science and Engineering, Northwest A and F University, Yangling Shaanxi, P. R. China 712100 2 Department of Nutrition and Food Science, University of Maryland, College Park Maryland, USA 20742

The yield of CoQ10, an intracellular product extracted from Agrobacterium tumefaciens cells is dependent on the effectiveness of cell lysis post fermentation. Various cell lysis approaches are investigated, including ultrasound, repetitive freezing/thawing, grinding and acid—heat treatment. The acid—heat combination using hydrochloric acid is found the most effective in releasing CoQ10, followed by lactic, sulfuric, phosphoric and oxalic acids. The most significant processing parameters, namely the ratio of acid solution volume and bacteria weight (A/B ratio), incubation temperature and reaction time, are optimized by using the central composite design with a quadratic regression model built by response surface methodology. The highest CoQ10 yield at 1.518 mg/g dry cell is attained using hydrochloric acid (3 mol/L) under optimal A/B ratio, temperature and time at 10.8 mL/g, 84.2  C and 35.3 min, respectively. Key Words: A. tumefaciens, cell lysis, coenzyme Q10, extraction

INTRODUCTION Coenzyme Q10 (CoQ10, ubiquinone) is an important biochemical compound receiving increased attention as a nutraceutical dietary supplement for its known benefits in the prevention of aging and cardiovascular problems (Ernster and Dallner, 1995; Stoyanovsky et al., 1995; Pepe et al., 2007). Extensive attempts have been made to produce CoQ10 to meet the growing demands. The production of CoQ10 follows one of the three routes: extraction from biological tissues (Laplante et al., 2009), chemical synthesis (Keinan and Eren, 1988) and microbial fermentation (Yoshida et al., 1998). In the wake of environmental awareness, the first two options became least desirable due to the inherent uses of solvents and chemicals in the process. Microbial fermentation, on the contrary, offers an environmentally benign option based on the enzymatic catalysis at the cellular level for CoQ10 assembly. *To whom correspondence should be sent (e-mail: yuetl@nwsuaf.edu.cn). Received 3 June 2009; revised 7 July 2009. Food Sci Tech Int 2010;16(2):0195–9 ß SAGE Publications 2010 Los Angeles, London, New Delhi and Singapore ISSN: 1082-0132 DOI: 10.1177/1082013210366788

Moreover, this approach is attractive to the industry because the process is easy to control at a relatively low production cost (Choi et al., 2005b; Cluis et al., 2007). Similar to other fermentation processes, downstream separation and purification play a role equally important as the fermentation operation per se, if not more. In the case of CoQ10 production, Agrobacterium tumefaciens has been shown to yield the highest quantity of CoQ10 among all strains investigated to date (Yoshida et al., 1998; Choi et al., 2005a; Gu et al., 2006; Ha et al., 2007a,b,2008). Several researchers have also attempted to optimize the fermentation conditions for A. tumefaciens with the scope of increasing CoQ10 productivity (Gu et al., 2006; Ha et al., 2007a,b). However, located in the plasma membrane of prokaryotes and the inner mitochondrial membrane of eukaryotes (Trumpower, 1981; Brandt and Trumpower, 1994), CoQ10 cannot be recovered from fermentation broth without lysing the cells. To enhance industrial production of CoQ10, it is highly desirable if a simple and effective approach capable of effectively releasing CoQ10 from the cell could be developed. Existing approaches for cell disintegration include chemical, enzymatic and mechanical methods (Chisti and Moo-Young, 1986; Golecki, 1988; Middelberg, 1995). Chemical methods rely on selective interaction of a chemical with components of the membrane, 195

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which allows intracellular material to seep through the cell wall (Falconer et al., 1999). On the other hand, enzyme digestion allows for the enzymes to attack and degrade specific components of the cell wall to release the product (Storebakken et al., 2004). While gentle and specific disruption of cells could be achieved chemically or enzymatically, the applicability of these methods for industrial production of CoQ10 remains questionable due mainly to the high processing costs and limited yields. A spectrum of mechanical approaches has also been reported in the literature for recovering cellular metabolites after fermentation, including sonication (Kapucu et al., 2000; Borthwick et al., 2005), freezing and thawing (Ormeci and Vesilind, 2001), bead milling (Rito-Palomares and Lyddiatt, 2002; Mayerhoff et al., 2008) and high-pressure homogenization (Zhu et al., 2007; Shynkaryk et al., 2009). Sonication has the advantage that the solvent (usually water) used for extraction becomes part of the disruption process themselves (Jaki et al., 2006). Freezing and thawing has long been accepted as a way to disrupt cells. Cell rupture is usually prompted by damage to the plasma membrane as a result of intracellular ice crystal growth during the freezing process (Walsh, 2002). Grinding is a simple and classical approach, where frozen-lyophilized cells are broken by grinding cell paste or by using an agate mortar and pestle (Jaki et al., 2006). All of the mechanical approaches appeared to be attractive alternatives to chemical treatment, yet their effectiveness in recovering CoQ10 remained unreported in the literature. The purpose of this study was to compare and evaluate effectiveness of sonication, freezing and thawing, grinding and acid—heat treatment for disrupting the cells of A. tumefaciens and to determine the effects of the four methods on the yield of CoQ10 after A. Tumefaciens cells were harvested from fermentation.

MATERIALS AND METHODS Microorganism Cultivation and Preparation of Cell Biomass Agrobacterium tumefaciens 1.2554 was purchased from China General Microbiological Culture Collection Center (CGMCC, Beijing, China) and was kept at 4  C in Mannitol Agar slants. It was inoculated into a 500 mL Erlenmeyer flask containing 100 mL of seed medium composed of 1% glucose, 0.5% peptone, 0.5% yeast extract and 0.5% NaCl (pH 7.2), and incubated for 24 h on a rotary shaker at 200 rpm and at 28  C. This seed culture was then transferred into a stirred tank fermentor (BioFlo 110, New Brunswick, NJ, USA) with a working volume of 2 L production medium composed of 5% sucrose, 4% CSP (corn steep powder), 1%

(NH4)2SO4, 0.05% K2HPO4, 0.05% KH2PO4, 0.025% MgSO47H2O and 2% CaCO3. The temperature, agitation speed and air flow rate during the culture were 28  C, 450 rpm and 0.6 L/min, respectively. The pH was controlled at 7.2±0.1 by addition of 3 M NaOH or 2 M HCl. After 56 h, the cells were harvested by centrifugation under 4 C at 10 000 rpm (21 000  g) for 10 min (PM180R, ALC International, Milan, Italy), washed with 0.1 M potassium phosphate buffer (pH 7.2), centrifuged, then dried under vacuum freeze-drying (MCFD5505, SIM, Newark, DE, USA) and stored at 18  C before further uses. Methods Cell Lysis Ultrasonic treatment was performed using an ultrasonic cell pulverizer (JY92-II DN, Ningbo, Zhejiang, China) equipped with a micro tip. Dry cells (0.5 g) were intermittently sonicated at an output power of 500 W, working/intermittence time of 12 s/10 s and the volume of water used was 40 mL/g. The total sonication time was 12 min. For freezing and thawing, 0.5 g of the dry cells were immersed into 20 mL of the sterile DI water for 2 h, kept in a freezer (28  C) for 8 h. Frozen samples were then placed in a 80  C water bath for thawing for 15 min. All samples were subjected to three freeze—thaw cycles for cell lysis. Grinding was performed in a cold room (4  C) using mortar and pestle with glass beads (0.1 mm diameter, Cole-Parmer, Vernon Hills, IL, USA) at the ratio of cells-to-beads at 1:5 (w/w) for 20 min. For acid—heat treatment, the effects of five factors, namely type of acid (oxalic acid, lactic acid, hydrochloric acid, sulfuric acid and phosphoric acid), concentration of acid (1, 2, 3, 4, 5 and 6 mol/L), treatment time (10, 20, 30, 40, 50 and 60 min), temperature (25, 40, 55, 70, 85, 100  C), and the ratio between acid solution and bacteria cells (A/B ratio; 5, 10, 15, 20, 25 and 30 mL/g) were investigated. For comparison purpose, cell lysis using a commercially available cell lysis reagent CelLytic B (SigmaAldrich, St. Louis, MO) was conducted using a modified protocol based on Zahiri et al. (2006) and Ha et al. (2007a,b,2008). Ten mL CelLytic B solution was added to 0.1 g dry cells and mixed vigorously at room temperature (25  C) for 30 min. Microscopic examination of samples obtained from cell lysis treatment was conducted using scanning electron microscopy (JSM-6360LV, Tokyo, Japan). Dried samples were mounted on a bronze stub (Coax Group Corporation Ltd., Bangkok, Thailand) and sputtercoated with platinum palladium (Sputter coater E-102 Hitachi, Tokyo, Japan). The specimens were observed at an acceleration voltage of 30 kV.

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Determination of Protein Release Release of intracellular protein is used as a measure of disruption (Lin et al., 1982; Middelberg, 1995; Ho et al., 2008). The total solution protein was estimated according to the Bradford assay (Bradford, 1976) using bovine serum albumin (BSA) as a standard. Optimization of Acid—Heat Treatment The optimum conditions for acid—heat treatment were determined by response surface methodology (RSM). The experiments were optimized using threelevel, three-factor central composite design (CCD). The range and center point values of the three independent variables based on the results of the above experiments are shown in Table 1. The CCD in the experimental design consisted of 20 treatments including 23 factorial points, and six axial points ( ¼ 1.682) and six replicates of the central point. The A/B ratio (mL/g, X1), temperature ( C, X2) and time (min, X3) were chosen as the independent variables, and the extraction yield of CoQ10 (mg/g, Y) was chosen as the response. Experimental runs were randomized to minimize the effects of unexpected variability in the observed responses. Extraction and Measurement of Coenzyme Q10 Petroleum ether was added to the cells obtained from cell lysis treatment and was mixed vigorously. The solution of solvent phase and that obtained by second extraction from the aqueous phase were combined and evaporated to dry using a speed vacuum concentrator (BUCHI 409, Buchi Corp., New Castle, DE, USA). The dry residue was dissolved in ethanol and applied to a high-performance liquid chromatography (HPLC) system (LC-2010A, Shimadzu, Tokyo, Japan) with a Hypersil ODS C18 (5 mm, 4.6 mm  250 mm, Germany) coupled with a UV detector (Waters 486). The column was eluted with ethanol and methanol (9:1, V/V) at a flow rate of 1.0 mL/min and a chromatogram was obtained by monitoring the absorbance at 275 nm identified and quantified by known concentrations of

Table 1. The levels of variables employed in the present study for the construction of central composition design (CCD).

authentic Coenzyme Shanghai, China).

Q10

standard

(Sigma

Co.,

Statistical Analysis All analyses were performed in triplicate. The experimental results obtained were expressed as means ± SD. Statistical analysis was performed using the software Õ Design-Expert 7.0.0 (Stat-Ease Inc., Minneapolis, MN, USA). Data were analyzed by analysis of variance (p < 0.05) and the means separated by Duncan’s multiple range test.

RESULTS AND DISCUSSION Selection of Cell Lysis Methods Lysis of A. tumefaciens cells was achieved by treating the cells with sonication, freezing and thawing, grinding and acid—heat methods and the respective yields of CoQ10 are shown in Table 2. It is evident that cell lysis by acid—heat treatment proved to be superior to other methods in terms of CoQ10 yield, which was significantly higher than that achieved by the other methods. While sonication is commonly employed in research laboratories for cell disintegration, it is also known to generate microscopic bubbles. These transient cavities are thought to create high-shear gradients by microstreaming, with most cavitational effects only observed close to the vibrating surface (Jaki et al., 2006; Gogate and Kabadi, 2009). Therefore, disintegration of cells might be incomplete, as evidenced by SEM observations (Figure 1b). The repeated freeze—thaw treatments were found less effective than sonication, reaching less than 1.0 mg CoQ10 per gram of dry cell weight (Table 2). As seen in Figure 1c, cell lysis was relatively uneven, as the cells on the perimeter of the aggregates tend to be subjected to more severe temperature fluctuations than those trapped in the core of the aggregates. The grinding method, on the other hand, was found the least effective in releasing CoQ10 (Table 2). It is not surprising because such an approach is heavily dependent upon the skills of

Table 2. Amount of CoQ10 attained after A. tumefaciens cells were disrupted by different methods. Cell lysis treatment

CoQ10* (mg/g dried cell)

Sonication Freezing/Thawing Grinding Acid—heat treatment

1.091±0.017b 0.936±0.020c 0.762±0.045d 1.420±0.025a

Levels Variables A/B ratio X1 (mL/g) Temperature X2 ( C) Time X3 (min)

r (1.682)

1

0

1

r (1.682)

5 76.6 27

7 80 30

10 85 35

13 93.4 40

15 98 43

*Mean ± SD (n ¼ 3). Means with the same letter are not significantly different as indicated by Duncan’s multiple range test (p < 0.05).

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Figure 1. Scanning electron micrographs of A. tumefaciens cells: (a) without any treatment; (b) after ultrasound treatment; (c) after repeated freezing and thawing treatment; (d) after grinding treatment; (e) after acid—heat treatment.

the operator grinding the cells as well as the length of treatment (Jaki et al., 2006), needless to say the increased possibility for the cells to escape the grinding force and remain intact, as supported by Figure 1d. Contrary to the aforementioned physical and mechanical disruption, chemical approaches can make cell membranes permeable, which can lead to a selective and rapid release of the product (Ren et al., 2007a). In this study, thermolysis and chemical permeabilization were combined. A. tumefaciens treated with 3 mol/L of HCl, an acid commonly used for cell lysis (MendesPinto et al., 2001; Sarada et al., 2006; Ni et al., 2008), at 10 mL/g cells at 80 C for 30 min was found capable of releasing ca. 1.4 mg CoQ10 per gram of dry cell weight (Table 2) with complete disintegration of cell wall (Figure 1e). The acid—heat treatment was further optimized and reported in the following section. Similar to other Gram-negative bacteria, A. tumefaciens has a thin inner wall and an outer membrane (OM). Certain external agents (HCl in this study) that either release

lipopolysaccharide (LPS) and other components from the OM or intercalate in the membrane can abolish the integrity of the OM (Vaara, 1992; Alakomi et al., 2000). Based on the results attained in this study, acid—heat treatment appeared to be a superior cell lysis method compared with all mechanic approaches. The conditions of acid—heat treatment were thus further optimized. Acid—Heat Treatment To evaluate the effectiveness of acid treatment on cell lysis, five different organic and mineral acids, namely oxalic acid (H2C2O4), lactic acid (C3H6O3), hydrochloric acid (HCl), sulfuric acid (H2SO4) and phosphoric acid (H3PO4) were employed, followed by petroleum ether extraction. While lactic acid, sulfuric acid and hydrochloric acids were found to effectively lyse the cells, as indicated by the high protein contents detected after the acids were added to 10 mL/g A. tumefaciens cells and

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CoQ10* (mg/g dried cell)

Protein* (mg/mL)

0.783±0.030e 1.193±0.019b 1.420±0.013a 1.120±0.020c 0.893±0.019d

2.694±0.144d 5.171±0.187b 5.015±0.139b 5.468±0.150a 3.171±0.078c

Oxalic acid Lactic acid Hydrochloric acid Sulfuric acid Phosphoric acid

1.4 CoQ10 (mg/g)

Acid

6.0 5.0

1.2 4.0

1.0

3.0

0.8 0.6

2.0

0.4 1.0

0.2

0.0

0.0 *Mean ± SD (n ¼ 3). Means within a column with the same letter are not significantly different as indicated by Duncan’s multiple range test (p < 0.05).

6.0

1.6

5.0

1.2 4.0

1.0

3.0

0.8 0.6

2.0

0.4

Protein (mg/mL)

CoQ10 (mg/g)

1.4

1.0

0.2

0.0

0.0 1

2

3

4

5

6

HCl (mol/L)

Figure 2. Effect of the concentration of hydrochloric acid (HCl) on cell lysis (represented by the amount of protein released) and CoQ10 production after A. tumefaciens cells were treated at 80  C for 30 min. Data are shown as mean±SD (n ¼ 3). (m) CoQ10, (*) Protein.

heated to 80  C for 30 min, the yield of CoQ10 was found the highest when hydrochloric acid was used (Table 3), followed by lactic acid, sulfuric acid, phosphoric acid and oxalic acid. It has been demonstrated that acids can penetrate bacteria cell wall and disrupt the normal physiology of certain bacteria. For instance, Alakomi et al. (2000) found that hydrochloric acid caused significant OM damage by disintegrating the LPS layer. Extractability of astaxanthin from Haematococcus pluvialis was found to be significantly enhanced (86—94 %) when the cells were treated by hydrochloric acid at 70  C (Sarada et al., 2006). It was also noted that the concentration and contact time of hydrochloric acid as well as the incubation temperature are crucial to the efficacy of astaxanthin extraction. The effects of HCl concentration on lyzing the cells were further investigated. As seen in Figure 2, by increasing HCl concentration from 1 to 3 mol/L, the amount of CoQ10 released from the cells was significantly increased, reaching the maximum of 1.419 mg/g. Further increase in HCl concentration to 4 mol/L did not show any increase in the CoQ10 content. In fact, while HCl concentrations higher than 3 mol/L

Protein (mg/mL)

Table 3. Amount of CoQ10 and protein released from A. tumefaciens after the cells were treated by different acids.

5

10

15

20

25

30

A/B ratio (mL/g)

Figure 3. Effect of the A/B ratio (mL/g) on cell lysis (represented by the amount of protein released) and CoQ10 production from A. tumefaciens when cells were treated with 3 mol/L HCl at 80  C for 30 min. Data are shown as mean±SD (n ¼ 3). (m) CoQ10, (*) Protein.

was found to lyze cells effectively, again as evidenced by the high protein level, the high acidity might have promoted the degradation of CoQ10. Therefore, 3 mol/L of HCl was selected for subsequent optimization of the treatment. To assess the effect of the volume of acid on cell lysis, six A/B ratios of HCl (3 mol/L) on protein release and CoQ10 extraction from A. tumefaciens were studied, including 5, 10, 15, 20, 25 and 30 mL/g over a 30-min disruption period under 80  C. As seen in Figure 3, the yield of CoQ10 extracted per gram of dry cells was the highest with the 10 mL/g A/B ratio while further increase in the ratio did not promote additional yield of CoQ10. It is noteworthy that the optimal acidity per unit cell weight (0.03 M/g) identified in Figure 3 was identical to that of Figure 2, indicating that precise control of the acidity around the cells — via concentration (Figure 2) or volume (Figure 3) — in the solution was most critical in achieving effective cell lysis without adversely reducing CoQ10 yield. On the other hand, adequate heat treatment is known to enable the release of intracellular components because it could cause certain changes in cell structure (Tsuchido et al., 1985; Ren et al., 2007a,b). A wide range of temperature treatments (25—100  C) for 30 min were employed to assess their effect on CoQ10 yield. A trend of increase was observed as the temperature was increased from 25  C to 85  C (Figure 4). Such an increase could be attributed to the fact that, when cells are heated, some of the LPS molecules in the OM of the cells could be released. The release of LPS could result in disorganization of the OM structure and, as a consequence, result in cell lysis (Tsuchido et al., 1985). Ren et al. (2007b) demonstrated that temperature is the most important factor that affects the disruption efficiency in thermolysis process. It was found that

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1.6 1.4

Table 4. Experimental design and result of factors chosen for the trials central composite design (CCD).

5.0 4.0

1.0 0.8

3.0

0.6

2.0

0.4

Protein (mg/mL)

CoQ10 (mg/g)

1.2

1.0

0.2 0.0

0.0 25

40

55

70

85

100

Temperature (°C)

Figure 4. Effect of the treatment temperature ( C) on cell lysis (represented by the amount of protein released) and CoQ10 production from A. tumefaciens when cells were treated with 3 mol/L HCl at 10 mL/g A/B ratio for 30 min. Data are shown as mean±SD (n ¼ 3). (m) CoQ10, (*) Protein.

x1

x2

x3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1 1 1 1 1 1 1 1 1.682 1.682 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 0 0 1.682 1.682 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 0 0 0 0 1.682 1.682 0 0 0 0 0 0

Y (mg/g) Experimental Predicted 1.303 1.405 1.344 1.319 1.296 1.418 1.345 1.338 1.289 1.392 1.361 1.300 1.375 1.398 1.522 1.517 1.507 1.510 1.514 1.512

1.300 1.408 1.335 1.315 1.297 1.424 1.338 1.337 1.298 1.388 1.355 1.311 1.381 1.397 1.514 1.514 1.514 1.514 1.514 1.514

6.0

1.6 1.4

5.0 4.0

1.0 0.8

3.0

0.6

2.0

Protein (mg/mL)

1.2 CoQ10 (mg/g)

No.

0.4 1.0

0.2 0.0

0.0 10

20

30

40

50

60

Time (min)

Figure 5. Effect of treatment time (min) on cell lysis (represented by the amount of protein released) and CoQ10 production from A. tumefaciens when cells were treated with 3 mol/L HCl under 80  C at 10 mL/g A/B ratio. Data are shown as mean±SD (n ¼ 3). (m) CoQ10, (*) Protein.

hyperthermophilic esterase, the targeted product, was released when the cell suspension of E. coli was heated at 80  C (Ren et al., 2007a). A similar observation was attained in the present study, as significant increases in supernatant protein concentration and CoQ10 yield were both increased when the cells were heated at elevated temperatures. The highest CoQ10 yield was attained at 85  C, whereas further temperature increase to 100  C caused a notable reduction in the yield. Although CoQ10 is known for its thermal stability up to 250  C as suggested by Fir et al. (2009),

the presence of acid might have promoted the degradation of CoQ10 at 100  C. Furthermore, CoQ10 is a light-sensitive compound. When exposed to light, CoQ10 would degrade quite rapidly (Yang and Song, 2006), so the disruption time could be a factor that influences the extraction efficiency of CoQ10. Apparently, the disruption time could be divided into three stages: during the first 30 min, the amount of CoQ10 and protein released increased rapidly; in the range of 30—40 min, the amount of CoQ10 remained almost constant; whereas after 40 min the CoQ10 content decreased significantly (Figure 5). The results indicated a likely decomposition of CoQ10 due to light exposure after extended treatment time. Therefore, the treatment time of 30—40 min was selected for further optimization. It is important to note that, extended disruption time could also result in accumulation of proteins and other impurities in the extract, hindering subsequent isolation and purification of CoQ10.

Modeling of Cell Lysis by Acid—Heat Treatment The application of RSM enables an empirical understanding of the relationship between the response variable (the yield of CoQ10) and the test variables under consideration. The complete design matrix together with the response value obtained from the experimental works is given in Table 4. The yield of CoQ10 was found to range from 1.303 to 1.522 mg/g. The maximum value was found at the A/B ratio of 10 mL/g under 85  C

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Table 5. Analysis of variance (ANOVA) testing the fitness of the regression equation. R ¼ 0.9978, R2 ¼ 0.9956, R2Adj ¼ 0.9917. Source

Table 6. Testing of the significance of the regression coefficients associated with different experimental factors.

Sum of squares DF Mean square F-value Prob(p) > F Factor

Model Residual Lack of fit Pure error Cor total

0.14 6.112E-4 4.694E-4 1.413E-4 0.014

9 10 5 5 19

0.015 6.112E-5 9.389E-5 2.827E-5

252.08

<0.0001

3.32

0.1067

at 35 min treatment time. Trials No. 15—20 in Table 4 were used to determine the experimental error. By applying multiple regression analysis on the experimental data, the response variable and the test variables were found to correlate by the following second-order polynomial equation: Y¼ 1:51 þ 0:027x1  0:013x2 þ0:0047x3  0:032x1 x2 þ0:0048x1 x3 þ0:0018x2 x3  0:061x1 x1  0:064x2 x2  0:044x3 x3 : ð1Þ The quality of the model developed was evaluated based on the correlation coefficient value. The R-value for Equation (1) was 0.9978, which was relatively high (close to unity), indicating that there was a good agreement between the experimental and the predicted value from the model. The R2-value for Equation (1) was 0.9956, which indicates that 99.56% of the total variation in the yield was attributed to the experimental variables studied. The adequacy of the model was further justified through analysis of variance (ANOVA). The ANOVA quadratic model for the yield of CoQ10 is listed in Table 5. From the analysis, the F-value of 252.08 and p-value < 0.0001 indicate the response surface quadratic model was significant as well. Table 6 shows the test of significance for regression coefficient. In this case, x1, x2, x3 and x1 x2 were significant model terms. Based on the aforementioned information (Equation (1)) using RSM, three-dimensional surface plots were constructed to determine the levels of the processing variables to reach the optimal yield of CoQ10 from A. tumefaciens (Figure 6). It was observed that, when the treatment time was set to 35.3 min, the highest CoQ10 yield should locate at the temperature and acid range of 80.7—87.6  C and 8.6—13.0 mL/g, respectively (Figure 6a). By setting the temperature to 84.2  C, the optimal CoQ10 yield should reside in the acid range of 8.9—12.7 mL/g with treatment time between 31.6 and 39.0 min (Figure 6b). Similarly, with the acid content set at 10.8 mL/g, the CoQ10 yield should reach its

Intercept X1 X2 X3 x1 x2 x1 x3 x2 x3 x1 x1 x2 x2 x3 x3

Coefficient Standard Estimate df error 1.51 0.027 0.013 0.0047 0.032 0.0048 0.0018 0.061 0.064 0.044

1 1 1 1 1 1 1 1 1 1

0.0032 0.0021 0.0021 0.0021 0.0028 0.0028 0.0028 0.0020 0.0020 0.0020

95% CI Low

95% CI High

Prob > F

1.51 0.022 0.018 0.000 0.038 0.0014 0.0044 0.065 0.069 0.049

1.52 0.031 0.0084 0.0094 0.026 0.0011 0.0079 0.056 0.059 0.040

— <0.0001 0.0001 0.0491 <0.0001 0.1165 0.5409 <0.0001 <0.0001 <0.0001

optimal value between treatment time of 31.7—39.0 min under 81.2—87.2  C (Figure 6c). The optimal values of the three variables investigated were calculated using the Design-Expert software to be 10.8 mL/g, 84.2  C and 35.3 min for A/B ratio, temperature and time, respectively. The model predicted a maximum response of 1.518 mg/g CoQ10 yield under the optimal condition. In order to verify the predictive capacity of the model, an optimum condition was determined using the simplex method and the maximum desirability for the extraction yield of CoQ10. A mean value of 1.518±0.031 (N ¼ 5) was obtained from laboratory experiments, which was superior to the 1.307 mg CoQ10/g dry cell weight acquired by using CelLytic B, a commercially available cell-lysing agent, suggesting that the acid—heat treatment could serve as an effective cell lysis protocol with a good correlation between predicted and measured values. It is evident that the response model constructed was adequate in predicting the optimal conditions achievable in laboratory settings.

CONCLUSIONS The acid—heat combination was found the most effective in releasing CoQ10 from A. tumefaciens among all treatments investigated. The final CoQ10 extracted by acid—heat treatment reached 1.518 mg/g, significantly higher than that of grinding (0.762 mg/g), freezing/thawing (0.936 mg/g) and ultrasonic treatment (1.091 mg/g). Hydrochloric acid followed by thermolysis yielded the most CoQ10 compared to lactic, sulfuric, phosphoric and oxalic acids. In acid—heat treatment, contacting time, temperature and the A/B ratio were the most critical factors. Hydrochloric acid (3 mol/L) showed the highest degree of cell disruption without residual toxicity when operated at 10.8 mL: 1 g (dry cell) A/B ratio under

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Y. TIAN ET AL.

(b) Content of CoQ10 (mg/g)

Content of CoQ10 (mg/g)

(a) 1.520 1.475 1.430 1.385 1.340 13.0

90.0 87.5 85.0 Te mp 82.5 era tur e( 80.0 °C )

10.0 7.0

ell to-c

l/g)

d-

Aci

1.445 1.408 1.370 13.0

37.5 Tre 35.0 atm ent 32.5 tim 30.0 7.0 e( min )

o (m

rati

1.482

40.0

11.5 8.5

1.520

11.5 10.0 8.5

atio

ell r to-c

/g)

(ml

d-

Aci

Content of CoQ10 (mg/g)

(c) 1.520 1.482 1.445 1.408 1.370 40.0

90.0

Tre

37.5

87.5 35.0

atm

ent

85.0

tim

e(

30.0

min

)

80.0

C)

° re ( atu r e p Tem

82.5

32.5

Figure 6. 3D response surface contour plots showing the experimental factors and their mutual interactions on CoQ10 extraction: (a) Y ¼ f(X1, X2, 35.3); (b) Y ¼ f(X1, 84.2, X3); (c) Y ¼ f(10.8, X2, X3). Y, content of CoQ10 (mg/g); X1, A/B ratio (mL/g); X2, temperature ( C); X3, treatment time (min).

84.2  C for 35.3 min. Such combinations might be applicable in situations where cell lysis is needed to release intracellular products.

ACKNOWLEDGMENTS The research was supported by projects including China State ‘‘11th Five-Year Plan’’ scientific and technological support scheme (2006 BAK02A18, 2006 BAK02A24, 2006 BAK02A05); Beyond Plan of the Ministry of Agriculture (2005-4); New century talents Plan of the Ministry of Education (2005); Special major science and technology in Shaanxi Province (2006 KZ09-G1, 2008 ZDKG-04).

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Cell Lysis in A. tumefaciens Fermentation Falconer R.J., O’Neill B.K. and Middelberg A.P.J. (1999). Chemical treatment of Escherichia coli. 3. Selective extraction of a recombinant protein from cytoplasmic inclusion bodies in intact cells. Biotechnology and Bioengineering 62: 455—460. Fir M.M., Smidovnik A., Milivojevic L., Zmitek J. and Prosek M. (2009). Studies of CoQ10 and cyclodextrin complexes: solubility, thermo- and photo-stability. Journal of Inclusion Phenomena and Macrocyclic Chemistry 64: 225—232. Gogate P.R. and Kabadi A.M. (2009). A review of applications of cavitation in biochemical engineering/biotechnology. Biochemical Engineering Journal 44: 60—72. Golecki J.R. (1988). Electron microscopy of isolated microbial membranes. In: Mayer F. (ed.), Methods in Microbiology, New York: Academic Press, pp. 262—274. Gu S.B., Yao J.M., Yuan Q.P., Xue P.J., Zheng Z.M. and Yu Z.L. (2006). Kinetics of Agrobacterium tumefaciens ubiquinone-10 batch production. Process Biochemistry 41: 1908—1912. Ha S.J., Kim S.Y., Seo J.H., Moon H.J., Lee K.M. and Lee J.K. (2007a). Controlling the sucrose concentration increases coenzyme Q10 production in fed-batch culture of Agrobacterium tumefaciens. Applied Microbiology and Biotechnology 76: 109—116. Ha S.J., Kim S.Y., Seo J.H., Oh D.K. and Lee J.K. (2007b). Optimization of culture conditions and scale-up to pilot and plant scales for coenzyme Q10 production by Agrobacterium tumefaciens. Applied Microbiology and Biotechnology 74: 974—980. Ha S.J., Kim S.Y., Seo J.H., Sim W.I., Moon H.J. and Lee J.K. (2008). Lactate increases coenzyme Q10 production by Agrobacterium tumefaciens. World Journal of Microbiology and Biotechnology 24: 887—890. Ho C.W., Tan W.S., Yap W.B., Ling T.C. and Tey B.T. (2008). Comparative evaluation of different cell disruption methods for the release of recombinant hepatitis B core antigen from Escherichia coli. Biotechnology and Bioprocess Engineering 13: 577—583. Jaki B.U., Franzblau S.G., Cho S.H. and Pauli G.F. (2006). Development of an extraction method for mycobacterial metabolome analysis. Journal of Pharmaceutical and Biomedical Analysis 41: 196—200. Keinan E. and Eren D. (1988). Total synthesis of polyprenoid natural products via Pd(O)-catalyzed oligomerizations. Pure and Applied Chemistry 60: 89—98. Kapucu H., Gulsoy N. and Mehmetoglu U. (2000). Disruption and protein release kinetics by ultrasonication of Acetobacter peroxydans cells. Biochemical Engineering Journal 5: 57—62. Laplante S., Souchet N. and Bry, P. (2009). Comparison of lowtemperature processes for oil and coenzyme Q10 extraction from mackerel and herring. European Journal of Lipid Science and Technology 111: 135—141. Lin H.M., Yang Z.Y. and Chen L.F. (1982). An improved method for disruption of microbial cells with pressurized carbon dioxide. Biotechnology Progress 8: 165—166. Mayerhoff Z.D.V.L., Franco T.T. and Roberto I.C. (2008). A study of cell disruption of Candida mogii by glass bead mill for the recovery of xylose reductase. Separation and Purification Technology 63: 706—709. Mendes-Pinto M.M., Raposo M.F.J., Bowen J., Young A.J. and Morais R. (2001). Evaluation of different cell disruption processes on encysted cells of Haematococcus pluvialis: effects on astaxanthin recovery and implications for bio-availability. Journal of Applied Phycology 13: 19—24. Middelberg A.P.J. (1995). Process-scale disruption of microorganisms. Biotechnology Advances 13: 491—551.

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Food Science and Technology International http://fst.sagepub.com/

Effect of Medium Composition and Kinetic Studies on Extracellular and Intracellular Production of L-asparaginase from Pectobacterium carotovorum S. Arrivukkarasan, M. Muthusivaramapandian, R. Aravindan and T. Viruthagiri Food Science and Technology International 2010 16: 115 DOI: 10.1177/1082013209353219 The online version of this article can be found at: http://fst.sagepub.com/content/16/2/115

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Effect of Medium Composition and Kinetic Studies on Extracellular and Intracellular Production of L-asparaginase from Pectobacterium carotovorum S. Arrivukkarasan, M. Muthusivaramapandian, R. Aravindan* and T. Viruthagiri Faculty of Engineering and Technology, Biochemical Engineering Laboratory, Department of Chemical Engineering, Annamalai University, Annamalai nagar — 608 002, Tamil Nadu, India Microbial L-asparaginase occupies a prominent place among biocatalysts owing to their ability to catalyze the reaction that hydrolyze the asparagine molecule. Effect of various medium components on the production of L-asparaginase in submerged fermentation by Pectobacterium carotovorum was studied for optimal nutrient requirements. Six different media compositions were tested for the L-asparaginase production keeping fermentation conditions constant at temperature 30  C, initial pH 7.0 and agitation speed of 120 rpm. Maximum intracellular and extracellular L-asparaginase activity was obtained in the medium containing tryptone, yeast extract, monosodium glutamate, K2HPO4 and L-asparagine. These medium components were further optimized by central composite experimental design using response surface methodology. Maximum intracellular and extracellular L-asparaginase activity of 2.282 U/mL and 0.587 U/mL were obtained respectively at the late logarithmic phase in optimized media. Unstructured kinetic models were used to describe the cell growth and product formation kinetics. The unstructured models predicted the cell growth and product formation profile accurately with high coefficient of determination. Key Words: L-asparaginase, Pectobacterium carotovorum, submerged fermentation, response surface methodology, kinetic modeling

INTRODUCTION L-Asparaginase (L-asparagine amido hydrolase, EC 3.5.1.1) catalyzes the hydrolytic cleavage of the substrate L-asparagine to form L-aspartate and ammonia. Its distinct antitumor activity is an incredible feature, due to which the enzyme has found wide application in pharmaceutical science as an effective chemotherapeutic agent against the acute lymphoblastic leukemia and non-Hodgkin’s lymphoma (Ylikangas and Mononen, 2000). Another significant application of L-asparaginase is its activity against the formation of acrylamide, a potent carcinogen and a neurotoxic compound present in fried foods (Stadler et al., 2002). L-asparaginase from various sources has been extensively studied ranging from prokaryotic organisms such

*To whom correspondence should be sent (e-mail: aravindraj@gmail.com). Received 7 October 2008; revised 23 January 2009. Food Sci Tech Int 2010;16(2):0115–11 ß SAGE Publications 2010 Los Angeles, London, New Delhi and Singapore ISSN: 1082-0132 DOI: 10.1177/1082013209353219

as Escherichia coli (Cedar and Schwartz, 1968), Erwinia aroideae (Peterson and Ciegler, 1969) and Pseudomonas aeruginosa (Abdel et al., 2002) to higher eukaryotes such as Aspergillus terreus (Sarquis et al., 2004), rodents, primates and plants. L-asparaginases are made up of homotetrameric subunits in 222 symmetry and each monomer consists of about 330 amino acids arranged in 8a helices and 14b sheets. The domains N-terminal and C-terminal are linked by 20 amino acidic residues (Aghaiypour et al., 2001). The L-asparaginase for anticancer treatment is commercially produced from microorganisms namely E. coli, Erwinia chrysanthemi, Wollinella succinogenes (Narta et al., 2007) whereas the Aspergillus niger and Aspergillus oryzae, L-asparaginase was mainly used for acrylamide prevention in fried foods. Development of polyethylene glycolatedasparaginase relaxed the immunological effects in the patients administered with the L-asparaginase which allowed the utilization of the enzyme from various sources (Pasut et al., 2008). The large scale production of L-asparaginase highly depends on the microbial source using submerged fermentation, where the activity is expressed mainly in intracellular form implying additional product recovery costs. Although extensive studies have been performed

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on the isolation, production and properties of the L-asparaginase, low productivity leads to high cost of enzymes still remains as a critical problem to be resolved. Studies on the effect of various medium compositions could reveal their influence on the production of extracellular and intracellular L-asparaginase that could in turn influence the large scale production of L-asparaginase. Process optimization may involve the study of many biochemical and physical parameters including media formulation and culture parameters. The classical method of changing one medium variable at a time in order to optimize performance is impractical and time consuming. The need for efficient methods for screening large number of variables has led to the adoption of statistical experimental designs. Response surface methodology is a powerful tool and an efficient mathematical approach based on the fundamental principles of statistics, such as randomization, replication and duplication, which simplifies the optimization by studying the mutual interactions among the variables over a range of values in a statistically valid manner widely applied in the optimization of fermentation. The availability of user-friendly software packages has made this technique increasingly popular for media optimization. A mathematical model describes relationships between principal state variables and explains quantitatively the behavior of the system. Studies on the cell mass and product formation kinetics could expose the status of the fermentation and can be utilized for the scale up calculations. Unstructured kinetic models are quiet satisfactory in many situations when balanced growth condition is accomplished or in many control and optimization problems in fermentation process with minimum mathematical complexity. In addition, it can provide useful information for the analysis, design and operation of a fermentation process (Aravindan and Viruthagiri, 2008). The objectives of this work were to study of the effect of various media compositions that influences the production of extracellular and intracellular L-asparaginase from the bacterium P. carotovorum MTCC1428, to analyze the mutual interactions among the variables over a range of values in a statistically valid manner using CCD and RSM, and to determine the kinetic parameters for the fermentative production of the enzyme using unstructured models in optimized fermentation medium.

was maintained on nutrient agar slants containing (g/L): beef extract, 1.0; yeast extract, 2.0; peptone, 5.0; NaCl, 5.0; agar, 15.0. Inoculum Preparation Growth characterization of the organism in the maintenance media revealed that the organism attains its mid log phase at 24 h and reaches the stationary phase at 42 h. Inoculum was prepared by growing the organism in 100 mL sterile seed medium (composition same as maintenance medium excluding agar) in 250 mL Erlenmeyer flask for 24 h on rotary shaker (120 rpm) at 30  C. All chemicals and medium components used in this study were obtained from Himedia Ltd, Mumbai, India. Methods Submerged Fermentation The six media ingredients were selected after reviewing various works on the L-asparaginase production from various microorganisms. Table 1 shows the composition of various production medium compositions utilized for the study. Media M1 is the optimized media for the production of L-asparaginase from Erwinia carotovora used by Liu and Zajic (1972). Media M2 was proposed by Maladkar et al. (1993) with lactose as carbon source and tryptone as an additional nitrogen source for L-asparaginase production by E. carotovora. Media M3 is the TGY media designed by Peterson and Ciegler (1969) for E. carotovora and media M4 (TYM media) was proposed by Maladkar et al. (1993) containing asparagine and monosodium glutamate as inducer. Medium M5 is the media optimized by Prakasham et al. (2006) for the production of

Table 1. Various medium composition used for the production of intracellular and extracellular L-asparaginase by P. carotovorum. Various medium

Composition of various medium (g/L)

M1

Lactose, 10.0; yeast extract, 15.0; K2HPO4, 1.0 Lactose, 1.0; tryptone, 5.0; yeast extract, 5.0; K2HPO4, 1.0 Tryptone, 5.0; glucose, 5.0; yeast extract, 5.0. Tryptone, 5.0; yeast extract, 40.0; monosodium glutamate, 20; K2HPO4, 1.0; L-asparagine, 5.0 Glucose, 20.0; L-asparagine, 5.0; ammonium chloride, 10.0; KH2PO4, 0.3; MgSO4.7H2O, 0.5; CaCl2. 2H2O, 0.014; NaCl, 0.5 Yeast autolysate, 40.0

M2 M3

MATERIALS AND METHODS

M4

Microorganism and Maintenance M5

P. carotovorum MTCC1428 was obtained from the Microbial Type Culture Collection (MTCC) and Gene Bank Centre, Institute of Microbial Technology, Chandigarh, India. The P. carotovorum stock culture

M6

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Kinetics of Production of L-asparaginase

L-asparaginase by Staphylococcus species 6A and the media M6 is the optimized media for Serratia marcescens proposed by Heinemann and Howard (1969). All the fermentation runs were conducted in 250 mL Erlenmeyer flask with 100 mL of the production medium. The initial pH was adjusted to 7.0 using 1 N NaOH and sterilized at 121  C for 20 min. The production medium was inoculated with 5% (v/v) of seed culture in its mid exponential phase at 24 h. The flasks were incubated in a temperature controlled rotary shaker at 120 rpm and 30  C for the fermentation period of 60 h. 2 mL of sample was withdrawn from the fermentation broth at every 6 h interval without much change in the culture volume to maintain constant oxygen transfer. Harvesting of Cells and Extraction of L-asparaginase Cells were separated from the broth by centrifugation (Remi, model C- 24BL cooling centrifuge) at 5030  g for 15 minutes at 4  C. The clarified supernatant was used for the analysis of protease activity, total soluble protein, glucose and extracellular L-asparaginase. Initial analytical experiments revealed that the L-asparaginase is located in the periplasm of the cell and could be released easily with the EDTA-lysozyme digestion procedure rather than using sonication or alkali digestion. The pellet of cells formed was lysed with 1/3 volume of stock buffer and then successively with equal volume of lysozyme (60 mg/mL) by incubating at room temperature for 90 minutes. The shock buffer comprises of 0.033 M tris buffer (pH 8.0) with 1 mM ethylenediaminetetraacetic-acid (EDTA) and 20% sucrose. The contents were centrifuged at 5030 g for 1 h at 4  C and 0.1 mL of supernatant from the lysed cells was taken for the analysis of intracellular L-asparaginase. Biochemical Assay L-asparaginase Assay L-asparaginase assay was performed by the method of Shirfrin et al. (1974): 0.1 mL of enzyme solution, 1 mL of 0.05 M tris buffer (pH 8.6), 0.9 mL of deionized water and 0.1 mL of 0.189 M asparagine solution were mixed and incubated for 30 min at room temperature. The reaction was stopped by the addition of 0.1 mL of 1.5 M trichloroacetic acid. After centrifugation, 0.2 mL of the supernatant was diluted with 4.3 mL of deionized water and treated with 0.5 mL of Nessler’s reagent and optical density was measured at the wavelength of 405 nm (Double beam UV-V Spectrophotometer, Elico India Limited, India). The OD was then compared with the standard chart prepared from solutions of ammonium sulfate as the ammonium source. One unit (U) of L-asparaginase is the amount of enzyme which liberates 1 mmole of ammonia in 1 min at 37  C.

Protease Assay The protease activity was analyzed by modified Anson’s method using casein as the substrate (Anson, 1938). Two mL of 1% (w/v) casein solution was mixed with 0.5 mL of enzyme solution and incubated at 37  C for 30 min. 2.5 mL of 10% trichloroacetic acid was added to arrest the reaction. The solution with precipitate was filtered and to 1 mL of filtrate, 5 mL of 0.5 M Na2CO3 and 0.5 mL of Folin’s reagent (1 : 3 dilutions) was added. After 30 minutes of incubation, the color density developed was determined at 660 nm in a UVV Spectrophotometer. One unit (U) of protease activity was defined as 1 mg of tyrosine liberated per minute by 1 mL of enzyme. Biomass, Glucose and Protein Determination The bacterial cell growth was determined by measuring the optical density at wavelength of 600 nm in a UV-V spectrophotometer. The biomass concentration was determined with a calibration curve made from the relationship between optical density at 600 nm and dry cell weight. The glucose and lactose concentration in the fermentation broth was determined by dinitrosalicylic acid method as described by Miller (1959). The total soluble protein in the medium was determined by Lowry’s method (Lowry et al., 1951). Central Composite Experimental Design and Response Surface Methodology The optimum concentration of the variables (tryptone, yeast extract, monosodium glutamate, K2HPO4 and L-asparagine) and their interactions were studied by response surface methodology (RSM) using central composite design (CCD). The medium components were studied at 5 levels; the design of experiments was formulated using the statistical software Minitab (Version 14, The MathWorks Inc.). A fraction of a coded, 25 central composite design with two axial points at a distance of a ¼ 2 from the design center and six replicates about the center point, making a total of 32 runs, were used to study the variables. The behavior of the system was explained by the following quadratic equation: Y ¼ 0 þ

5 X i¼1

i xi þ

5 X i¼1

ii x2i þ

4 X 5 X

ij xi xj

ð1Þ

i¼1 j¼iþ1

Where Y is the predicted response (L-asparaginase production); xi is the i-th independent variables, b0 is the intercept, i is the linear coefficients, ii is the quadratic coefficients for the factor i, and ij is the interaction coefficients between factors i and j (Abdel et al., 2002). All experiments were carried out in triplicate and their mean values are presented.

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Unstructured Model Development for Fermentation Kinetics Unstructured models were used to describe the growth of biomass and formation of metabolic products. Malthusian law states that the rate of cell mass production is directly proportional to the biomass concentration as,

2

ð3Þ

where 0 is the initial specific growth rate (1/h) and Xmax is the maximum cell mass concentration (g/L). Equation (3) on integration using X0 ¼ X (t ¼ 0) gives a sigmoidal variation X (t) that may empirically represent both exponential and stationary phase. XðtÞ ¼

X0 e0 t  0 1  XXmax ð1  e0 t Þ 

ð4Þ

The kinetic parameter, 0 in this equation is determined by rearranging Equation (4) as,     Xmax X  1 þ ln 0 t ¼ ln X0 1X

ð5Þ

describes the data where X ¼ XXmax , if the logistic h equation i

suitably, then a plot of ln

X 1X

versus t should giveia h Xmax straight line of slope ‘m0’ and intercept ln X0  1 . The kinetics of L-asparaginase production was described by Luedeking—Piret equation which states that the product formation rate depends upon both the instantaneous biomass concentration (X) and growth rate (dX/dt) in a linear fashion (Luedeking and Piret, 1959). dP dX ¼ þ X dt dt

ð7Þ

where P0 and Pt are the product concentrations at initial time and at anytime ‘t’ respectively and,

ð2Þ

where dX/dt is the growth rate (g/L h); X is the concentration of biomass (g/L);  is the specific cell growth rate (1/h). The growth of cell is governed by a hyperbolic relationship and there is a limit to the maximum attainable cell mass concentration. There is a saturation limit for growth rate on each substrate, and the cells need substrate and may synthesize products even when they do not grow. Such growth kinetics is described by logistic equation as (Weiss and Ollis, 1980),   dX X ¼ 0 1  X dt Xmax

Pt ¼ P0 þ AðtÞ þ BðtÞ

ð6Þ

3 0 t e    15 AðtÞ ¼ X0 4 X0  t 0 1  Xmax ð1  e Þ

ð8Þ

  Xmax X0  0 t BðtÞ ¼ ln 1  1e 0 Xmax

ð9Þ

The parameters  and  in Equation (7) are determined by plotting [Pt  P0]/B(t) versus A(t)/B(t) which is a straight line with slope ‘’ and intercept ‘’.

RESULTS AND DISCUSSION Effect of Various Medium Compositions on L-asparaginase Production P. carotovorum is a Gram negative rod shaped facultative anaerobic bacterium. Preliminary characterization of the bacterium revealed that the organism is catalase positive and oxidase negative. Production of extracellular and intracellular L-asparaginase by P. carotovorum in batch cultures with six different media compositions was studied (Table 1). Figure 1 illustrates the profile of

Cell mass (g/L), extracellular and intracellular L-asparaginase activity (U/mL), protease activity (U/mL)

dX ¼ X dt

where (gP/gX) and (gP/gX h) are empirical constants that may vary with fermentation conditions. Integrating Equation (6) using Equation (3),

7 6 5 4 3 2 1 0 M1

M2

M3

M4

M5

M6

Medium

Figure 1. Comparison of intracellular and extracellular L-asparaginase activity, cell mass and protease activity in six different production media. (¨) cell mass, (g) extracellular L-asparaginase activity, (m) intracellular L-asparaginase activity, (*) protease activity.

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Kinetics of Production of L-asparaginase 2

6 4 2 0 0

10

20

30

40

50

60

Time (h)

Intracellular L-asparaginase activity (U/mL)

Cell mass (g/L)

8

1.6 1.2 0.8 0.4 0

Figure 2. Time course of cell mass in various medium compositions in L-asparaginase fermentation in different media: (¨) M1, (g) M2, (m) M3, (h) M4, (S) M5 and () M6.

0.4 0.3 0.2 0.1 0 0

10

20

30

40

50

60

Figure 3. Time course of extracellular L-asparaginase activity in various medium compositions by P. carotovorum in different media: (¨) M1, (g) M2, (m) M3, (h) M4, (S) M5 and () M6. cell mass, protease activity, intracellular and extracellular L-asparaginases. Maximum yield of cell mass was obtained in the medium M6, followed by the medium M2, M1, M4, M5 and M3 (Figure 2). In the medium M1, M2, M3 and M5 the stationary phase was attained at 30 h, whereas in the medium M4 and M6 the stationary phase was attained at 42 h. P. carotovorum was found to produce both extracellular and intracellular L-asparaginase. Maximum extracellular L-asparaginase activity of 0.607 U/mL was observed in the media M4 followed by M2 (0.313 U/ mL at 18 h), M1 (0.267 U/mL, at 12 h) and M6 (0.138 U/mL at 24 h) and no extracellular L-asparaginase activity was detected in M3 and M5 (Figure 3). This result shows that the extracellular enzyme was maximum during the late log phase in all the media except media M6 in which the enzyme was secreted at earlier logarithmic stage. The intracellular enzyme production of 1.8 U/mL was obtained in the media M4 at 36 h. Both the extracellular and intracellular enzyme production was found to be growth associated. The order of production of

60

2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 0

Time (h)

40

Figure 4. Time course of intracellular L-asparaginase activity in various medium compositions by P. carotovorum in different media: (¨) M1, (g) M2, (m) M3, (h) M4, (S) M5 and () M6.

0.6 0.5

20 Time (h)

Protease activity (U/mL)

Extracellular L-asparaginase activity (U/mL)

0.7

0

20

40

60

Time (h)

Figure 5. Time course of protease activity in various medium compositions in L-asparaginase fermentation by P. carotovorum in different media: (¨) M1, (g) M2, (m) M3, (h) M4, (S) M5 and () M6. intracellular L-asparaginase was found to be M4 > M6 > M5 > M2 > M3 > M1 (Figure 4). In the medium M1 (0.144 U/mL) and M5 (0.298 U/mL) maximum L-asparaginase activity was obtained at 24 h whereas in media M3 (0.236 U/mL) and M6 (1.12 U/mL), maximum enzyme activity was found during the late log phase of 36 h. L-asparaginase activity of 0.210 U/mL was obtained in the media M2 at 30 h. The protease activity was determined in all the fermentation runs to find the effect of protease on L-asparaginase production. Protease production was maximum in the medium M5 and the protease production was high in the order M5 > M4 > M3 > M2 > M6 > M1 as shown in Figure 5. Maximum protease activity was observed during the stationary phase of P. carotovorum at 60 h (Figure 5). In the medium M2, M3 and M5, the protease activity was detected during the logarithmic phase of growth and the maximum activity was observed during the late logarithmic phase of the fermentation, whereas in the medium M1, M4 and M6 the maximum protease activity was observed during the stationary phase.

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Extracellular protease production was observed in all the media examined. In the medium M2, M3 and M5 maximal activity of protease was observed during the exponential phase of the organism. The decrease in L-asparaginase production during the late hours of fermentation might be due to the action of protease. It has been reported previously by Sarquis et al. (2004) that after 60 h, the protease production reaches its maximum in the production of L-asparaginase from A. terreus and Aspergillus tamari. Similar results have been obtained by Aguilar et al. (2001) in A. niger for L-asparaginase production. The analysis of variance of the experimental results with the three trials was done using SPSS statistical software (Version 16.0.2) which shows that the error is not significant as the p value (0.981) is greater than 0.05. The maximum intracellular and extracellular L-asparaginase production was observed in the medium M4 which contains the following medium composition (g/L): tryptone 5.0, yeast extract 40, monosodium glutamate 20, K2HPO4 1.0 and L-asparagine 5.0. Response Surface Optimization of Medium Components The five variables in the medium M4 were optimized by CCD experimental design (Table 2). Table 3 shows the experimental and predicted values of cell mass and L-asparaginase activity. The multiple regression analysis of the experimental results of the CCD was fitted with the second-order polynomial function for the estimation of L-asparaginase production: Y ¼ 1:9247 þ 0:1515x1 þ 0:3751x2 þ 0:0412x3  0:0337x4 þ 0:1337x5 þ 0:0673x1 x2  0:0310x1 x3 þ 0:1160x1 x4  0:0653x1 x5 þ 0:0463x2 x3  0:0110x2 x4  0:0029x2 x5  0:0435x3 x4  0:0526x3 x5  0:0109x4 x5  0:1024x21  0:1288x22  0:0606x23  0:1964x24  0:0342x25 ð10Þ

Table 2. Experimental range and levels of the five significant independent variables used in response surface methodology in terms of actual and coded factors. Coded levels Variables X1 X2 X3 X4 X5

-

Tryptone Yeast extract Monosodium glutamate K2HPO4 L-asparagine

2

1

0

1

2

1 20 5 1 1

3 25 10 1.5 3

5 30 15 2 5

7 35 20 2.5 7

9 40 25 3 9

where Y is the response variable (L-asparaginase activity) and x1, x2, x3, x4 and x5 are the coded values of the independent variables tryptone, yeast extract, monosodium glutamate, K2HPO4 and L-asparagine concentration in medium, respectively. The factorial design enables the identification of the medium components that plays a significant role on L-asparaginase production and their concentration ranges for maximum L-asparaginase production. The second degree polynomial equation was maximized by a constraint search procedure using the MATLAB software (Version 6.5, The MathWorks, Inc. Natick, USA) to obtain the optimal levels of the independent variables and the predicted maximum L-asparaginase activity (Aravindan and Viruthagiri, 2007). The student t distribution and the corresponding P values, along with parameter estimates, were evaluated by MINITAB software. Statistical testing of the model was done in the form of analysis of variance (ANOVA), which is required to test the significance and adequacy of the model. The ANOVA result for the quadratic regression model is given in Table 4. The ANOVA showed that the model was highly significant with good adequacy of the second-order polynomial model proposed to explain the observed yields. Student ‘t’ test indicated that linear coefficient (x2) and quadratic term (x24 ) were highly significant (p < 0.005). The coefficient of determination (R2) for the production of L-asparaginase was 0.829, implying that only 17.1% of the total variations are not explained by the model. The 3D surface response plots were generated for the response (L-asparaginase) at any two independent variables while keeping the others at their centre point. Thus, 10 three-dimensional plots and corresponding 2D plots were obtained by considering all the possible combinations. Figure 6 shows the contour plots of the medium components (a) tryptone and yeast extract, (b) tryptone and monosodium glutamate, (c) yeast extract and K2HPO4 and (d) yeast extract and L-asparagine on L-asparaginase production by P. carotovorum with the remaining factors held constant at the middle level of the central composite experimental design. If the contour plot is elliptical, the maximum point is obtained at the point of intersection of major and minor axis of the ellipse. In the contour plots, the stationary or the centroid point is the point at which the slope of the response surface is zero when taken in all directions. In Figure 6, the contour plots are not perfectly elliptical which indicates that there is less interaction among the independent variables. From the plot of the mean L-asparaginase activity and the experimental levels of tryptone, the maximum mean L-asparaginase activity was observed at the higher factor setting. Similar trend was obtained in the plot for the mean L-asparaginase activity and experimental range of monosodium glutamate concentration and

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Kinetics of Production of L-asparaginase

Table 3. The central composite design matrix of independent variables used in response surface methodology with corresponding experimental and predicted values of L-asparaginase activity. Experimental

Predicted by RSM model

Run no.

x1

x2

x3

x4

x5

Cell massa (g/L)

L-asparaginase activityb (U/mL)

Cell massa (g/L)

L-asparaginase activityb (U/mL)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0

1.899 3.241 2.006 2.839 1.947 2.913 1.933 2.456 3.112 3.581 2.684 3.876 2.649 3.235 2.972 3.226 2.54 3.413 2.955 2.496 2.512 1.909 1.801 3.8 2.973 1.966 3.201 3.727 3.814 3.523 3.7 3.785

1.36852 0.95638 1.5694 1.95684 1.07011 0.86541 2.03695 1.99213 0.86533 1.50247 1.85692 2.14621 1.02895 1.12382 1.53787 2.09843 0.74084 1.90457 0.56988 1.86439 1.12562 1.8542 1.23474 0.65853 1.15648 2.03455 2.15682 1.782 1.98253 1.90661 2.03659 2.0685

1.7832 3.135 1.9592 2.5598 1.9314 2.6648 1.7442 2.2768 3.1962 3.4328 2.5954 3.7968 2.5912 3.187 2.9832 3.0046 2.5334 3.8518 3.067 2.8166 2.686 2.1672 2.175 3.8582 2.947 2.4246 3.553 3.553 3.553 3.553 3.553 3.553

1.2559 0.8977 1.3195 1.9321 1.0127 1.0333 2.0139 2.0229 0.5565 1.4191 1.5825 1.9255 0.9469 1.0955 1.3185 2.1043 1.2121 1.8181 0.6593 2.1597 1.5999 1.7647 1.2065 1.0717 1.5205 2.0553 1.9247 1.9247 1.9247 1.9247 1.9247 1.9247

Note: x1: Tryptone; x2: Yeast extract; x3: Monosodium glutamate; x4: K2HPO4; x5: L-asparagine. a,b The maximum values of cell mass and L-asparaginase activity respectively and are the mean of triplicates. 250 mL Erlenmeyer flask containing 100 mL production medium (constant) was incubated in an orbital shaker (variable) for the fermentation period of 60 h.

Table 4. Analysis of variance values for the quadratic regression model obtained from central composite design employed in optimization of medium for L-asparaginase production by P. carotovorum. Source Regression Linear Square Interaction Error Lack of Fit Pure Error Total

Degree of freedom

Sum of squares

Mean square

F- value

p- value

20 5 5 10 11 6 5 31

6.61893 4.42479 1.71009 0.48405 1.36747 1.28104 0.08643 7.98640

0.33095 0.88496 0.34202 0.04840 0.12432 0.21351 0.01729

2.66 7.12 2.75 0.39

0.049 0.003 0.075 0.926

12.35

0.007

between L-asparaginase activity and experimental range of L-asparagine concentration. From the plot of the mean L-asparaginase activity and experimental range for yeast extract concentration, it was evident that L-asparaginase production was maximum just above the center point in the experimental range, but further

increase in tryptone concentration reduced the L-asparaginase activity. Similar trend was obtained in the plot for the mean L-asparaginase activity and experimental range of K2HPO4 concentration with the maximum mean L-asparaginase activity observed at the center point of K2HPO4 concentration (Figure 7); however

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S. ARRIVUKKARASAN ET AL. 0.8 1.3 1.3 2.3

Yeast extract

1 0 –1

(b) Monosodium glutamate

(a) 2

2

1.3 1.6 1.9

1 0 –1 –2

–2 –2

–1

0

1

–2

2

–1

0.5 1.0 1.5

K2HPO4

1

2.0

0

1.8

–2 2

2.3

0

–2 1

0.8

1

–1

0

2

1.3

–1

–1

1

(d) 2

L-asparagine

(c) 2

–2

0 Tryptone

Tryptone

–2

–1

0

1

2

Yeast extract

Yeast extract

Figure 6. Contour plots of (a) tryptone and yeast extract, (b) tryptone and monosodium glutamate, (c) yeast extract and K2HPO4 and (d) yeast extract and L-asparagine on L-asparaginase production by P. carotovorum using central composite experimental design.

Figure 7. Main effects plot (data means) for L-asparaginase activity with the medium components (a) tryptone, (b) yeast extract, (c) monosodium glutamate, (d) K2HPO4 and (e) L-asparagine. Downloaded from fst.sagepub.com at HINARI on February 22, 2011

Kinetics of Production of L-asparaginase

the optimum concentration of the medium components can be determined by using the polynomial equation. The regression equation (Equation 10), was solved using MATLAB and the optimal values of the test variables in the coded units were found to be x1 ¼ 1.31, x2 ¼ 1.89, x3 ¼ 0.56, x4 ¼ 0.19 and x5 ¼ 0.21, and the corresponding uncoded values were X1 ¼ 7.63 g/L, X2 ¼ 39.45 g/L, X3 ¼ 17.81 g/L, X4 ¼ 2.09 g/L and X5 ¼ 5.43 g/L giving a predicted optimum L-asparaginase activity of 2.401 U/mL. The optimized media obtained from the statistical evaluation comprised of the following concentration of the medium components (g/L): tryptone, 7.63; yeast extract, 39.45; monosodium glutamate, 17.81; K2HPO4, 2.09; and L-asparagine, 5.43. The optimized medium possesses the components monosodium glutamate and L-asparagine which has considerably increased the intracellular and extracellular production of L-asparaginase. Maximum L-asparaginase activity was observed when 1% (w/v) of L-glutamate was used in the fermentation media for Proteus vulgaris supports the results (Tosa et al., 1971). Maladkar et al. (1993) reported that monosodium glutamate in addition to the tryptone, glucose and yeast extract in fermentation media increased the L-asparaginase production by 12 fold. It is also evident that the supplementation of the production media with the free amino acids such as L-asparagine, L-aspartate, L-glutamate and L-glutamine increased the production of enzyme considerably in E. carotovora and in S. marcescens (Sukumaran et al., 1979). In Pseudomonas aeruginosa, addition of asparagine was found to enhance the production of L-asparaginase (Abdel et al., 2002). In E. coli, addition of L-asparagine or its product L-aspartate and ammonia individually do not induce the L-asparaginase production but addition of a mixture of amino acids increased the production of the enzyme. In the media M6, the yeast autolysate which is a rich source of vitamins and free amino acids, increased the intracellular production of the enzyme to a higher extent next to media M4. In S. marcescens, 4% autolyzed yeast extract medium yielded maximum cell mass and L-asparaginase (Heinemann and Howard, 1969). Media M1 and M2 showed a high level of enzyme production than media M3 and M5, which might be due to the presence of lactose. Presence of tryptone along with yeast extract and glucose increased the production to 13 fold in E. carotovora (Maladkar et al., 1993). In contrast, the production of L-asparaginase was minimal in the media with tryptone and yeast extract when compared with the media containing only yeast extract as the nitrogen source (Liu and Zajic, 1972). Considering the media M3 and M5, the media with the carbon source glucose showed catabolite repression for the enzyme production. Glucose is the basic carbohydrate that could be utilized by all microorganisms very easily as they directly enters the glycolytic pathway. In the presence of glucose with other carbon sources, the organisms will prefer to utilize

123

the glucose first and they do not easily switch over to the new carbon source due to the fact that the level of cyclic adenosine monophosphate required for the activation of operons is repressed, which in turn inhibits the catabolism of the required molecules. In this case, the regulon for the asparaginase might not have switched on due to the presence of glucose in the media. This type of repression of the catabolism of various compounds by glucose is called as glucose catabolite repression. Similar results were reported in 1968, that the glucose was found to repress the L-asparaginase production in E. coli which might be due to the catabolite repression and the decrease in pH in these media are due to glucose metabolites. For E. coli, galactose was found to enhance L-asparaginase production among the various carbon sources tested and the addition of glucose completely repressed the enzyme production (Cedar and Schwartz, 1968). Liu and Zajic (1972) reported that the bacterium E. carotovora produces only intracellular L-asparaginase and they are present mostly in periplasm which can be easily released by lysozyme digestion or by sonication. By altering the media composition, the production of extracellular L-asparaginase could be activated that could reduce the cost on cell disruption, an important step in the isolation of intracellular enzymes. In Saccharomyces cerevisiae, the extracellular enzyme was stimulated during the nitrogen starvation in the cells (Dunlop and Roon, 1975). Extracellular L-asparaginase was completely absent in the media M3 and M5. In addition it is evident that the high extracellular protease activity in the media with glucose has significantly affected the production of extracellular asparaginase in the media M3 and M5. Ramirez and Bentley have shown that certain recombinant protein production in a glucose based medium was affected with protease activity (Ramirez and Bentley, 1995). Validation of Model To confirm the predicted response from the polynomial equation, a validation experiment was carried out under optimum conditions. It was found that the L-asparaginase production increased with the growth of P. carotovorum and found to be maximum at 30 h (2.282 U/mL), which was near the predicted value of L-asparaginase (2.401 U/mL). Kinetics of L-asparaginase Fermentation in Screened Media The extracellular and intracellular L-asparaginase production was found to increase gradually from 6 h of the fermentation period in the optimized medium when the growth of the microorganism reaches the early exponential phase which shows that the L-asparaginase production is growth associated. The maximum

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2.5

10 9 8 7 6 5 4 3 2 1 0

2 1.5 1 0.5 0 0

10

20

30 40 Time (h)

50

increase from initial pH of 7.0 to 8.19 at 48 h and remains constant thereafter for the entire fermentation period. The increase in pH may be due to the formation and accumulation of ammonia in the fermentation media. De Jong (1972) also observed the increase in pH accompanied with the increase in L-asparaginase production by Streptomyces griseus. Unstructured Kinetic Model for Cell Growth and Product Formation P. carotovorum in batch culture using the optimized medium showed a classical cell growth trend. From the plot of Equation (5), with values x0 ¼ 0.348 g dry weight/L and xm ¼ 2.98 g dry weight/L, the value of 0 was found to be 0.14 1/h. Figure 9 shows the experimental and model predictions of cell growth by logistic model and L-asparaginase production by logistic incorporated Luedeking—Piret model at the optimized conditions. Logistic model predicted the cell growth kinetics of P. carotovorum with high R2 (coefficient of determination) value of 0.995. The coefficient of determination for the product formation was found to be 0.9397 in the range of 0—2.5 U/mL (y ¼ 0.9279x þ 0.0862, R2 ¼ 0.9397). L-asparaginase production kinetics was described using the logistic incorporated Leudeking—Piret equation. The experimental data were fitted with Equation (7) and the parameters  and  in equation are determined by plotting [Pt  P0]/B(t) versus A(t)/B(t) which is a straight line with a slope ‘’ of 1.35 and intercept ‘’ of 0.002. The values of a and b signifies that the L-asparaginase production by P. carotovorum is growth associated since the magnitude of the growth associated parameter ‘’ is much greater than the magnitude of non-growth associated parameter ‘’ in Luedeking—Piret model.

Extracellular and intracellular L-asparaginase activity (U/mL)

Cell mass (g/L), protease activity (U/mL), total soluble protein (g/L), pH

extracellular L-asparaginase activity was found in the late exponential phase and early stationary growth phase of P. carotovorum. The maximum extracellular L-asparaginase activity of 0.587 U/mL at 30 h fermentation period and intracellular activity of 2.282 U/mL was obtained at 36 h of fermentation and the activity reduced significantly after 36 h. The decrease in L-asparaginase activity might be due to the increased protease production during the stationary phase of the microorganism and also due to scarcity of the substrate. The kinetic profile of L-asparaginase activity, protease activity, cell mass concentration, pH and total soluble protein concentration in the optimized medium is given in Figure 8. The protease activity reached a maximum value of 1.25 U/mL at 60 h at the stationary phase of P. carotovorum. Maximum cell mass concentration of 2.98 g/L was observed at 42 h during the stationary phase and there was no further increase in the cell mass concentration. The pH of the fermentation medium was found to

60

Figure 8. Profile of intracellular L-asparaginase activity (m), extracellular L-asparaginase activity, (h), protease activity (), pH (¨), cell mass concentration (g) and total soluble protein (S) in optimized medium.

Intracellular L-asparaginase activity (U/mL), cell mass (g/L)

3.5 3 2.5 2 1.5 1 0.5 0 0

6

12

18

24

30

36

42

Fermentation time (h)

Figure 9. Experimental and model predictions of cell growth () by logistic model and L-asparaginase activity (¨) by logistic incorporated Luedeking—Piret model for L-asparaginase fermentation by P. carotovorum in optimized medium. The points represent the experimental value and the line represents the predicted values. Downloaded from fst.sagepub.com at HINARI on February 22, 2011

Kinetics of Production of L-asparaginase

CONCLUSION P. carotovorum has the ability to produce considerable amount of both extracellular and intracellular enzyme under appropriate inducers and carbon source. By understanding the regulatory concepts for the synthesis of extracellular and intracellular enzyme, large scale production of the asparaginase can be enhanced. The response surface methodology (RSM) was successfully adopted for optimization and the polynomial response equation was used to determine the optimum medium composition for the yield of L-asparaginase. The unstructured kinetic model presented in this work provides a good description of cell mass and product formation kinetics. Satisfactory agreement between numerical results and experimental data was evidenced with high coefficient of determination.

ACKNOWLEDGMENT The authors gratefully acknowledge the Department of Chemical Engineering, Annamalai University for providing the facilities to carry out this research work.

REFERENCES Abdel F., Yasser R. and Olama Z.A. (2002). L-Asparaginase production by Pseudomonas aeruginosa in solid-state culture: Evaluation and optimization of culture conditions using factorial designs. Process Biochemistry 38(1): 115—122. Aghaiypour K., Wlodawer A. and Lubkowski J. (2001). Do bacterial L-asparaginases utilize a catalytic triad Thr-Tyr-Glu? Biochimica et Biophysica Acta 1550: 117—128. Aguilar C.N., Augur C., Favela E. and Viniegra-Gonzalez G. (2001). Production of L-asparaginase by Aspergillus niger Aa-20 in submerged and solid state fermentation: Influence of glucose and tannic acid. Journal of Industrial Microbiology and Biotechnology 26: 296—302. Anson M.L. (1938). The estimation of pepsin, trypsin, papain and cathepsin with hemoglobin. Journal General Physiology 22: 79—89. Aravindan R. and Viruthagiri T. (2007). Optimization of medium composition for lipase production by Candida rugosa NCIM 3462 using response surface methodology. Canadian Journal of Microbiology 53(5): 643—655. Aravindan R. and Viruthagiri T. (2008). Evaluation of various unstructured kinetic models for protease production by Bacillus sphaericus MTCC511. Engineering in Life Science 8(2): 179—185. Cedar H. and Schwartz J.H. (1968). Production of L-Asparaginase II by Escherichia coli. Journal of Bacteriology 96(6): 2043—2048.

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De Jong P. (1972). L-asparaginase production by Streptomyces griseus. Applied Microbiology 23(6): 1163—1164. Dunlop P.C. and Roon R.J. (1975). L-Asparaginase of Saccharomyces cerevisiae: an Extracellular enzyme. Journal of Bacteriology 122(3): 1017—1024. Heinemann B. and Howard A.J. (1969). Production of L-asparaginase by submerged growth of Serratia marcescens. Applied Microbiology 18(4): 550—554. Liu F.S. and Zajic J.E. (1972). L-Asparaginase synthesis by Erwinia aroideae. Applied Microbiology 23(3): 667—668. Lowry O.H., Rosenbrough M.J., Farr A.L. and Randell R.J. (1951). Protein measurement with Folin phenol reagent. Journal of Biological Chemistry 193: 265—275. Luedeking R. and Piret E.L. (1959). A kinetic study of the lactic acid fermentation. Biotechnology and Bioengineering 1: 393—412. Maladkar N.K., Singh V.K. and Naik S.R. (1993). Fermentative production and isolation of L-Asparaginase from Erwinia carotovora, EC-113. Hindustan Antibiotics Bulletin 35(1—2): 77—86. Miller G.L. (1959). Use of dinitrosalicylic acid reagent for determination of reducing sugar. Analytical Chemistry 31: 426—428. Narta U.K., Kanwar S.S. and Azmi W. (2007). Pharmacological and clinical evaluation of L-asparaginase in the treatment of leukemia. Critical Reviews in Oncology 61: 208—221. Pasut G., Sergi M. and Veronese F.M. (2008). Anti-cancer PEGenzymes: 30 years old, but still a current approach. Advanced Drug Delivery Reviews 60(1): 69—78. Peterson R.E. and Ciegler A. (1969). L-asparaginase production by Erwinia aroideae. Applied Microbiology 18(1): 64—67. Prakasham R.S., Subba Rao C., Sreenivas Rao R., Lakshmi G.S. and Sarma P.N. (2006). L-asparaginase production by isolated Staphylococcus sp.—6A: design of experiment considering interaction effect for process parameter optimization. Journal of Applied Microbiology 102: 1382—1391. Ramirez D.M. and Bentley W.E. (1995). Fed-Batch feeding induction policies that improve foreign protein synthesis and stability by avoiding stress response. Biotechnology and Bioengineering 47: 596—608. Sarquis M.I.M., Oliveira E.M.M., Santos A.S. and Costa G.L. (2004). Production of L-asparaginase by filamentous fungi. Mem Inst Oswald Cruz, Rio de Janeria 99(5): 489—492. Shirfrin S., Parrott C.L. and Luborsky S.W. (1974). Substrate binding and Intersubunit interactions in L-asparaginase. Journal of Biological Chemistry 249: 1335—1340. Stadler R.H., Blank I.D., Varga N., Robert F., Hau J., Guy P.A., Robert M.C. and Riediker S. (2002). Acrylamide from Maillard reaction products. Nature 419: 449—450. Sukumaran C.P., Singh D.V. and Mahadevan P.R. (1979). Synthesis of L-asparaginase by Serratia marcescens (Nima). Journal of Bioscience 1(3): 263—269. Tosa T., Sano R., Yamamoto K., Nakamura M., Ando K. and Chibata I. (1971). L-Asparaginase from Proteus vulgaris. Applied Microbiology 22(3): 387—392. Weiss R.M. and Ollis D.F. (1980). Extracellular microbial polysaccharides I. Substrate, biomass and product kinetic equations for batch Xanthan gum fermentation. Biotechnology and Bioengineering 22: 859—873. Ylikangas P. and Mononen I. (2000). A fluorometric assay for L-Asparaginase activity and monitoring of L-Asparaginase Therapy. Analytical Biochemistry 280: 42—45.

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Food Science and Technology International http://fst.sagepub.com/

Konjac Glucomannan as a Carrier Material for Time−−Temperature Integrator

J. Wang, Li Deng, Yin Li, X. Xu, Yi Gao, Nahimana Hilaire, H. Chen, Z. Jin, Jin Moon Kim and L. He Food Science and Technology International 2010 16: 127 originally published online 5 February 2010 DOI: 10.1177/1082013209353082 The online version of this article can be found at: http://fst.sagepub.com/content/16/2/127

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Konjac Glucomannan as a Carrier Material for Time–Temperature Integrator JinPeng Wang,1,2 Li Deng,1,2 Yin Li,3 XueMing Xu,1,2 Yi Gao,1,2 Nahimana Hilaire,1,2 HanQing Chen,1,2 ZhengYu Jin,1,2,* Jin Moon Kim1,2,* and LiFeng He1,2 1

The State Key Lab of Food Science and Technology, Jiangnan University, Wuxi, 214122, China 2 School of Food Science and Technology, Jiangnan University, Wuxi, 214122, China 3 Department of Plant Sciences, North Dakota State University, Fargo, ND, 58105, USA Hardness, springiness and water retention of konjac glucomannan gel (g-KGM) as a novel carrier material for time–temperature integrator (TTI) in aseptic processing were determined and compared with those of sodium alginate gel (g-SA). Hardness of both g-KGM and g-SA increased with temperature: values of g-SA were significantly higher (p < 0.05) than those of g-KGM at all temperatures. No significant difference in springiness between g-KGM and g-SA from 40 C to 90  C and significant differences (p < 0.05) between 100  C and 140  C were found. Water retention property of g-KGM was lower than that of g-SA between 60  C and 100  C, but much higher between 100  C and 140  C. Heat transfer tests performed on g-KGM alone as well as on g-KGM as a carrier, embedded with TTI, a-amylase as an integrator, indicated that g-KGM was suitable for industrial ultrahigh temperature sterilization test. Key Words: aseptic processing, konjac glucomannan gel, time–temperature integrator, sodium alginate gel, retort system, thermostable a-amylase

INTRODUCTION Time–temperature integrator (TTI) could quantify the impact of integrated time–temperature on a target attribute, without information on the actual temperature history of the product (Taoukis and Labuza, 1989; Weng et al., 1991). Three kinds of TTI are being used in the food industry: dispersed, permeable and isolated TTI (Van Loey et al., 1996). To avoid influence of complex food environment on kinetic behavior of the TTI, encapsulated TTI, embedded in a carrier material system, has been proposed by Van Loey et al. (1996). Some of the required properties that TTI-carrier system should meet have been intensively discussed by Maesmans (1993). To make the rate of heat transfer to the TTI comparable to the rate of heat transfer to the target quality attribute in a solid food particle, the thermal diffusivity of the chosen TTI-carrier material should be in the same range as that of the foodstuff (Van Loey et al., 1996). *To whom correspondence should be sent (e-mail: jinlab2008@yahoo.com; jin.moon.kim@hotmail.com). Received 10 November 2008; revised 16 February 2009. Food Sci Tech Int 2010;16(2):0127–8 ß SAGE Publications 2010 Los Angeles, London, New Delhi and Singapore ISSN: 1082-0132 DOI: 10.1177/1082013209353082

In case where encapsulated TTIs are used in aseptic processing of liquid/particle food, the mechanical behavior of the TTI-carrier system should be investigated to avoid deformation of the particle, which influences the residence time of the particle (Ramaswamy and Abdelrahim, 1995). Also, the carrier material should be adapted to the heating process such as the high temperature and high pressure (Barigou et al., 1998; Guiavarch et al., 2005). Nylon (Hendrickx et al., 1992), polyacetal (Weng et al., 1991), real food (Guiavarch et al., 2002; Tucker et al., 2002; Mehauden et al., 2007; Mabit et al., 2008) and alginate beads (Heppel, 1985) have been investigated as carrier materials. Considering the requirement of TTI carrier, alginate beads seem to be an appropriate carrier due to the similar thermal diffusivity and density of the real food. However, pervious findings in our lab indicated that alginate beads showed poor distortion properties when used in aseptic processing of liquid/particle (Li, 2006). Thus, there is a need to find a novel TTI carrier material for the aseptic processing. Konjac glucomannan (KGM) is a high molecular weight, water soluble and non-ionic heteropolysaccharide found in tubers of the Amorphophallus konjac plant. KGM is a linear random copolymer of (1-4)-b-D-glucopyranose and b-D-mannopyranose, having glucose and mannose units in a molar ratio of 1 : 1.6 with a low degree of acetyl groups (approximately 1 acetyl group

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per 19 residues) at the C-6 position (Martin et al., 2000; Xu et al., 2007). Hardness and elasticity of konjac glucomannan gel (g-KGM) increased with the increase in deacetylation (Shanjun and Nishinari, 2004; Penroj et al., 2005), and deacetylation of KGM increased with the increase in temperature (Shen and Yang, 1995). Considering the requirement of TTI-carrier, g-KGM as a kind of food (Zhong et al., 2005) had the similar thermal diffusivity and density as the real food. These properties suggest that g-KGM could be used as a novel TTI carrier in the aseptic processing of liquid/particle. The objective of this study was to evaluate the behavior of g-KGM when used as a TTI carrier in aseptic processing. In order to evaluate the feasibility of g-KGM as a TTI carrier, gel properties, such as hardness and elasticity as well as water retention property of g-KGM, were determined and compared with those of sodium alginate gel (g-SA). Application and validation experiment were also conducted employing TTI, in which a-amylases was used as an indicator, and g-KGM as a carrier.

MATERIALS AND METHODS g-KGM and Sodium Alginate Gels g-KGM was prepared by dissolving KGM powder (Qiangsen Company, China) into 200 mL phosphate buffer (preheated and degassed) at the concentration of 6% (w/v); the pH was adjusted to confirm the optimum pH. Then the mixture was stirred in a water bath at 30  C for 10 min. Prepared mixture was left at 90  C in a water bath for an additional 2 h to form g-KGM. g-SA was prepared according to the procedures described by Roopa and Bhattacharya (2008). Equal volumes (25 mL) of calcium chloride (1.1%) solution and SA (2.1%) dispersion were mixed by continuous stirring for 10 min at 37.6  C in a water bath. The pH was adjusted to 5, and the resulting dispersion was poured into a beaker where a gel was formed. Prepared gels were left at room temperature for 4 h before the compression test was carried out. For measurement of textural properties, gels were cut to 60 mm diameter and 10 mm height. For measurement of heat transfer properties, gels were shaped into 60 mm length and 10 mm diameter. Methods Experimental Setup A retort system used in this experiment (Li, 2006) is shown in Figure 1. The device consists of flow system, heating system, data collection system, vacuum system and high pressure system. Using fluidization heating of liquid/particles, ultrahigh temperature sterilization was

achieved with the soybean oil as a heating medium, and then flash evaporation was carried out to reduce the temperature to room temperature rapidly. The sterilization procedures used were as follows: (1) A thermocouple was inserted in the geometric center of the heating object in the retort chamber (B) to monitor the temperature. Temperature profiles were collected by the data collection system (CMC-92 data acquisition system, TR9216, Ellab Company, Denmark). (2) The heating system was turned on to preheat the heating medium to the desired temperature (soybean oil was used as a heating medium in this system). (3) The valves 1 and 4 were opened, the preheated oil was poured into the retort chamber through valve 1 and the cooled oil returned to the oil heating and storage tank (A) through valve 4. The heating oil was circulated between retort chamber and oil heating system to maintain the temperature. Simultaneously, valve 5 was opened to provide high pressure. Different pressures were provided for different temperatures (1.2 MPa for 100  C, 1.5 MPa for 110  C, 2.0 MPa for 120  C, 2.8 MPa for 130  C, 3.7 MPa for 140  C) in the retort to avoid evaporation of water. (4) When heat treatment was accomplished, oil heating was discontinued and valves 1, 4 and 5 were closed. Valve 3 was opened and the pressure in the retort chamber was released. Then, valve 3 was closed, and valves 5 and 2 were opened to recover the oil from the retort chamber. (5) Valves 5 and 2 were closed and valve 6 was opened to introduce vacuum and to cool the retort chamber to room temperature. Then, valve 6 was closed and valve 3 was opened. (6) Heat-treated material was taken out of the retort chamber. Operation of the heating system was controlled by a preset computer program. Texture Profile Analysis and Water Retention Properties of g-KGM and g-SA A compression test was performed using a 5-cm diameter plunger attached to TA.XTi2 Texture Measuring Machine (Stable Microsystems, UK). Cylindrically shaped g-KGM and g-SA (60 mm in diameter, 10 mm in height) were compressed to 50% deformation, the plunger was withdrawn to the original height, the sample was rested for 5 s, and the sample was subjected to a second compression-withdrawal cycle to 50% deformation. Speed of compression head was adjusted to 1 mm/s. The hardness value is a maximum peak force during the first compression. Springiness is the degree to which the sample returns to its original size after compression. It can be considered as the ratio of the second compression distance to the distance of the first compression (Jin, 1991). Triplicates were performed for each sample. Water retention properties of g-KGM and g-SA were determined by contrasting the weight before and after heating; this determination was made on samples treated

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4

5

High pressure system

6 (C) Data collection system

Food particle

Out

In

Heating medium purifier

Vacuum system

(B)

1

2

3 Residual steam

Heating medium

(A)

Pump

Oil heating system and storage tank

Figure 1. Schematic diagram of the heating system used in this experiment (see details of the workflow in Materials and Methods section). by heat treatment (HT) 1 Percent water retention was calculated as follows: %Water retention ¼ 0 Weight of the gel before heat treatment 1 B C B1  Weight of the gel after heat treatment C  100 @ Weight of the gel after heat treatment A

Preparation of Bioindicator The thermostable a-amylase with initial activity of 20,000 U/mL was used as a bioindicator of TTI. Optimum pH and temperature of a-amylase are 6.0 and 90  C, respectively (Genencor Bio-Products Limited Company, China). One gram of the a-amylase was dissolved in 100 mL of the Tris buffer (pH 6.0, 0.05 mol/L) to make a final concentration of a-amylase, 10 mg/mL. This solution was stored at 18  C until used. Thawed amylase solution (20 mL) was inserted into a capillary tube (1 mm internal diameter and 50 mm long) by holding the middle of the tube with cool cotton. The extremities of the tubes were closed by the flame of an alcohol burner. During this process, direct contact of a-amylase with the flame was avoided. The prepared TTI was used immediately. Heat Treatment Experiments Four kinds of HTs were carried out in this study. HT 1: cylindrical (60 mm in diameter, 10 mm in height) g-KGM or g-SA was heated in the retort (soybean oil was used as

the heating medium), and textural properties and water retention ability were determined. HT 2: a thermocouple was inserted in the geometric center of the cylindrical (60 mm in length, 10 mm in diameter) g-KGM and heated in the retort to determine time–temperature profiles. HT 3: prepared TTI was heated in the silicone oil bath at various temperatures for different time (Table 1) to determine inactive kinetics of thermostable a-amylase. HT 4: TTI embedded in the geometric center of g-KGM (60 mm in length, 10 mm in diameter) was heated in the retort at three conditions selected at random from time– temperature profiles generated by HT 2.

Measurement of TTI Activity After the heat treatments, the extremities of the TTI were broken and flushed with 5 mL Tris buffer and collected in a flask. Residual activity of a-amylase as an integrator in TTI was measured according to the following method: ethylidene-blocked p-nitrophenylmaltoheptaoside was used as a substrate for a-amylase assay. This substrate is cleaved by the a-amylase mainly into glucose and p-nitrophenol. The increase in absorbance represents the total amylase activity in the sample (Mehauden et al., 2007). Absorbance was read at 405 nm using a spectrophotometer (Unic, Shanghai, China). From the period of 10 s through 240 s, absorbances were recorded at every 10 s. Residual enzyme activity was recorded by changes in absorbance per minute resulting from a linear regression of absorption versus reaction time between 10 and 240 s.

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Table 1. Heat treatment of TTI.

Table 2. TTI response functions for different reaction orders.

Temperature ( C)

Time (s)

80

90

100

110

120

130

140

180 540 900 1260 1520

120 360 600 840 1080

60 120 180 240 300

40 80 120 160 200

30 60 90 120 150

20 40 60 80 100

10 20 30 40 50

Inactivation Kinetics of Enzyme Inactivity kinetics of a-amylase in TTI was calculated. The inactivation read from TTI should undergo the same thermal treatment (time–temperature profiles) as a target index (Hendrickx et al., 1992). Considering TTI system (X) of order n, the rate equation can generally be written as: dX=dt ¼ kx Xn

ð1Þ

where kx is the rate constant. When the TTI is subjected to an isothermal heat treatment, from integrating Equation (1), we obtain: f ðXÞ ¼ kx t

ð2Þ

where f(X) is the TTI response function; different reaction orders show different function as illustrated in Table 2. If the TTI is subjected to a variable time–temperature profile, Equation (1) can be integrated as: Z

t

kx dt

f ðXÞ ¼

ð3Þ

0

According to the Arrhenius equation, the temperature (T) dependence of the rate constant can be expressed as:    Eax 1 1  kx ¼ kxref exp R Tref T

ð4Þ

where Eax is the activation energy of the TTI, kxref is the rate constant of the TTI at a reference temperature (Tref) and R is the universal gas constant. The thermal death time (TDT) approach can also define the temperature sensitivity of kinetic parameters based on the assumption that the TDT (or D values) of microorganisms/nutrients follows a semi-logarithmic relationship with temperature as illustrated in Equation (5): log

Dx Txref ¼ z Dxref Tx

ð5Þ

Reaction order

TTI function [f(x)]

0 1 N (n 6¼ 1)

X0X Ln(X0/X) 1/[(n1)(X1nX01n )]

where Dx is the decimal reduction time at Tx, Dxref is the decimal reduction time at Txref and z presents the negative reciprocal slope of the D value curve. According to the Bigelow model (Ball and Olson, 1957), the rate constant can then be expressed in Equation (6): Kx ¼ 2:303=Dx

ð6Þ

Equation (7) can be derived from Equation (4): log

kx Ea Txref  Tx  ¼ kxref 2:303R Txref Tx

ð7Þ

From Equations (5)–(7), the relationship between Ea and z can be obtained as shown below: Ea ¼ 2:303R Tx Txref =z

ð8Þ

Feasibility of g-KGM Being Used as TTI Carrier in the Retort The time–temperature profiles of g-KGM during retort were determined by inserting a thermocouple into the geometrical center of g-KGM (HT 2). Two other thermocouples were placed in the retort to monitor the retort chamber temperature. Thermocouples were connected to the data collection system (CMC-92 Data Acquisition System, TR9216, Ellab, Denmark). An accuracy of 0.1  C was obtained for each thermocouple, by comparing with a standard mercury-in-glass thermometer in both ice water and boiling water, following the procedure by Hendrickx et al. (1992). Residual activities of a-amylase in the TTI after HT 4 were determined and compared with data calculated according to the kinetic model. The differences between the detected and calculated data can be used to investigate the feasibility of g-KGM as a carrier. Statistical Analysis The data were expressed as means of triplicate determinations. Statistical significance was assessed with one-way analysis of variance (ANOVA) using ORIGIN 7.5 (OriginLab Inc., USA) for Windows program. Treatment means were considered to be significantly different at p  0.05.

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Physicochemical Properties of g-KGM

2000

1500

1000

500

0 6

8

10 pH

12

14

Figure 2. Effect of pH of the gel preparation solution on the hardness of g-KGM. 95 90

Springness

85 80 75 70 65 60

6

8

10

12

14

pH

Figure 3. Effect of pH of the gel preparation solution on the springiness of g-KGM. 110

100

Water retention (%)

Hardness of g-KGM generally increased with increase in pH as shown in Figure 2, while the springiness of g-KGM increased at the beginning, but began to decrease when pH exceeded 12 (Figure 3). The greatest springiness was obtained at pH 12. Springiness is one of essential properties of a carrier, which should avoid deformation due to surroundings. Therefore, considering the properties of hardness and springiness, g-KGM was prepared at pH 12. One of the most important factors that influence heat transfer is water retention of the product. As shown in Figure 4, % water retention of g-KGM and g-SA decreased with increase in temperature (HT 1) and there was no significant difference in water retention between the two gels at all temperatures. Moisture loss during the heat treatment is due to flash evaporation resulting from higher vapor pressure in food than in its environment. The higher the temperature, the faster the evaporation of water. Water retention of g-SA was on average 3.05% higher than that of g-KGM when temperature was lower than 100  C. However, water retention of g-SA was average 3.35% lower than that of g-KGM when temperature was increased to higher than 100  C (Figure 4). In other words, the water retention ability of g-SA decreased more drastically compared to that of g-KGM as temperature increased, particularly above 100  C. This behavior indicates that g-KGM has water retention advantage over g-SA at the elevated temperature (100–140  C). Heat transfer properties are significantly influenced by water content (Rao and Rizvi, 1986) in the product. Less change in water retention property of g-KGM compared to g-SA during heat treatments, especially at the elevated temperatures, indicated that heat transfer property of g-KGM was more stable than g-SA at the high temperatures. From this thermal stability character, it can be suggested that g-KGM was more suitable than g-SA when used in ultrahigh temperature sterilization. Hardness of g-SA and g-KGM increased with the increase in temperature, and that of g-SA is significantly greater (p < 0.05) than that of g-KGM (Figure 5) at all temperatures (40–140  C). For g-KGM, acetyl played an important role in stabling helix structure. Deacetylation occurred under alkaline conditions, which destroy the helix chain of glucomannan and promote cross-linking with neighboring glucomannan chains, and a network structure was formed (Shanjun and Nishinari, 2004). High temperature can intensify the molecular vibration of glucomannan. This promotes the network structure formation and intensifies the hardness of g-KGM as temperature increases. As for g-SA, hardness of the g-SA significantly increased (p < 0.05) from 80  C,

Hardness (g)

RESULTS AND DISCUSSION

90

80

70

60 60

80 100 120 Temperature (°C)

140

Figure 4. Effect of temperature on the water retention of g-KGM (g) and g-SA (m). reaching the peak at 100  C, and significantly decreased (p < 0.05) after 100  C (Figure 5). Calcium ions react preferentially with the polygluronate segments of sodium alginate to form intermolecular and

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intramolecular cross-linking; intramolecular cross-linking mainly happened at low temperature. With proper heating of g-SA, molecule elongation and intermolecular cross-linking may happen, which can strengthen the hardness of g-SA (Zheng et al., 1996). However, high temperature could disrupt the polymerization of the alginate molecules, which led to the decrease in hardness (Leo et al., 1990). In contrast, hardness of the g-KGM increased gradually at all temperatures. There were no significant differences in the hardness of g-KGM between 100  C and 120  C. This also indicates that g-KGM is more stable than g-SA during ultrahigh temperature sterilization process. Springiness of g-KGM was higher than that of g-SA at all temperatures (Figure 6). One-way ANOVA showed that there was no significant difference in the springiness between g-KGM and g-SA at temperatures between 40 and 100  C. However, when temperature increased above 100  C, springiness of g-KGM became significantly higher (p < 0.05) than that of g-SA due to the drastic decrease in the springiness of g-SA at 120  C and 140  C. In other words, g-KGM tends to retain its springiness and g-SA does not. There was no significant difference in the springiness of g-KGM between 100  C and 140  C. This indicates that g-KGM is much more stable than g-SA during retort. All the data from Figures 4000 3500 Hardness (g)

3000 2500 2000 1500 1000 40

60

80 100 Temperature (°C)

12 0

140

Figure 5. Effect of temperature on the hardness of g-KGM (g) and g-SA (m). 100

Springness

90 80 70 60

40

60

80 100 Temperature (°C)

120

140

Figure 6. Effect of temperature on springiness of g-KGM (g) and g-SA (m).

4–6 demonstrate that g-KGM is more suitable for ultrahigh temperature sterilization than g-SA. The differences in those properties of water retention and texture between g-KGM and g-SA due to the heat treatments were probably due to the function of the chemical made up of these two gels as mentioned earlier. TTI with a-amylase as Indicator and g-KGM as Carrier a-Amylase was chosen as an indicator for its popularity of use in TTI as a bioindicator (Hendrickx et al., 1992; Van Loey and Arthawan, 1997; Tucker et al., 2002; Mehauden et al., 2007; Tucker et al., 2007). It is important to calculate the enzyme kinetic parameters as they determine the value of sterilization. D values were obtained by placing the TTI in the silicon oil bath at 80– 140  C for different lengths of time as shown in Table 1 and then cooled rapidly in cold water (HT 3). It took 5 s for the TTI to reach the temperature of the oil bath, so D values were determined under essentially isothermal conditions. The logarithm of the relative enzyme activity (ratio between the initial and final enzyme activity) for each time–temperature profile was calculated and plotted against time, so that regression equations can be achieved. D values were calculated from regression equations as presented in Table 3. D121.1 value obtained for the a-amylase was 3.32 min. This was within the range of average enzyme D values reported by Hallstrom (1988). Z value also can be used to evaluate thermal stability of the enzyme. Z value for a-amylase used in this study was calculated as 35.34  C from the regression equation of logarithm D value to temperature. In the literature, the range of Z values for the a-amylase from Bacillus amyloliquefaciens were between 7.6  C (Van Loey and Arthawan, 1997) and 12  C (Mehauden et al., 2007). Van Loey et al. (1996) discussed that various types of a-amylase from different sources showed different Z values. They also indicated that, in practice, calibration should be carried out on each batch of enzyme, rather than relying on the published Z values. Z values obtained in this experiment were somewhat higher than those obtained in their study. For the investigation on the heat transfer properties of g-KGM, time–temperature profiles of g-KGM were generated by HT 2 (Figure 7). Three different conditions were selected at random from Figure 7. Residual activities of a-amylase in the TTI after HT 4 were determined and the results are shown in Table 4. Then, the residual a-amylase activities were compared with the data generated by HT 4 to validate the results (Table 4), and there were no significant differences in the residual enzyme activities between calculated and experimental data obtained. This result was in agreement with the report by other researchers (Hendrickx et al., 1992; Mehauden et al., 2007) that TTI can give an accurate and sensitive

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Konjac Glucomannan as a Carrier Material

reading for the heat transfer test in industry. In our study, TTI was embedded in g-KGM, and the result implicated that g-KGM as a carrier of TTI is applicable for the heat transfer test in food industry, especially in ultrahigh temperature sterilization processing. It has been suggested (Van Loy et al., 1996) that three criteria should be met for a useful TTI: (1) it should be inexpensive, easily prepared and recovered; (2) heat transfer should be similar to real food; and (3) kinetics of TTI should be similar to the target attribute. KGM was obtained from konjac tuber, which is widely planted in the south of China (Yingqing et al., 2005), and can be incorporated into the food without affecting heat transfer as seen in this study. This study demonstrated that g-KGM would be a promising TTI material.

CONCLUSIONS TTI has been widely used in food industry for the heat transfer test, and a carrier system for TTI is needed for the encapsulation of TTI. Our study showed that g-KGM was more suitable as a carrier over g-SA for the aseptic processing test. g-KGM demonstrated greater stability in the textural properties and water retention than g-SA did after retort. TTI prepared with a-amylase as a bioindicator and g-KGM as a carrier generated the accurate measurement on the residual enzyme activity. This result was validated with the values of the residual enzyme activity calculated by the inactivity kinetics. Therefore, g-KGM can be used as a carrier in the aseptic processing test in industry. The results of this research provided valuable information for future heat transfer studies employing g-KGM as a carrier system.

Table 3. D values of a-amylase in different temperatures. Temperature ( C) 80 90 100 110 120 130 140

Regression equation

R2

D value (min)

y ¼ e0.04535x y ¼ e0.08702x y ¼ e0.167x y ¼ e0.03203x y ¼ e0.01106x y ¼ e0.02022x y ¼ e0.02837x

0.988 0.996 0.991 0.993 0.993 0.992 0.994

56.3 37.22 10.14 5.711 3.47 1.9 1.353

ACKNOWLEDGMENTS The authors are grateful to the research program of state key laboratory of Food Science and Technology, Jiangnan University (Project No. SKLF-MB-200804) for finanical support. We thank the Jiangsu Natural Science Foundation in ‘‘Climbing’’ Program (Grant No. BK2008003), the subject of Jiangsu science and technology support program (Grant No. BE2008317), and National Natural Science Foundation of China (Grant No. 20436020) for financial support.

160

Temperature (°C)

120

REFERENCES 80 Barigou M., Mankad S. and Fryer J.P. (1998). Heat transfer in two-phase solid-liquid food flows: a review. Food and Bioproducts Processing 76(1): 3–29. Ball C. and Olson F. (1957). Sterilization in food technology. New York: McGraw Hill, pp. 1–150. Guiavarch Y., Van L. and Hendrickx M. (2005). Enzyme based monitoring device for the thermal processing of objects. EP Patent 1520034. Guiavarch Yann P., Dintwa Edward., Van Loey Ann M., Zuber Francois T. and Hendrickx Marc E. (2002). Validation and use of an enzymic time–temperature integrator to monitor thermal

40

0 1

101

201 301 Time (s)

401

Figure 7. Time–temperature profiles of g-KGM.

Table 4. Calculated and measured residual enzyme activities of a-amylase in TTI embedded in g-KGM as a carrier after retort. Treatment* 

Calculated residual enzyme activity (U/mg) Measured residual enzyme activity (U/mg)

100 C for 300 s

110  C for 180 s

120  C for 120 s

11162 a 11153±85 a

10356 a 10360±56 a

8742 a 8748±66 a

Mean values followed by the same letter within a column are not significantly different (p  0.05). Downloaded from fst.sagepub.com at HINARI on February 22, 2011

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impacts inside a solid/liquid model food. Biotechnology Progress 18(5): 1087–1094. Hallstrom B. (1988). Heat transfer and foods products. London and New York: Elsevier Applied Science. Hendrickx M., Weng Z., Maesmans G. and Tobback P. (1992). Validation of a time–temperature-integrator for thermal processing of foods under pasteurization conditions. International Journal of Food Science and Technology 27: 21–31. Heppel N.J. (1985). Measurement of the liquid–solid heat transfer coefficient during continuous sterilization of liquid containing solids. In: 4th, Proceedings of International Congress on Engineering and Food Edmonton, Alberta, Canada. Jin W.H. (1991). Instrument for Physical Properties of Foods. Physical Properties of Foods. BeiJing: China Science and Technology Press, pp. 148–149. Leo W.J., McLoughlin A.J. and Malone D.M. (1990). Effects of sterilization treatments on some properties of alginate solutions and gels. Biotechnology Progress 6(1): 51–53. Li D. (2006). Study on the fluidization solid food ultra high temperature sterilization technology. PhD thesis, Jiangnan University, China. Mabit J., Belhamri R., Fayolle F. and Legrand J. (2008). Development of a time temperature integrator for quantification of thermal treatment in scraped surface heat exchangers. Innovative Food Science and Emerging Technologies 9(4): 516–526. Maesmans G. (1993). Possibilities and limitations of thermal process evaluation techniques based on time temperature integrators. PhD thesis, Leuven, Belgium. Martin A.K., Williams T.J.F., Dave. R. Martin and Norton T. (2000). A molecular description of the gelation mechanism of konjac mannan. Biomacromolecules 1(3): 440–450. Mehauden K., Cox P.W., Bakalis S., Simmons M.J.H., Tucker G.S. and Fryer P.J. (2007). A novel method to evaluate the applicability of time–temperature integrators to different temperature profiles. Innovative Food Science and Emerging Technologies 8(4): 507–514. Penroj P., Mitchell J.R., Hill S.E. and Ganjanagunchorn W. (2005). Effect of konjac glucomannan deacetylation on the properties of gels formed from mixtures of kappa carrageenan and konjac glucomannan. Carbohydrate Polymers 59(3): 367–376. Ramaswamy H.S. and Abdelrahim K.A. (1995). Residence time distribution (RTD) in aseptic processing of particulate foods: a review. Food Research International 28(3): 291–310. Roopa B.S. and Bhattacharya S. (2008). Alginate gels: I. Characterization of textural attributes. Journal of Food Engineering 85(1): 123–131.

Rao M.A. and Rizvi S.S.H. (1986). Thermal properties of foods. Engineering Properties of Foods. New York: Marcel Dekker Press. Shanjun G. and Nishinari K. (2004). Effect of deacetylation rate on gelation kinetics of konjac glucomannan. Colloids and Surfaces B: Biointerfaces 38(3): 241–249. Shen Y.Y. and Yang X.Q. (1995). Physicochemical properties of konjac and konjac food. Food Science China 16(6): 14–19. Taoukis P.S. and Labuza T.P. (1989). Reliability of time–temperature indicators as food quality monitors under nonisothermal conditions. Journal of Food Science 54: 789–792. Tucker G.S., Brown H.M., Fryer P.J., Cox P.W., Poole II F.L., Lee H.S. and Adams M.W.W. (2007). A sterilisation time-temperature integrator based on amylase from the hyperthermophilic organism Pyrococcus furiosus. Innovative Food Science and Emerging Technologies 8(1): 63–72. Tucker G.S., Lambourne T., Adams J.B. and Lach A. (2002). Application of a biochemical time–temperature integrator to estimate pasteurisation values in continuous food processes. Innovative Food Science and Emerging Technologies 3(2): 165–174. Van Loey A., Hendrickx M., DeCordt S., Haentyens T. and Tobback P. (1996). Quantitative evaluation of thermal process using time– temperature integrators. Trends in Food Science and Technology 7: 16–26. Van Loey A. and Arthawan A. (1997). The development and use of an a-amylase-based time–temperature integrator to evaluate in-pack pasteurization processes. Lebensmittel-Wissenschaft und-Technologie 30(1): 94–100. Weng Z., Hendrickx M., Maesmans G. and Tobback P. (1991). Immobilized peroxidase: a potential bioindicator for evaluation of thermal process. Journal of Food Science 56(2): 567–570. Xu X., Li B., Kennedy J.F., Xie B.J. and Huang M. (2007). Characterization of konjac glucomannan-gellan gum blend films and their suitability for release of nisin incorporated therein. Carbohydrate Polymers 70(2): 192–197. Yingqing Z., Bi-jun X. and Xin G. (2005). Advance in the applications of konjac glucomannan and its derivatives. Carbohydrate Polymers 60(1): 27–31. Zheng H.H., Dong L., Ning D.X., Zhang H.C. and Zhang Q.Z. (1996). Temperature effects on gel of konjac glucomannan. Chemistry World 48–49. Zhong G.Q., Sheng D.X., Teng J.X. and Chen Y.B. (2005). The newest research development of konjac food. Food Research and Development 26(1): 106–108.

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Food Science and Technology International http://fst.sagepub.com/

Thin Layer Modeling of Tom Yum Herbs in Vacuum Heat Pump Dryer A. Artnaseaw, S. Theerakulpisut and C. Benjapiyaporn Food Science and Technology International 2010 16: 135 originally published online 5 February 2010 DOI: 10.1177/1082013209353090 The online version of this article can be found at: http://fst.sagepub.com/content/16/2/135

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Thin Layer Modeling of Tom Yum Herbs in Vacuum Heat Pump Dryer A. Artnaseaw,* S. Theerakulpisut and C. Benjapiyaporn Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, 40002 Thailand Thin layer vacuum heat pump drying experiments were conducted to determine drying models for Tom Yum herbs (chili, lemon grass, kaffir lime leaf and galangal slice). The drying experiments were conducted in a vacuum heat pump dryer at a constant drying pressure of 0.2 bars and drying temperatures ranging from 50  C to 65  C. The experimental results were fitted to a number of well-known thin layer drying models and it was found, for the range of drying temperature tested, that the Midilli model is the best model for all Tom Yum herbs. To account for the influence of drying temperature, the constants and coefficients of model were formulated as functions of the drying temperature. Statistical tests of agreement between the model and experimental results were performed by determining the coefficient of determination (R2), reduced chi-square (2) and root mean square error (RMSE). It was found that the model is in very good agreement with the experimental results. Key Words: Tom Yum herbs, thin layer model, vacuum heat pump dryer

INTRODUCTION Tom Yum is a popular dish and well known among foreigners who have experience with Thai food. Tom Yum is characterized by its distinct hot and sour flavors, with fragrant herbs generously used. Savory complements of various kinds of Thai herbs such as galangal, lemon grass, kaffir lime leaves, chili and coconut cream are combined to enhance its flavoring appeal. Tom Yum is not only delicious but also nutritious. The dish contains proteins from chicken, fish, shrimp or mixed seafood and fiber from other Thai herbs and other ingredients of nutritional value. Thai herbs are effective in inhibiting tumors in the digestive tract (Division of Health Statistics, 1989; Murakami et al., 1994). In addition, other ingredients of Tom Yum include chili, shallot and garlic which are natural antimicrobial, antioxidant compounds with health benefits. Moreover, Tom Yom herbs have generally been found to be a great antibacterial, antidiabetic, hypocholesterolemic and cancer preventive agent (Nishimura et al., 2000). The savory scent of Tom Yum is often used in aroma therapy for its relaxing and curing effects in the treatment of some *To whom correspondence should be sent (e-mail: aapich@kku.ac.th). Received 4 February 2009; revised 10 March 2009. Food Sci Tech Int 2010;16(2):0135Â&#x2014;12 Ă&#x; SAGE Publications 2010 Los Angeles, London, New Delhi and Singapore ISSN: 1082-0132 DOI: 10.1177/1082013209353090

diseases. For Tom Yum herbs drying, it is important to preserve the color and nutrients of the dried product. Often good quality of a biological product implies a product of desired specifications after it undergoes several necessary physical, chemical and biological changes. Hot air dryers, such as solar dryers, have been developed to dry Tom Yum herbs but they require fairly constant sunshine. Cloudy weather increases drying time and risk of spoilage. Uncontrolled high drying temperature causes damage to the flavor, color and nutrients of the dried Tom Yum herbs. Vacuum drying is a method of drying with high drying rate because the boiling point of water in the product, held under vacuum, is lower than that at atmospheric pressure and water vapor is removed by a vacuum pump (Holkeboer and David, 1967). Characteristics of this drying technique such as high drying rate, low drying temperature and oxygen deficient drying environment may help to maintain the qualities such as shape, color, aroma and flavor and nutritive values of the dried product (Alibas, 2007). Energy saving is also an increasingly important advantage of this drying technique (Zhangjing and Fred, 2001). There are several heat sources that can be used for vacuum dryer, namely, electrical heater, microwave, steam and heat pump. However, each heat source has certain advantages and disadvantages. For example, microwave drying has the disadvantages of nonhomogeneous distribution in the processing cavity, creating problems of nonuniform heating (Abe and Afzal, 1997). The application of heat pump in vacuum drying has

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received significant attention due to three main advantages. First, vacuum heat pump drying gives lower specific moisture extraction rate than other conventional hot air drying (Soponronnarit et al., 2000). Second, it is suitable for temperature-sensitive products as drying can take place at low temperature (Vazquez et al., 1997). Third, cooling effect of the heat pump can be used for other purposes such as air conditioning in residential and commercial buildings. When the drying atmosphere is a partial vacuum, drying can readily take place at a lower temperature. The advantages of a heat pump for drying can be combined with the advantages of drying under vacuum in a vacuum heat pump dryer. Even though vacuum heat pump drying offers many advantages, the use of vacuum heat pump drying is rather limited. Literature review on vacuum heat pump drying reveals only some research work by Hawlader et al., (2006), Perera (2001) and Wu et al., (2007). Simulation models are needed in the design and operation of vacuum heat pump dryers. Furthermore, simulation models are also useful in improving the existing drying system. Our literature review has not revealed any work on modeling of Tom Yum herbs drying under vacuum with a heat pump dryer. Therefore, the objective of this research is mainly concerned with the development of a mathematical model of the thin layer vacuum heat pump drying for Tom Yum herbs. Chili, lemon grass, kaffir lime leaf and galangal slice are chosen as the herbs of concern because they are herb ingredients that dominate flavors of Tom Yum.

MATERIALS AND METHODS Vacuum Heat Pump Dryer A prototype of vacuum heat pump dryer was designed, built and installed at the Department of Chemical Engineering, Khon Kaen University, Thailand. A pictorial view and a schematic diagram of the vacuum heat pump dryer are shown in Figures 1 and 2, respectively. It consists of an insulated cylindrical drying chamber made of steel sheet with a diameter of 1 m and a length of 1.2 m, a 5.2 kW heat pump to supply heat to the drying chamber, a 1.2 kW liquid ring vacuum pump to control the drying pressure in the drying chamber, a 100 W service liquid pump to circulate cooling water to the vacuum pump, a 100 W cooling water pump to circulate chilled water to the dehumidifier for reducing humidity in the drying chamber, a 0.2  0.2  0.3 m3 condensate collector to collect condensed water from the dehumidifier and a 1  2  0.3 m3 cooling water tank to supply the cooling water produced by the evaporator of the heat pump, to the dehumidifier and the vacuum pump.

Figure 1. The vacuum heat pump dryer.

Figure 2. A schematic diagram of the vacuum heat pump dryer: (1) internal condenser, (2) receiver, (3) filter dryer, (4) expansion valve, (5) evaporator, (6) compressor, (7) solenoid valve, (8) external condenser, (9) cooling water tank, (10) dehumidifier, (11) vacuum pump, (12) blower, (13) cooling water pump, (14) load cell, (15) sample tray, (16) condensate collector, (17) valves, (18) service liquid pump, (19) drying chamber and (20) insulator.

To maintain the temperature in the drying chamber, the heat pump was run continuously. The drying temperature was controlled by a solenoid valve controlling the flow of hot refrigerant to the internal condenser of the heat pump. The solenoid valve would open and close when the drying temperature, measured by a type K thermocouple at 5 cm above the herbs tray, reached a higher and lower set values, respectively. The difference between the lower and higher set values was 2  C. The continuous running of the heat pump was also important for maintaining the temperature of cooling water in the cooling water tank to be in the range of 15Â&#x2014;17  C to prevent damage to the vacuum pump. Measured by a digital humidity meter with an accuracy of Âą2% RH (50Â&#x2014;65  C), humidity in the drying chamber was controlled by the cooling water pump. The cooling water

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pump would operate to supply the cooling water to the dehumidifier when the humidity in the drying chamber reached a set value. The change of mass of the herbs during drying was measured continually using a load cell (20 kg maximum) with an accuracy of ±0.01 kg. The drying temperatures within the drying chamber were monitored by type-K thermocouples calibrated with a GRANT water bath with an accuracy of ±0.5  C (5—90  C) and connected to the CAMBELL model CX-23 data logger. A digital pressure meter with an accuracy of ±0.5% of full scale was used to measure pressure in the drying chamber. Water flow rate was measured by a panel mount flow meter with an accuracy of ±5% of full scale. A hot wire anemometer with an accuracy of ±2% of full scale was used to measure the drying medium velocity in the drying chamber. Working fluid in the heat pump was R-22.

water pump would operate to reduce humidity when the humidity in the drying chamber reached 40% RH. The drying test was stopped when the moisture content of herbs was constant. The experiment was repeated three times for each test to obtain the average values reported in this article.

Mathematical Model of Vacuum Heat Pump Drying Curves and Formulation The moisture ratio was simplified to M(t)/Min instead of (M(t)—Me)/(Min—Me) because relative humidity of the drying medium continuously fluctuated in vacuum heat pump drying and therefore Me is difficult to predict under those circumstances (McMinn, 2006). The moisture ratio is defined in this study as:

Vacuum Heat Pump Drying Experiments Tom Yum herbs (chili, lemon grass, kaffir lime leaf and galangal slice) of commonly grown varieties in the Northeast of Thailand were used as the test material in this study. Fresh herbs were directly obtained from farmers with typical initial moisture content of 320%, 300%, 190% and 230% dry basis for chili, lemon grass, kaffir lime leaf and galangal slice, respectively. Herbs were not treated with any chemicals before conducting the experiment. Sticks of lemon grass were cut into pieces, approximately 15 cm long, and then the outer layers were peeled away until the middle diameters were approximately 12 mm. The galangal was cut into 3 mm thick slices by a slicing machine. The average diameter of chili was 10 mm. The average thickness of kaffir lime leaf was 0.3 mm. Initial moisture contents of the tested herbs were determined by the oven drying method (AOAC, 1984). Generally, herbs of relatively uniform size were used in the tests. Approximately 10, 12, 5 and 15 kg of chili, lemon grass, kaffir lime leaf and galangal slice were respectively used in each experiment. The herbs were cleaned in 25—28  C running water for approximately 4 min. The herbs were then spread on a plastic net to remove excess water. After removing excess water, the herbs were weighed and spread out over a tray in a single layer in the vacuum drying chamber. Before each experiment, the equipment was allowed to run for 30 min to obtain steady drying temperature. Weight loss of the drying herbs was measured at various time intervals, ranging from 10 min during the early stage of drying to 30 min during the final stage of drying. The experiment was carried out at the drying temperatures of 50—65  C. The pressure in the drying chamber was maintained at 0.2 bars. The drying medium velocity in the drying chamber was controlled at 1.2 m/s by controlling the blower speed. Additionally, the range of humidity in the drying chamber was 10—40% RH. The cooling

MR ¼

MðtÞ Min

ð1Þ

where MR is the moisture ratio, Min is the initial moisture content (kg/kg, dry basis) and M(t) is the moisture content at time t (kg/kg, dry basis). The vacuum heat pump drying data obtained were fitted to nine different moisture ratio equations detailed in Table 1 (Akal et al., 2007) using the nonlinear least squares regression analysis. Statistical analyses of the experimental data were performed by using the software package, Statistica Version 5. Three criteria were adopted to evaluate the goodness of fit of each model, namely, the coefficient of determination (R2), the reduced chi-square (2) and the root mean square error (RMSE). The 2 and RMSE can be described in equation form as: 2 ¼

N  2 1 X MRexp,i  MRpred,i N  n i¼1

"

N  2 1X RMSE ¼ MRexp,i  MRpred,i N i¼1

ð2Þ

#0:5 ð3Þ

Table 1. Thin layer models applied to the drying curves. Name of model Lewis Page Modified page Henderson and Pabis Logarithmic Two-term model Approximation of diffusion Wang and Singh Midilli

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Model MR ¼ exp(kt) MR ¼ exp(ktn) MR ¼ exp[(kt)n] MR ¼ a exp(kt) MR ¼ a exp(kt) þ c MR ¼ a exp(k0t) þ b exp(k1t) MR ¼ a exp(kt) þ (1  a)exp(kbt) MR ¼ 1 þ at þ bt2 MR ¼ a exp(ktn) þ bt

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A. ARTNASEAW ET AL.

where MRexp,i is the experimental moisture ratio, MRpred,i is the predicted moisture ratio, N is the number of experimental data points and n is the number of parameters in model (Demir et al., 2007). The lower value of reduced chi-square (2), the higher value of coefficient of determination (R2) and the lower value of root mean square error (RMSE) indicate better fitting. The relationship between the constants and coefficients of the best model with drying temperature were determined by the regression technique using several models. The different types of models used were linear, power, exponential, Arrhenius, logarithmic and polynomial (Ertekin and Yaldiz, 2004; Togrul, 2006). Linear

Y ¼ a þ bX

ð4Þ

Y ¼ aXb

ð5Þ

Y ¼ a expðbXÞ

ð6Þ

Y ¼ a expðb=XÞ

ð7Þ

Power Exponential Arrhenius Logarithmic

Y ¼ a þ b lnðXÞ   Inverse polynomial Y ¼ 1= a þ bX þ cX2 Polynomial

Y ¼ a þ bX þ cX2

a slope of: Slope ¼

2 D : 4L2

ð13Þ

For chili and lemon grass, a cylinder model for the diffusion equation is given by the following equation (Kaleemullah and Kailappan, 2006):  2  4 1 D t MR ¼ 2 exp r2 1

ð14Þ

where D is the diffusion coefficient (m2/s), MR is the moisture ratio, r the radius of the cylinder (m), 1 is the root of the Bessel function and t is the drying time (s). Then, Equation (14) can be written in logarithmic form as: ln MR ¼ ln

4 21 D  2 t r 21

ð15Þ

ð8Þ ð9Þ ð10Þ

The diffusion coefficient is typically calculated by plotting experimental drying data in terms of ln MR versus drying time. From Equation (15), a plot of ln MR versus the drying time gives a straight line with a slope of:

Diffusion Coefficient Fick’s diffusion equation as given below is used to determine the diffusion coefficient of samples at different drying temperatures. For kaffir lime leaf and galangal slice, an infinite slab model for the diffusion equation is given by the following equation (Ramesh et al., 2001):

Slope ¼

21 D r2

ð16Þ

RESULTS AND DISCUSSION Drying Characteristics of Tom Yom Herbs

  8 1 ð2n þ 1Þ2 2 D exp  t MR ¼ 2  n¼1 ð2n þ 1Þ2 4L2 1 X

ð11Þ

where MR is the moisture ratio, D is the diffusion coefficient (m2/s), L is the half-thickness of slab thickness (m) and t is the drying time (s). For long drying time, a limiting form of Equation (11) is obtained for slab geometries by considering only the first term in their series expansion. Then, Equation (11) can be written in logarithmic form as: ln MR ¼ ln

8 2 D  t: 2 4L2

ð12Þ

The diffusion coefficient is typically calculated by plotting experimental drying data in terms of ln MR versus drying time. From Equation (12), a plot of ln MR versus the drying time gives a straight line with

Figure 3 presents the changes in the moisture ratio versus drying time of chili, lemon grass, kaffir lime leaf and galangal slice under different drying temperatures. As expected, experimental results showed that drying time decreased with an increase in drying temperature from 50  C to 65  C. This is because higher drying temperature results in a larger driving force for heat transfer, which is also related to the rate of mass transfer. From this figure, it can be seen that the shortest drying time for chili, lemon grass, kaffir lime leaf and galangal slice are approximately 8, 13, 4 and 5 h, respectively. The drying time should be compared with the reported values of 20 h to dry chillies from 285% to 5% dry basis at 55  C air temperature using a solar tunnel dryer (Hossain and Bala, 2007), 13 h from 325% to 10.5% dry basis at 65  C air temperature (Kaleemullah and Kailappan, 2006) and 26 h from 300% to 8—9% dry basis at 45  C air temperature

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Thin Layer Modeling of Tom Yum Herbs in Vacuum Heat Pump Dryer Kaffir lime leaf 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

Moisture ratio

Moisture ratio

Chili 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0

2

4

6

8 10 12 14 16 18 20 22 24

0

1

2

3

4

6

7

Galangal slice

Lemon grass

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

5

Time (h)

Moisture ratio

Moisture ratio

Time (h)

0 2 4 6 8 10 12 14 16 18 20 22 24 26

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Time (h)

Time (h)

Figure 3. Drying kinetics of Tom Yum herbs. Temperature: (f) 50  C, () 55  C, (n) 60  C, (i) 65  C.

(Mangaraj et al., 2001). Vacuum drying gives lower drying time than atmospheric drying because lower drying pressure increases the driving force for mass transfer and facilitates evaporation of water from the materials, and thus greatly shortens the drying time. A marked decline was observed in the drying time of herbs with increasing temperature level. When the drying process at the drying temperature of 55  C was compared with the drying processes at the drying temperatures of 60  C and 65  C, the drying time was shortened by 7.5 and 9.5 h for chili, 3.5 and 4 h for lemon grass, 0.8 and 0.85 h for kaffir lime leaf and 3 and 4 h for galangal slice, respectively. The influence of drying temperature is very evident when the drying temperature reaches 60  C. This is because moisture is vaporized in the materials during drying as the boiling temperature is lowered by vacuum. Boiling point of water is about temperature of 60  C at the pressure of 0.2 bars. This phenomenon was markedly observed in the case of drying chili and galangal slice (Figure 3). In practice, when the drying temperature is higher than 65  C, the pressure of the compressor of the heat pump becomes very high and can cause damage to the heat pump. Therefore, with a view of operation efficiency and the drying time, the drying temperature should not exceed 65  C. Mechanism of internal mass transfer was the dominant physical mechanism governing moisture movement in the materials. The internal mass transfer was therefore by liquid diffusion or vapor diffusion or by

capillary forces in the interior region of materials and the water was evaporated as it reached the surface. At the drying temperatures approaching and exceeding the boiling point of water (60  C at 0.2 bars), the most probable mechanism within all mechanisms governing moisture transfer was that of liquid and vapor diffusion that most moisture was transferred in vapor and liquid form from the center to the surface of materials, while at the drying temperature below the boiling point of water, the most probable mechanism was that of liquid diffusion. The experimental data indicated that there was no constant rate period of drying and all the drying process was seen to occur in the falling rate period because of the thin layer arrangement and rapid internal liquid or vapor migration that is established as a direct result of an increased internal vapor pressure gradient that drives the moisture efficiently from materials that lead to the quick moisture removal from surface of the materials. During the early period of drying, the drying rates of chili, kaffir lime leaf and galangal slice were greater than lemon grass. This is because lemon grass has several tough outer layers of leaves, the significance of internal resistance to mass transfer in the materials. One other point is the long time that was necessary for kaffir lime leaf to dry. Although, its thickness was just 0.3 mm and it was spread out in a single layer, the drying time of kaffir lime leaf exceeded 4 h. This indicates that water loss was very slow. This may be due the intrinsic property of kaffir lime leaf, which resists against the

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Table 2. Empirical constants and statistical results for chili. Model

T ( C)

Constant

R2

2

RMSE

Lewis

50 55 60 65 50 55 60 65 50 55 60 65 50 55 60 65 50 55 60 65 50

k ¼ 0.11639 k ¼ 0.14591 k ¼ 0.28039 k ¼ 0.33663 k ¼ 0.07578, n ¼ 1.17671 k ¼ 0.10236, n ¼ 1.19849 k ¼ 0.18234, n ¼ 1.17671 k ¼ 0.28636, n ¼ 1.12767 k ¼ 0.64657, n ¼ 0.18003 k ¼ 1.15101, n ¼ 0.12677 k ¼ 1.19426, n ¼ 0.23478 k ¼ 2.01475, n ¼ 0.16708 k ¼ 0.12241, a ¼ 1.04558 k ¼ 0.15276, a ¼ 1.04389 k ¼ 0.30130, a ¼ 1.07519 k ¼ 0.34671, a ¼ 1.03028 k ¼ 0.08103, a ¼ 1.24117, c ¼ 0.23661 k ¼ 0.10764, a ¼ 1.18281, c ¼ 0.17918 k ¼ 0.22123, a ¼ 1.18966, c ¼ 0.15097 k ¼ 0.27422, a ¼ 1.09970, c ¼ 0.9914 k0 ¼ 0.12242, k1 ¼ 0.12239, a ¼ 0.64669, b ¼ 0.39888 k0 ¼ 0.15276, k1 ¼ 0.15277, a ¼ 0.52344, b ¼ 0.52044 k0 ¼ 0.30131, k1 ¼ 0.30126, a ¼ 0.81431, b ¼ 0.26087 k0 ¼ 0.27793, k1 ¼ 0.01550, a ¼ 1.08416, b ¼ 0.08314 k ¼ 0.20008, a ¼ 142.00623, b ¼ 0.99569 k ¼ 0.24088, a ¼ 181.42267, b ¼ 0.99684 k ¼ 0.52321, a ¼ 162.81131, b ¼ 0.99527 k ¼ 0.51242, a ¼ 146.98820, b ¼ 0.99674 a ¼ 0.09190, b ¼ 0.00227 a ¼ 0.11287, b ¼ 0.00340 a ¼ 0.21236, b ¼ 0.01177 a ¼ 0.24777, b ¼ 0.01594 k ¼ 0.07077, a ¼ 0.97823, n ¼ 1.17978, b ¼ 0.00267 k ¼ 0.10994, a ¼ 0.99358, n ¼ 1.06882, b ¼ 0.00518 k ¼ 0.17488, a ¼ 0.98934, n ¼ 1.33004, b ¼ 0.00013 k ¼ 0.29994, a ¼ 1.00399, n ¼ 0.99492, b ¼ 0.00896

0.9925 0.9936 0.9903 0.9942 0.9978 0.9970 0.9987 0.9953 0.9925 0.9936 0.9903 0.9942 0.9905 0.9916 0.9868 0.9930 0.9983 0.9988 0.9947 0.9979 0.9905

0.0010589 0.0009965 0.0021970 0.0008690 0.0001930 0.0002695 0.0001443 0.0004862 0.0010910 0.0010297 0.0023262 0.0009234 0.0008151 0.0007897 0.0075677 0.0008669 0.0001419 0.0001022 0.0005954 0.0002184 0.0008677

0.00550 0.03107 0.01075 0.02865 0.00235 0.01592 0.00276 0.02079 0.00558 0.03107 0.01106 0.02865 0.00483 0.02721 0.00968 0.02678 0.00201 0.00962 0.00560 0.01349 0.00498

0.9916

0.0008461

0.02721

0.9868

0.0017767

0.00967

0.9979

0.0002339

0.01349

0.9982

0.0001472

0.01193

0.9974

0.0002310

0.01447

0.9986

0.0001540

0.01139

0.9954

0.0004796

0.02738

0.9991 0.9988 0.9979 0.9944 0.9989

0.0007710 0.0001284 0.0002669 0.0007188 0.0002349

0.00142 0.01079 0.00379 0.02528 0.00165

0.9989

0.0001003

0.00953

0.9987

0.0001513

0.00282

0.9979

0.0002349

0.01352

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Logarithmic

Two-term model

55 60 65 Approximation of diffusion

50 55 60 65

Wang and Singh

Midilli

50 55 60 65 50 55 60 65

moisture movement from the inside to the surface of kaffir lime leaf. Mathematical Model of Vacuum Heat Pump Drying Curves Curve-fitting of the experiment data was performed for nine well-known semi-empirical models. The statistical results from these models such as coefficient of determination (R2), reduced chi-square (2) and RMSE are given in Tables 2—5. From the statistical analysis, it

is revealed that the Midilli model as given by Equation (17), with R2 greater than the acceptable R2-value of 0.93 in all the cases (Madamba et al., 1996), yielded the highest average R2-value, the lowest average 2 value and the lowest average RMSE value. Hence, the Midilli model gave better predictions than the others. The results were consistent with observations during air drying of rough rice made by Akal et al. (2007) who reported that the Midilli model fits better to the experimental data because of the high number of coefficients and the form of the model equation modified

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Table 3. Empirical constants and statistical results for lemon grass. Model

T ( C)

Lewis

50 55 60 65 50 55 60 65 50 55 60 65 50 55 60 65 50 55 60 65 50

Page

Modified page

Henderson and Pabis

Logarithmic

Two-term model

Constant k ¼ 0.0851 k ¼ 0.1204 k ¼ 0.1515 k ¼ 0.1646 k ¼ 0.0278, n ¼ 1.4486 k ¼ 0.0503, n ¼ 1.4058 k ¼ 0.0726, n ¼ 1.3778 k ¼ 0.0838, n ¼ 1.3620 k ¼ 0.2918, n ¼ 0.2918 k ¼ 0.3470, n ¼ 0.3470 k ¼ 0.3892, n ¼ 0.3894 k ¼ 0.4057, n ¼ 0.4057 k ¼ 0.0961, a ¼ 1.1195 k ¼ 0.1341, a ¼ 1.1052 k ¼ 0.1681, a ¼ 1.1041 k ¼ 0.1822, a ¼ 1.1009 k ¼ 0.0415, a ¼ 1.6513, c ¼ 0.6179 k ¼ 0.0613, a ¼ 1.5757, c ¼ 0.5515 k ¼ 0.0859, a ¼ 1.4558, c ¼ 0.4283 k ¼ 0.0963, a ¼ 1.4274, c ¼ 0.3998 k0 ¼ 0.0961, k1 ¼ 0.0961, a ¼ 0.5637, b ¼ 0.5558 k0 ¼ 0.1341, k1 ¼ 0.1341, a ¼ 0.4121, b ¼ 0.6931 k0 ¼ 0.1681, k1 ¼ 0.1681, a ¼ 0.3021, b ¼ 0.8020 k0 ¼ 0.1822, k1 ¼ 0.1822, a ¼ 0.5513, b ¼ 0.5496 k ¼ 0.1758, a ¼ 26.8566, b ¼ 0.9680 k ¼ 0.2428, a ¼ 23.5996, b ¼ 0.9654 k ¼ 0.2981, a ¼ 27.9232, b ¼ 0.9715 k ¼ 0.3229, a ¼ 20.9463, b ¼ 0.9629 a ¼ 0.0599, b ¼ 0.0008 a ¼ 0.0863, b ¼ 0.0017 a ¼ 0.1096, b ¼ 0.0028 a ¼ 0.1199, b ¼ 0.0034 k ¼ 0.0354, a ¼ 0.9971, n ¼ 1.2471, b ¼ 0.0062 k ¼ 0.0548, a ¼ 0.9869, n ¼ 1.2520, b ¼ 0.0076 k ¼ 0.0821, a ¼ 0.9954, n ¼ 1.1991, b ¼ 0.0089 k ¼ 0.0931, a ¼ 0.9962, n ¼ 1.1926, b ¼ 0.0091

55 60 65 Approximation of diffusion

Wang and Singh

Midilli

50 55 60 65 50 55 60 65 50 55 60 65

from the simplified version of the solution of diffusion equation: MR ¼ a expðktn Þ þ bt:

2

RMSE

0.9526 0.9585 0.9626 0.9654 0.9955 0.9955 0.9955 0.9963 0.9526 0.9585 0.9626 0.9654 0.9708 0.9730 0.9762 0.9783 0.9991 0.9990 0.9991 0.9994 0.9708

0.0043990 0.0035863 0.0032763 0.0030005 0.0004370 0.0003914 0.0003980 0.0032599 0.0044140 0.0036040 0.0032955 0.0030197 0.0027420 0.0023431 0.0021005 0.0018922 0.0000990 0.0000907 0.0000820 0.0000560 0.0027610

0.06622 0.05974 0.05707 0.05460 0.02084 0.01969 0.01983 0.01794 0.06621 0.05974 0.05707 0.05460 0.05219 0.04817 0.04556 0.04322 0.00988 0.00945 0.00898 0.00741 0.05219

0.9730

0.0023664

0.04817

0.9762

0.0021256

0.04556

0.9783

0.0019168

0.04322

0.9934 0.9941 0.9943 0.9954 0.9986 0.9990 0.9991 0.9994 0.9998

0.0006410 0.0005126 0.0004975 0.0004023 0.0002920 0.0001226 0.0000753 0.0000541 0.0000170

0.02518 0.02247 0.02211 0.01987 0.01702 0.01102 0.00862 0.00731 0.00406

0.9998

0.0000170

0.00408

0.9996

0.0000322

0.00563

0.9999

0.0000075

0.00271

a ¼ 1:61325 exp ½160:922=T

ð19Þ

n ¼ 241:69368 þ 1:47580T  0:0022415T 2

ð20Þ

b ¼ 6:81426 þ 0:04149T  0:0000632T 2

ð21Þ

ð17Þ

To account for the effect of the drying temperature on the constants and coefficients of Midilli model, the values of constants and coefficients were formulated as functions of the drying temperature using regression analysis. The relationships between the constants and coefficients of the model and the drying temperature are given as follows. For chili: k ¼ 88:98812  0:55272T þ 0:0008589T 2

R2

ð18Þ

For lemon grass: k ¼ ð10,127:30699  60:01247T þ 0:08899T 2 Þ1 a ¼ 12:96747  0:072594T þ 0:00011T 2

ð22Þ ð23Þ

n ¼ ð10:28699  0:060234T þ 0:0000955377T 2 Þ1 ð24Þ b ¼ ð32,421:47817 þ 191:9748T  0:28514T 2 Þ1 ð25Þ

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Table 4. Empirical constants and statistical results for kaffir lime leaf. Model

T ( C)

Constant

R2

2

RMSE

Lewis

50 55 60 65 50 55 60 65 50 55 60 65 50 55 60 65 50 55 60 65 50

k ¼ 0.39464 k ¼ 0.64219 k ¼ 0.90553 k ¼ 1.04820 k ¼ 0.39718, n ¼ 0.99390 k ¼ 0.60905, n ¼ 1.08438 k ¼ 0.91518, n ¼ 0.96309 k ¼ 1.06044, n ¼ 0.92490 k ¼ 0.28492, n ¼ 1.38509 k ¼ 1.64961, n ¼ 0.38930 k ¼ 3.46398, n ¼ 0.26141 k ¼ 3.57782, n ¼ 0.29297 k ¼ 0.38577, a ¼ 0.97930 k ¼ 0.64460, a ¼ 1.00372 k ¼ 0.88666, a ¼ 0.97995 k ¼ 1.01497, a ¼ 0.96971 k ¼ 0.30224, a ¼ 1.05526, c ¼ 0.10701 k ¼ 0.49177, a ¼ 1.07750, c ¼ 0.11218 k ¼ 0.87846, a ¼ 0.98153, c ¼ 0.00311 k ¼ 1.07428, a ¼ 0.96248, c ¼ 0.01727 k0 ¼ 0.38576, k1 ¼ 0.38579, a ¼ 0.49140, b ¼ 0.48790 k0 ¼ 0.64460, k1 ¼ 0.64461, a ¼ 0.47128, b ¼ 0.53245 k0 ¼ 0.88364, k1 ¼ 0.88553, a ¼ 0.04163, b ¼ 1.02586 k0 ¼ 1.01503, k1 ¼ 1.01510, a ¼ 1.75924, b ¼ 0.78953 k ¼ 0.47595, a ¼ 15.04873, b ¼ 0.98798 k ¼ 0.64219, a ¼ 0.27830, b ¼ 1 k ¼ 6.66810, a ¼ 0.04124, b ¼ 0.13030 k ¼ 0.93853, a ¼ 0.87730, b ¼ 3.79347 a ¼ 0.30802, b ¼ 0.02604 a ¼ 0.47829, b ¼ 0.05998 a ¼ 0.67370, b ¼ 0.12009 a ¼ 0.75198, b ¼ 0.14701 k ¼ 0.38720, a ¼ 1.00851, n ¼ 0.76989, b ¼ 0.02739 k ¼ 0.53588, a ¼ 0.96161, n ¼ 1.03004, b ¼ 0.01592 k ¼ 0.89108, a ¼ 0.99837, n ¼ 0.92975, b ¼ 0.00613 k ¼ 1.05774, a ¼ 1.00590, n ¼ 0.90592, b ¼ 0.00215

0.9932 0.9892 0.9974 0.9976 0.9932 0.9906 0.9975 0.9983 0.9932 0.9892 0.9974 0.9976 0.9939 0.9890 0.9975 0.9973 0.9975 0.9952 0.9975 0.9977 0.9939

0.0004748 0.0009500 0.0002100 0.0002700 0.0004802 0.0007900 0.0001800 0.0001200 0.0004813 0.0009600 0.0002200 0.0002800 0.0004400 0.0009600 0.0001800 0.0002000 0.0001700 0.0003800 0.0001800 0.0001700 0.0004500

0.02165 0.03046 0.01439 0.01622 0.02162 0.02752 0.01306 0.01053 0.02165 0.03046 0.01439 0.01622 0.02060 0.03044 0.01310 0.01373 0.01286 0.01899 0.01307 0.01243 0.02060

0.9890

0.0010000

0.03044

0.9975

0.0001900

0.01310

0.9973

0.0002100

0.01373

0.9931

0.0004727

0.02131

0.9876 0.9978 0.9985 0.9897 0.9945 0.9864 0.9779 0.9997

0.0009629 0.0001588 0.0001056 0.0013000 0.0008400 0.0018100 0.0028900 0.0000167

0.03046 0.01230 0.01002 0.03557 0.02839 0.04150 0.05240 0.00431

0.9945

0.0004100

0.01954

0.9979

0.0001600

0.01211

0.9984

0.0001200

0.01038

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Two-term model

55 60 65 Approximation of diffusion

Wang and Singh

Midilli

50 55 60 65 50 55 60 65 50 55 60 65

For kaffir lime leaf: k ¼ ð953:35516  5:63772T þ 0:008343T 2 Þ1 a ¼ 60:2394  0:3592T þ 0:0005443T 2

ð26Þ ð27Þ

n ¼ 311:22627 þ 1:88324T  0:0028397T 2

ð28Þ

b ¼ 8:75712 þ 0:051219T  0:000074899T 2

ð29Þ

For galangal slice: k ¼ ð302:85774  1:56343T þ 0:0019910T 2 Þ1

ð30Þ

a ¼ ð55:4552 þ 0:341886T  0:00051738T 2 Þ1

ð31Þ

n ¼ 414:48566 þ 2:520226T  0:0038188T 2

ð32Þ

b ¼ ð10,001:61966  168:88403T þ 0:58579T 2 Þ1 ð33Þ where T is the drying temperature (K). Validation of the Midilli model was evaluated by comparing the computed moisture content at different drying temperatures with the observed moisture contents. The performance of the model at the different drying temperatures was illustrated in Figure 4.

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Table 5. Empirical constants and statistical results for galangal. Model

T ( C)

Lewis

50 55 60 65 50 55 60 65 50 55 60 65 50 55 60 65 50 55 60 65 50

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Logarithmic

Two-term model

Constant k ¼ 0.2327 k ¼ 0.3213 k ¼ 0.4844 k ¼ 0.5689 k ¼ 0.1642, n ¼ 1.2141 k ¼ 0.2485, n ¼ 1.1949 k ¼ 0.3664, n ¼ 1.3074 k ¼ 0.5221, n ¼ 1.1136 k ¼ 0.0902, n ¼ 2.5812 k ¼ 0.1259, n ¼ 2.5515 k ¼ 0.1922, n ¼ 2.5208 k ¼ 0.2202, n ¼ 2.5830 k ¼ 0.2506, a ¼ 1.0788 k ¼ 0.3435, a ¼ 1.0714 k ¼ 0.5243, a ¼ 1.0882 k ¼ 0.5932, a ¼ 1.0435 k ¼ 0.2091, a ¼ 1.1152, c ¼ 0.0699 k ¼ 0.2866, a ¼ 1.1065, c ¼ 0.0686 k ¼ 0.4323, a ¼ 1.1278, c ¼ 0.0746 k ¼ 0.5168, a ¼ 1.0687, c ¼ 0.0500 k0 ¼ 0.2505, k1 ¼ 0.2505, a ¼ 0.5394, b ¼ 0.5394 k0 ¼ 0.3435, k1 ¼ 0.3435, a ¼ 0.5357, b ¼ 0.5357 k0 ¼ 0.5243, k1 ¼ 0.5243, a ¼ 0.5441, b ¼ 0.5441 k0 ¼ 0.5933, k1 ¼ 0.5933, a ¼ 0.5217, b ¼ 0.5217 k ¼ 0.1220, a ¼ 7.0566, b ¼ 1.0829 k ¼ 0.1712, a ¼ 7.1290, b ¼ 1.0794 k ¼ 0.2471, a ¼ 7.4937, b ¼ 1.0818 k ¼ 0.3394, a ¼ 6.8931, b ¼ 1.0658 a ¼ 0.1697, b ¼ 0.0074 a ¼ 0.2315, b ¼ 0.0137 a ¼ 0.3476, b ¼ 0.0305 a ¼ 0.4096, b ¼ 0.0431 k ¼ 0.1666, a ¼ 0.9973, n ¼ 1.1878, b ¼ 0.0011 k ¼ 0.2561, a ¼ 1.0015, n ¼ 1.1408, b ¼ 0.0026 k ¼ 0.3204, a ¼ 0.9578, n ¼ 1.4140, b ¼ 0.0008 k ¼ 0.5324, a ¼ 1.0129, n ¼ 1.0290, b ¼ 0.0062

55 60 65 Approximation of diffusion

Wang and Singh

Midilli

50 55 60 65 50 55 60 65 50 55 60 65

The Midilli model provided a very good agreement between the experimental and predicted moisture ratios generally banded around a 45 straight line (R2ave ¼ 0.9976 for chili, 0.9997 for lemon grass, 0.9976 for kaffir lime leaf and 0.9979 for galangal slice).

Determination of the Diffusion Equation The diffusion coefficient, calculated by Equations (12) and (15) (Figure 5) and values of diffusion coefficient for different drying temperature are presented in Table 6. The diffusion coefficient during drying of herbs varied

R2

2

RMSE

0.9863 0.9879 0.9785 0.9949 0.9983 0.9979 0.9979 0.9987 0.9863 0.9879 0.9785 0.9949 0.9923 0.9928 0.9856 0.9968 0.9971 0.9978 0.9926 0.9997 0.9923

0.0006760 0.0003730 0.0019050 0.0004090 0.0001080 0.0001210 0.0001820 0.0001010 0.0006840 0.0006470 0.0019280 0.0004150 0.0003920 0.0003670 0.0012950 0.0002560 0.0002130 0.0001700 0.0006750 0.0000270 0.0003950

0.02592 0.01900 0.04339 0.02007 0.01032 0.01091 0.01332 0.00990 0.02599 0.02522 0.04339 0.02007 0.01968 0.01900 0.03556 0.01576 0.01447 0.01286 0.02551 0.00508 0.01964

0.9928

0.0003730

0.01900

0.9856

0.0013270

0.03556

0.9968

0.0002640

0.01576

0.9961 0.9972 0.9920 0.9995 0.9939 0.9925 0.9960 0.9885 0.9985

0.0002860 0.0002170 0.0007240 0.0000430 0.0002930 0.0004080 0.0003660 0.0009320 0.0001100

0.01676 0.01456 0.02643 0.00643 0.01701 0.02003 0.01891 0.03008 0.01037

0.9986

0.0001130

0.01045

0.9988

0.0001060

0.01005

0.9997

0.0000260

0.00492

from 1.68  1010 to 5.08  1010 m2/s for chili, 1.31  1010 to 3.33  1010 m2/s for lemon grass, 1.16  1012 to 2.46  1012 m2/s for kaffir lime leaf and 5.85  1011 to 1.42  1010 m2/s for galangal slice in the temperature range from 50  C to 65  C. As expected, the values of diffusion coefficient increased greatly with an increase in drying temperature. This is because the temperature difference between the sample and drying medium at the higher drying temperature was greater than that at the lower temperature, hence a larger driving force for heat and mass transfer. These values are within the standard range for food and agricultural products (from 1012 m2/s to 109 m2/s) and can be compared with 3.78  109 to 7.10  109 m2/s for

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144

Chili

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

Predicted moisture ratio

Predicted moisture ratio

A. ARTNASEAW ET AL.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Experimental moisture ratio

Lemon grass

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

Predicted moisture ratio

Predicted moisture ratio

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Experimental moisture ratio

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Experimental moisture ratio

Kaffir lime leaf

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

Galangal slice

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Experimental moisture ratio

Figure 4. Comparison of the experimental and predicted moisture ratio of Tom Yum herbs at all drying temperatures for the developed models. Temperature: (f) 50  C, () 55  C, (n) 60  C and (i) 65  C.

Table 6. Diffusion coefficients of Tom Yum herbs at different drying temperatures. Tom Yum herbs Chili

Lemon grass

Kaffir lime leaf

Galangal slice

Drying temperature ( C)

Diffusion coefficient (m2/s)

R2

50 55 60 65 50 55 60 65 50 55 60 65 50 55 60 65

1.68  1010 2.13  1010 4.44  1010 5.08  1010 1.31  1010 2.11  1010 2.94  1010 3.33  1010 1.16  1012 1.98  1012 2.26  1012 2.46  1012 5.85  1011 8.62  1011 1.08  1010 1.42  1010

0.9881 0.9801 0.9888 0.9663 0.9902 0.9857 0.9832 0.9791 0.9675 0.9749 0.9965 0.9939 0.9805 0.9895 0.9886 0.9991

red chill in temperature range 50Â&#x2014;65  C (Kaleemullah and Kailappan, 2006), 7.04  1012, 4.53  1012, 6.44  1012 m2/s for mint, parsley and basil leaves, respectively (Akpinar, 2006), 52.91  1010,

48.72  1010 and 43.42  1010 m2/s for crain-crain, bitter and fever leaves, respectively (Sobukola et al., 2007). The difference between the results of this study and those reported in the literature could be attributed to the different processing conditions as well as properties of material.

CONCLUSIONS A prototype of novel drying system, which combines the advantages of vacuum and heat pump, has been developed for drying Tom Yum herbs (chili, lemon grass, kaffir lime leaf and galangal slice). The drying kinetics of herbs was investigated. The experimental results showed that there is no constant rate period of drying. Drying time was reduced by increasing drying temperature. The Midilli model provided the best prediction of the thin layer drying characteristics of herbs. The constants and coefficients of the Midilli model can be expressed as functions of the drying temperature. The diffusion coefficient of herbs increased as the drying temperature increased.

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0.5 0.0 –1.0

ln MR

ln MR

–0.5 –1.5 –2.0 –2.5 –3.0 –3.5 0

2

4

6

8

Kaffir lime leaf

0.5 0.0 –0.5 –1.0 –1.5 –2.0 –2.5 –3.0 –3.5 –4.0

10 12 14 16 18

0

1

2

Lemon grass

0.5

ln MR

ln MR

0.0 –0.5 –1.0 –1.5 –2.0 –2.5

4

5

6

7

Time (h)

Time (h) 1.0

3

0 1 2 3 4 5 6 7 8 9 10 11 12

1.0 0.5 0.0 –0.5 –1.0 –1.5 –2.0 –2.5 –3.0 –3.5 –4.0 –4.5 –5.0 –5.5 –6.0

Galangal slice

0

2

Time (h)

4

6

8

10

12

14

16

Time (h)

Figure 5. Experimental and predicted logarithmic moisture ratio at different drying times of Tom Yum herbs. Temperature: (f) 50  C, () 55  C, (n) 60  C and (i) 65  C.

ACKNOWLEDGMENTS The authors would like to express their sincere thanks to the Energy Management and Conservation Office (EMCO), Khon Kaen University, for its financial support and NGC Engineering Co., Ltd for assistance in the construction of the heat pump vacuum dryer as well as the technical staff of the Department of Chemical Engineering, Khon Kaen University for their help in installing and testing the vacuum heat pump dryer and in testing herb qualities.

REFERENCES Abe T. and Afzal T.M. (1997). Thin-layer infrared radiation drying of rough rice. Journal of Agricultural Engineering Research 67: 289—297. Akal D., Kahveci K. and Cihan A. (2007). Mathematical modelling of drying of rough rice in stacks. Food Science and Technology International 13: 437—445. Akpinar E.K. (2006). Mathematical modelling of thin layer drying process under open sun of some aromatic plants. Journal of Food Engineering 77: 864—870. Alibas I. (2007). Energy consumption and colour characteristics of nettle leaves during microwave, vacuum and convective drying. Journal of Agricultural Engineering Research 96: 495—502. AOAC (1984). Official Methods of Analysis. 14th edn, Washington, DC: Association of official Analytical Chemists.

Demir V., Gunhan T. and Yagcioglu A.K. (2007). Mathematical modelling of convection drying of green table olives. Journal of Agricultural Engineering Research 98: 47—53. Division of Health Statistics (1989). Office of the Permanent Secretary, Ministry of Public Health. Bangkok, Thailand: Public Health Statistic. Ertekin C. and Yaldiz O. (2004). Drying of eggplant and selection of a suitable thin layer drying model. Journal of Food Engineering 63: 349—359. Hawlader M.N.A., Perera C.O. and Tian M. (2006). Properties of modified atmosphere heat pump dried food. Journal of Food Engineering 74: 392—401. Holkeboer D.H. (1967). Vacuum Technology and Space Simulation. Bosto, US: Boston Technical Publishers. Hossain M.A. and Bala B.K. (2007). Drying of hot chilli using solar tunnel drier. Solar Energy 81: 85—92. Kaleemullah S. and Kailappan R. (2006). Modelling of thin-layer drying kinetics of red chillies. Journal of Food Engineering 76: 531—537. Madamba P.S., Driscoll R.H. and Buckle K.A. (1996). Thin layer drying characteristics of garlic slices. Journal of Food Engineering 29: 75—97. Mangaraj S., Singh A., Samuel D.V.K. and Singhal O.P. (2001). Comparative performance evaluation of different drying methods for chillies. Journal of Food Science and Technology 38(3): 296—299. McMinn W.A.M. (2006). Thin-layer modeling of the convective, microwave, microwave-convective and microwave-vacuum drying of lactose powder. Journal of Food Engineering 72(2): 113—123. Murakami A., Ohigashi H. and Koshimizu K. (1994). Possible antitumor promoting properties of edible Thai food items and some of their active constituents. Asia Pacific Journal of Clinic Nutrition 3: 185—191.

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Nishimura H., Takahashi T., Wijaya C.H., Satoh A. and Ariga T. (2000). Thermochemical transformation of sulfur compound in Japanese domestic Allium. BioFactors 13: 257—263. Perera C.O. (2001). Modified atmosphere heat pump drying of food products. In: Proceedings of the Second Asia-Oceania Drying Conference, Penang, Malaysia, pp. 469—476. Ramesh M.N., Wolf W., Tevini D. and Jung G. (2001). Influence of processing parameters on the drying of spice paprika. Journal of Food Engineering 49: 63—72. Sobukola O.P., Dairo O.U., Sanni L.O., Odunewu A.V. and Fafiolu B.O. (2007). Thin layer drying process of some leafy vegetables under open sun. Food Science and Technology International 13: 35—40.

Soponronnarit S., Wetchacama S. and Kanphukdee T. (2000). Seed drying using a heat pump. International Energy Journal 1: 97—102. Togrul H. (2006). Suitable drying model for infrared drying of carrot. Journal of Food Engineering 77: 610—619. Vazquez G., Chenlo F., Moreria R. and Cruz E. (1997). Grape drying in a pilot plant with a heat pump. Drying technology 15: 899—920. Wu L., Orikasa T., Ogawa Y. and Tagawa A. (2007). Vacuum drying characteristics of eggplants. Journal of Food Engineering 83: 422—429. Zhangjing C. and Fred M.L. (2001). Vacuum drying of small wood components at room temperature. Forest Products Journal 51: 55—57.

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Food Science and Technology International http://fst.sagepub.com/

An Improved Process for High Quality and Nutrition of Brown Rice Production W. Watchararparpaiboon, N. Laohakunjit and O. Kerdchoechuen Food Science and Technology International 2010 16: 147 originally published online 9 July 2010 DOI: 10.1177/1082013209353220 The online version of this article can be found at: http://fst.sagepub.com/content/16/2/147

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An Improved Process for High Quality and Nutrition of Brown Rice Production W. Watchararparpaiboon, N. Laohakunjit* and O. Kerdchoechuen School of Bioresources and Technology, King Mongkut’s University of Technology Thonburi Bangkhuntien Bangkok, 10150, Thailand Germinated brown rice (GBR) of two popular Thailand varieties, Khao Dawk Mali 105 (KDML 105) and Chainat 1, with improved nutritional composition, was obtained by optimizing water soaking conditions. Different water pH (3, 4, 6 and 8), temperatures (25  C, 35  C and 45  C), and soaking times (12 and 24 h) were tested. Using the response surface methodology (RSM), the best condition for producing GBR of both varieties was soaking in water with pH 6 and temperature of 35  C for 24 h. It caused a 4- to 5-fold increase in gamma-amino butyric acid (GABA) content which, together with protein and lipid contents, were highest among treatments. Intermediate levels of thiamine (vitamin B1) and phytic acid (IP6) were obtained. In GBR of KDML 105 variety, GABA, vitamin B1 and IP6 contents were 16.48, 0.526 and 501.06 mg/100 g, respectively, while protein and lipid contents were 10.50% and 4.00%, respectively. For Chainat 1 variety, GABA, vitamin B1 and IP6 contents were 14.50, 0.436 and 486.03 mg/100 g, respectively, while protein and lipid contents were 9.80% and 3.99%, respectively. Carbohydrate and amylose contents differed by only less than 1—2% among treatments. Supplemental aeration during water soaking decreased GABA, protein and lipid contents, but increased vitamin B1 and IP6 contents in both varieties. Furthermore, cooked GBR of both varieties had softer texture than cooked ordinary brown rice. Key Words: germinated brown rice, Khao Dawk Mali 105, Chainat 1, nutritional quality, gamma-amino butyric acid, vitamin B1, phytic acid

INTRODUCTION Germinated brown rice (GBR) is a more nutritious food than polished grains and is produced by soaking brown rice grains in water to initiate germination (Komatsuzaki et al., 2005). Three nutrients in GBR beneficial to human health are of particular interest: gamma-aminobutyric acid (GABA), phytic acid (myoinositol hexakisphosphate, IP6) and thiamine or vitamin B1. GABA occurs at high levels in the brain, playing a major role in neurotransmission (Aurisano et al., 1995). In plants, it appears to play a dual role as a signaling molecule and metabolite that may control various aspects of plant development, metabolism and responses to stress, similar to the functions of other metabolites, such as glutamate and sugars (Nicolas and Hillel, 2004). IP6 is utilized during seed germination and supplies biosynthetic needs of the growing tissues (Oatway et al., 2001). Germination activates phytase, which hydrolyzes *To whom correspondence should be sent (e-mail: nutta.lao@kmutt.ac.th). Received 28 December 2008; revised 23 March 2009. Food Sci Tech Int 2010;16(2):0147–12 ß SAGE Publications 2010 Los Angeles, London, New Delhi and Singapore ISSN: 1082-0132 DOI: 10.1177/1082013209353220

IP6 into myo-inositol, an important compound used in cell wall formation, and inositol mono-penta phosphates (IP1-IP5), increasing the availability of phosphorus for the developing embryo. Processes such as soaking, fermentation, germination and irradiation have been shown to enhance IP6 degradation. Thiamine is an essential constituent of all cells and serves as a cofactor for two enzyme complex (pyruvate dehydrogenase and alpha-ketoglutarate dehydrogenase) in the citric acid cycle and, in plant cells, for the plastid-localized isozymes (pyruvate dehydrogenase and transketolase; Belanger et al., 1995). In cereal grains, thiamine is concentrated in the aleurone layer and in the embryo (Batifoulier et al., 2006). The starchy endosperm has low levels of thiamine, so consumption of only white flours and polished grains increases human susceptibility to thiamine deficiency. Lack of dietary thiamine results in abnormal carbohydrate metabolism, leading to increased pyruvate concentration in the blood and causing the disease called beriberi, which is a major health problem in many rice-consuming nations. During water soaking to produce GBR, hydrolytic enzymes are activated, breaking down high molecular weight polymers (e.g., starch, nonstarch polysaccharides and proteins) that results in the increase in oligosaccharides, amino acids and other bio-functional substances and improvement of organoleptic quality mainly due to

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grain softening, which makes food preparation by cooking easier (Kim et al., 2004; Ohtsubo et al., 2005). Earlier, Saikusa et al. (1994) found that water soaking of rice cv. Koshihikari brought about remarkable changes in the component and content of free amino acids in the kernel, the most significant of which was the increase in GABA content. In rice cv. Haiminori, GABA content increased after soaking in water for 4 h by about 3Â&#x2014;4 times higher than that of ordinary rice cultivars (Maeda et al., 2001). Zhang et al. (2005) reported that the nutrients that increased dramatically in GBR include GABA, lysine, magnesium, thiamine, tocotrienol and calcium. Several processes have been described to produce GBR. Rice grains with husks intact are immersed in brine solution to select those with high germination potential and the selected grains are partially polished to brown rice, which are then soaked in water and aerated repeatedly (Soon and Lee, 1999; Liu et al., 2005). Hiromichi et al. (2003) described another method for preparing GBR with good cooking ability, texture and storage potential whereby the water content, degree of gelatinization and efficiency of water absorption during immersion in water were controlled by subjecting the grains to steaming or moist heating followed by drying. After soaking for 3 h and gaseous treatment for 21 h at 35  C, the content of GABA in GBR of Japonica rice (24.9 mg/100 g) was higher than by the conventional soaking method (10.1 mg/100 g) (Komatsuzaki et al., 2005). Recently, a large amount of nutrient in GBR (vitamin, minerals, fibers and effective component such as phytic, ferulic and GABA) has been correlated with specific bioactivity. Therefore, GBR could become popular healthy food but the comprehensive data on nutrition and GABA content of GBR in Thailand are lacking. The aim of the present study was to investigate the effects of pH, temperature and soaking time on nutritional or bio-functional contents and textural quality of two rice varieties with different amylose contents, Khao Dawk Mali 105 (KDML 105) and Chainat 1 by response surface methodology (RSM). The effects of supplemental aeration during water soaking were also examined.

MATERIALS AND METHODS Rice Samples Two popular rice varieties, KDML 105 (15.88% amylose) and Chainat 1 (20.21% amylose) were obtained from Rice Research Center, Thailand. All paddy rices (rough rice) were harvested in December 2006 and contained 20% moisture. After 24 h from harvesting, the samples were sun-dried to 14% moisture content (wet basis).

Methods Optimization Experiment KDML 105 and Chainat 1 rough rice that had been stored for 4 months at 4  C were dehulled using a Satake dehuller (THU-35, Thailand). Three water soaking conditions were studied and combined, water pH (x1) (3, 4, 6 and 8), water temperature (x2) (25  C, 35  C and 45  C) and soaking time (x3) (12 and 24 h). The pH was adjusted to the desired values (3, 4, 6 and 8) with 0.1 N CH3COOH or NaOH and temperatures were controlled by subjecting the soaked samples in water bath. One-hundred grams of grain samples were soaked in distilled water (the ration 1 : 10) with specified pH and temperature for the specified period of time in the dark and then incubated at room temperature until a radical emerge at a height of approximately 0.5Â&#x2014;2.0 mm. The GBR was dried at 50  C in an oven until about 15% moisture content. For each rice variety, the experiment was arranged in 4  3  2 factorial (water pH  water temperature  soaking time) in randomized complete block design. All treatments were replicated three times. To identify optimum levels of three variables, the response surface methodology was applied. Effects of Aeration The best condition in producing GBR (KDML 105 and Chainat 1) as identified in the above optimization experiment was used. During water soaking, aeration was continuously supplied using an air injection motor (S-4000, Thailand), compared with the control of without aeration. Other procedures were the same as that in the optimization experiment. The experiment was 2  3 factorial (rice varieties  water soaking conditions) in completely randomized design with three replicates. Lipid, Protein, Carbohydrate and Amylose Analysis GBR flours were prepared by grinding GBR samples using UD cyclone sample mill (IKA, Germany) with 100-mesh sieve. The GBR flour samples were sealed and stored in the dark at 4  C until use. Moisture, crude lipid, crude protein, crude fiber and ash contents were analyzed following the AOAC standard method (AOAC, 1990). Total carbohydrates was determined by subtracting all constituents from 100. Amylose content was determined by the simplified assay method of Juliano (1971). The ground GBR (0.10 g) was suspended in 1 mL 95% ethyl alcohol, mixed with 90 mL 1 M NaOH and incubated at room temperature for 10 min. The sample was heated for 10 min in a boiling water bath, allowed to cool, volume made to exactly 100 mL with distilled water and mixed thoroughly. The sample was allowed to stand at room temperature for at least 2 h. A 5 mL of the vigorously resuspended sample was mixed with 1 mL

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An Improved Process for High Quality and Nutrition of Brown Rice Production

of 1N acetic acid, 2 mL of iodine solution and volume adjusted to 100 mL with distilled water. The sample was incubated to develop a dark purple color at room temperature for 20 min. Sample absorbance at 620 nm was measured in a spectrophotometer (UV-1601, Shimadzu, Japan) against a blank consisting of 2 mL of iodine solution and 1 mL of 1N acetic acid diluted to 100 mL with distilled water. The 620 nm absorbance was converted to amylose concentration using a standard absorbance curve using purified potato amylose (Sigma, St. Louis, USA) with known concentrations. Three replicates of each test were performed. Vitamin B1 Analysis The procedure of Liu et al. (2002) was followed. One gram GBR powder was placed in 100 mL calibrated flask, added with 1.5 mL NH4ClÂ&#x2014;NH3.H2O buffer (49 mL of 0.2 mol/L NH4Cl and 1 mL of 0.2 mol/L NH3.H2O, pH 7.6) and solubilization agent (1%) 1.0 mL Triton-X-100 for phenol red and diluted to 15 mL with water. Dye solution (0.05%) 1.5 mL for PR was added and the mixture was diluted to 100 mL with water. The solution was set aside for 10 min for PR. Absorbance was measured at 427 nm using a spectrophotometer. A stock solution containing 500 mg/mL of vitamin B1 was prepared by dissolving 0.025 g of vitamin B1 in deionized water. The standard concentration was 10, 20, 30 and 40 mg/mL prepared from the stock solution. All determinations were carried out in triplicate. Phytic Acid (IP6) Analysis  This was done by the method of Bilgicli and Ibanoglu (2007). GBR powder 0.6 g in weight was extracted with 0.2 N HCl and 0.5 mL of this extract was pipetted into the test tube fitted with a ground-glass stopper. After adding 1 mL of ferric solution (0.2 g of ammonium iron (III) sulphate 12 H2O in 100 mL 2 N HCl and made up to 1000 mL with distilled water), the test tube was covered with the stopper and fixed with a clip. The solution in test tubes was heated in boiling water bath for 30 min. This was followed by cooling in ice water for 15 min to equilibrate with room temperature. The contents of the tube were mixed and centrifuged for 30 min at 3000 g. 1 mL of the supernatant was transferred to another tube and then added and mixed with 2 mL 2,20 -bipyridine solution (10 g of 2,20 -bipyridine and 10 mL of thioglycollic acid in distilled water and make up to 100 mL). Absorbance was measured at 519 nm against distilled water. Standard solutions were prepared from a stock solution of 1500 mg/mL phytic acid (Sigma, St. Louis, USA) prepared with deionized water. Calibration standards with 20, 40, 60, 80, 100, 120, 140, 160 mg/mL phytic acid were prepared from the stock solution in deionized water. All experiments were carried out in triplicate.

149

-Aminobutyric acid Analysis The extraction procedure of Komatsuzaki et al. (2005) was employed. Three grams GBR powder was placed in a screw-capped test tube containing 30 mL 70% (v/v) ethanol solution. The mixture was vigorously mixed for 1 min at room temperature and then centrifuged at 8000 g for 5 min at 4  C. The same volume of 70% ethanol solution was added to the precipitate as described above, and extraction was repeated. The collected supernatant was evaporated to dryness under vacuum at 40  C and dissolved in 3 mL water. An aliquot (0.1 mL) of extract was placed in a test tube and added with 0.2 mL borate buffer (0.2 M boric acid and 0.2 M sodium borate, pH 9) and 1 mL phenol reagent (6%). After mixing thoroughly and cooling in ice water, 0.4 mL of 7.5% sodium hypochlorite reagent was added. The test tube was then shaken vigorously in ice water and placed in a boiling water bath for 10 min. It was then immediately cooled by immersion in ice water for 5 min. The optical density was read at 630 nm against a blank containing the reagents without the GBR extract. Standard solutions were prepared from a stock solution of 500 mg/mL g-aminobutyric acid (GABA, Sigma, St. Louis, USA) prepared with deionized water. Calibration standards with 125, 250, 375 and 500 mg/mL GABA were prepared from the stock solution in deionized water. Twenty mL of the extract solution obtained above was injected in high-performance liquid chromatographyevaporation light scattering detection (HPLC-ELSD) unit. HPLC analyses were carried out using a Alltech liquid chromatograph (pump model 626, and an ELSD 200 ES). The column was a Prevail-C18, 5 mm, 250  4.6 mm2 inner diameter (Alltech, Deerfield, Italy). The HPLC-ELSD conditions were performed according to the previous reported procedures with modifications (Abe et al., 1998). The mobile phases were (A) 0.1% trifluoroacetic acid in water and (B) 0.1% trifluoroacetic acid in acetonitrile, with a gradient of 0% B in A in the first 7 min, 40% B from 7 to 8 min and 60% B from 8 to 15 min. The flow rate was 0.6 mL/min. The conditions set for ELSD (200ES, Alltech, USA) were 95  C of drift tube temperature, 2.5 L/min nitrogen gas flow, and gain value of 1 in the impactor-on mode. In quantitative analyses, GABA was quantified by peak area using the external standard method. A volume of 20 mL standard sample (100 ppm) was injected for HPLC-ELSD analysis with the same method above. All samples were analyzed in triplicate. Texture Analysis Hardness of cooked GBR rice was determined using a texture analyzer (LLOYD Instrument model TA PLUS) with back extrusion method and the peak force in kg was recorded. The cooked GBR samples were prepared using the adequate water method. Milled GBR (25 g)

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was placed in 100 mL glass beakers with 45 mL of water for KDML 105 and 55 mL for Chainat 1. The rice was cooked at a time for 20 min in automatic rice cooker (Hitachi Model RPT 20 HK, 650 W, 2.5 L capacity) and 800 mL of excess water was poured into the outer pot to provide steam for regulating the cooking process. The rice cooker was left undisturbed for at least 15 min after cooking. After cooling, the cooked rice was placed in plastic bags at room temperature before analysis on a same day. A back extrusion test cell linked to an Instron testing machine equipped with a 50 kg capacity compression load cell was used. The back extrusion test cell consisted of a cylinder and a spherical plunger with 12.4 mm diameter. The sample vessel was made from commercial stainless steel pipe with an internal diameter of 15.5 mm. Three replicate samples (4 g) of cooked GBR rice were placed in the extrusion cell (4.91  5.51 cm2). Hardness was also measured at the same chart sample speed using the 0—50 kg load cell. The total compression energy was determined by integrating the area under the force— distance curve. Pasting Properties Pasting properties of GBR flours were determined with a Rapid Visco Analyser (Model RVA 4, Newport Scientific, Australia) using the AACC Approved Method (AACC, 2000). A slurry (3 g, 14% moisture basis) was equilibrated at 50  C for 1 min, heated to 95  C within 3 min 40 s, held at 95  C for 2 min 70 s and then cooled down to 50  C within 3 min 88 s, and finally held at 50  C for 2 min. The suspension was stirred at 960 rpm for 6 s and then at 160 rpm for remainder of the test. The viscosity was expressed in Rapid Visco Units (RVUs). All measurements were performed in triplicate. Statistical Analysis Analysis of variance and Duncan’s multiple range test (DMRT) were performed using the SAS program (Version 6.0, Analytical Software, SAS Institute, 1997). To identify the optimum levels of the three variables (water pH, temperature and soaking time) for high nutritional components (lipid, protein, carbohydrate, amylose, vitamin B1, IP6 and GABA), the response surface methodology (RSM) was applied. RSM is to optimize this response, leading to rapid and efficient development of new products or processes. The response value Y was estimated by the following equation: Y ¼ b0 þ b1 x1 þ b2 x2 þ b3 x3 þ b12 x1 x2 þ b13 x1 x3 þ b23 x2 x3 þ

b11 x21

þ

b22 x22

þ

b33 x23

coefficient, b1, b2 and b3 as the linear coefficient (main effects) and b12, b13, and b23 as the two factors interaction coefficients. The model was simplified by removing the nonsignificant terms according to backward elimination technique (Mendenhall and Sincich, 1996). Response surface plot was generated using STATISTICA Version 5.0 (StatSoft’s Ltd, USA, 1995).

RESULTS AND DISCUSSION Optimization Experiment The nutritional components of GBR of the two varieties significantly varied with water pH, temperature and soaking time (Tables 1 and 2). Protein, vitamin B1 and IP6 contents showed wide differences among treatments, ranging from 7.55% to 10.50%, 0.406 to 0.646 mg/100 g and 406.03 to 650.03 mg/100 g, respectively, for KDML 105 variety and 7.44% to 9.80%, 0.313 to 0.556 mg/ 100 g and 349.96 to 644.90 mg/100 g, respectively, for Chainat 1 variety. GABA for KDML105 and Chainat1 was 7.61—16.48 mg/100 g and 5.76—14.51 mg/ 100 g, respectively (Figures 1 and 2). Lipid, carbohydrate and amylose contents varied only by less than 1—2% between treatments. Water soaking conditions that gave maximum nutrient level differed. GABA as well as lipid and protein contents were highest in GBR of the two varieties at water pH 6, temperature of 35  C and soaking time of 24 h. The same water temperature and soaking time but higher water pH of 8 caused the highest vitamin B1 content in both varieties. IP6 content was highest at water temperature of 25  C and soaking time of 12 h at pH 6 for KDML 105 variety and pH 8 for Chainat 1 variety. For carbohydrate and amylose contents, no clear trend on the effect of water pH, temperature and soaking time was obtained. Multiple linear regression analysis showed significant R2 values of >0.80 for the effects of water pH, temperature and soaking time on protein, vitamin B1, IP6 and GABA contents for both KDML 105 (Table 3) and Chainat 1 (Table 4). The analysis of variance showed that the models to be adequate, with no significant lack of fit and with a satisfactory R2. From this, the best explanatory model equations for protein, vitamin B1, IP6 and GABA contents for KDML 105 variety are respectively shown as follows: Yprotein ¼ 6:740 þ 0:167x2 þ 1:396x3

ð1Þ

where Y as the response variable, x1, x2 and x3 as the independent variables representing water pH, temperature and soaking time, respectively, b0 as an constant

þ 0:003x1 x3  0:073x21  0:037x23 YB1 ¼ 10:083  0:011x1 þ 1:252x3  0:0008x1 x3 þ 0:002x21  0:0004x22

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ð2Þ

ð3Þ

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An Improved Process for High Quality and Nutrition of Brown Rice Production

Table 1. Nutritional components of GBR of KDML 105 variety as affected by water pH, temperature and soaking time. Water soaking condition pH 3

Temp. ( C)

Time (h)

Lipid (%)

Protein (%)

12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24

3.45 x 3.50 k 3.36 m 3.66 g 3.46 x 3.75 e 3.60 h 3.80 d 3.73 f 3.89 b 3.55 j 3.79 d 3.49 k 3.76 e 3.55 j 4.00 a 3.61 h 3.75 e 3.45 x 3.72 f 3.55 j 3.84 c 3.35 m 3.58 i ** 0.231

8.70 xm 9.04 h 8.83 k 9.50 c 8.66 n 9.04 h 8.56 o 9.30 f 7.96 r 9.41 e 8.24 q 9.43 de 8.86 j 9.60 b 9.01 i 10.50 a 8.73 x 9.50 c 7.55 s 8.69 m 8.44 p 9.44 d 8.65 n 9.13 g ** 0.173

25 35 45

4

25 35 45

6

25 35 45

8

25 35 45 F-test C.V. (%)

Carbohydrate (%) 67.55 67.11 67.72 67.06 66.80 66.51 67.18 66.64 67.25 66.68 66.81 66.03 66.78 66.00 66.38 65.84 66.26 65.96 67.16 66.58 66.06 65.66 67.19 66.26 ** 0.15

Amylose (%)

a b a b c de b cd b cd c gh c gh ef hi f gh b d g i b f

14.99 14.86 14.66 14.26 15.06 14.97 14.54 14.16 14.29 14.03 14.97 14.60 14.46 14.06 14.16 14.06 14.66 14.16 15.00 14.97 14.66 14.33 14.76 14.29 ** 0.29

Vit. B1 (mg/100 g)

b c e h a b f i h j b ef g j i j e i ab b e h d h

0.443 0.483 0.426 0.496 0.443 0.456 0.423 0.486 0.476 0.533 0.406 0.493 0.466 0.546 0.473 0.526 0.453 0.506 0.506 0.536 0.606 0.646 0.483 0.540 ** 1.21

IP6 (mg/100 g)

n ij o g n m o hi jk de p gh x c kx e m f f d b a ij cd

585.46 561.66 649.66 545.06 567.06 523.76 605.06 496.56 582.36 478.26 468.06 406.03 650.03 521.46 537.66 501.06 571.06 459.76 576.06 543.80 545.06 531.66 580.06 475.03 ** 0.02

d j b k i o c r e s u w a p m q h v g x k n f t

Means in a column followed by the same letter are not significantly different based on DMRT, 5%. **significant (p  0.01).

YIP6 ¼ 6:232 þ 2:934x2 þ 96:977x3 þ 0:203x1 x3 þ

5:050x21



0:094x22

YGABA ¼ 10:844 þ 4:925x1 þ 0:154x3  0:023x1 x3  0:427x21 þ 0:001x23

ð4Þ

ð5Þ

For KDML 105, interaction on protein, vitamin B1, IP6 and GABA contents were significantly influenced between water pH (x1) and soaking time (x3) (p  0.01 Table 3). Regression coefficients showed that temperature and soaking time had a linear effect on protein and IP6 values. Water pH and soaking time had the most significantly linear (p  0.01) on vitamin B1 and GABA. The largest values of estimated regression coefficient for soaking time (b3 ¼ 1.396, 1.256 and 96.977) indicated that it was the most important linear variable influencing the protein, vitamin B1 and IP6 contents. The positive value implied that protein, vitamin B1 and IP6 values increased with increasing of soaking time. Water pH (b1 ¼ 4.925) had the most significantly linear and quadratic effects (p  0.01) on GABA contents; temperature had the significant quadratic effect (p  0.01). However, water pH and temperature did not interact significantly (p > 0.05). GABA content increased with

increasing water pH. Figure 3c shows the significant quadratic effect of water pH on GABA content. By increasing water pH from pH 3 to 6, content of GABA increased while at water pH over 6 its content decreased at a fixed temperature. Figure 3d shows the significant quadratic effect of soaking time on IP6 content. At a fixed temperature, soaking time led to gradual increase of IP6 but it declined later. These results were similar to the results reported by other researchers (Saikusa et al., 1994; Oatway et al., 2001; Kim et al., 2004). For Chainat 1 variety, the best explanatory model equations for protein, vitamin B1, IP6 and GABA contents are respectively as follows: Yprotein ¼  8:744 þ 0:170x2 þ 1:337x3  0:006x1 x3  0:105x21  0:035x23 YB1 ¼ 8:014 þ 0:031x1 þ 0:962x3  0:0002x1 x3 þ 0:001x21  0:0004x22 YIP6 ¼ 7:265 þ 4:339x2 þ 95:392x3  0:406x1 x3 þ 6:197x21  0:111x22

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ð6Þ

ð7Þ

ð8Þ

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Table 2. Nutritional components of GBR of Chainat 1 variety as affected by water pH, temperature and soaking time. Water soaking condition pH 3

Temp. ( C) 25 35 45

4

25 35 45

6

25 35 45

8

25 35 45

Time (h)

Lipid (%)

Protein (%)

12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24

3.26 m 3.30 x 3.34 k 3.46 j 3.56 hi 3.87 b 3.58 hg 3.75 d 3.59 g 3.80 c 3.45 j 3.66 f 3.55 i 3.70 e 3.80 c 3.99 a 3.60 g 3.89 b 3.55 i 3.66 f 3.45 j 3.74 d 3.28 xm 3.87 b ** 0.36

7.70 n 8.03 h 7.82 x 8.50 c 7.66 o 8.06 g 7.74 m 8.31 e 7.94 j 8.37 d 7.82 x 8.26 f 7.96 j 8.60 b 8.00 i 9.80 a 7.91 k 8.48 c 7.64 o 7.57 p 7.55 q 7.44 r 7.64 o 7.59 p ** 0.16

F-test C.V. (%)

Carbohydrate (%) 67.65 67.29 67.63 67.14 67.70 67.51 68.15 67.94 67.68 67.65 67.81 67.15 68.03 67.78 67.87 67.38 67.58 67.28 68.15 67.53 68.06 67.65 67.59 67.58 ** 0.30

gh x h m f j a c fg gh e m b e d k i x a j b gh i i

Amylose (%)

Vit. B1 (mg/100 g)

18.50 efg 18.30 hijk 18.86 bc 18.19 jk 19.60 a 18.50 efg 18.77 bcd 18.94 b 18.86 bc 18.33 ghij 18.87 bc 18.40 fghi 18.84 bc 18.42 fgh 18.50 efg 18.12 k 18.94 b 18.81 bc 18.77 bcd 18.68 cde 18.96 b 18.59 def 18.21 ijk 18.19 jk ** 0.62

0.336 0.376 0.333 0.366 0.336 0.356 0.336 0.386 0.376 0.406 0.313 0.396 0.376 0.446 0.356 0.436 0.346 0.406 0.406 0.446 0.506 0.556 0.376 0.336 ** 1.48

x h x i x j x g h e m f h c j d k e e c b a h x

IP6 (mg/100 g) 650.06 537.03 626.93 512.03 589.96 477.93 598.93 460.03 560.03 419.93 460.23 349.96 610.13 500.86 597.90 486.03 558.03 446.93 644.90 521.93 614.93 454.90 559.93 400.03 ** 1.40

a g b hi e j de k f m k o cd i e j f x a h bc kx f n

Means in a column followed by the same letter are not significantly different based on DMRT, 5%. **significant (p  0.01).

YGABA ¼ 15:653 þ 5:648x1 þ 0:906x3  0:029x1 x3 

0:498x21



0:018x23

ð9Þ

The ANOVA showed that the linear effect of temperature and soaking time on protein and IP6 contents, but the linear effect of water pH and soaking time on vitamin B1 and GABA contents dominated over the other variable (Table 4). Being the most important linear variable affecting protein, vitamin B1 and IP6, soaking time had the highest regression at (b3 ¼ 1.337, 0.969 and 95.392), respectively. Water pH (b1 ¼ 5.648) had the maximum influence on GABA and as a shown in Table 4, GABA increased by increasing water pH. Water pH had a most significantly quadratic effect (p  0.01) on protein, vitamin B1, IP6 and GABA yield. Besides, there was a significant interaction between water pH and soaking time (p  0.01), which also influenced protein, vitamin B1, IP6 and GABA contents. The high quadratic effect of water pH caused GABA to have a curvilinear behavior (Figure 4c). Figure 4d shows the significant quadratic effect of soaking time on IP6 content. The plot pattern of both GBR varieties was similar pattern to each other. Optimal condition for brown rice production was determined to obtain maximum protein, vitamin B1

and GABA but minimum IP6. Second-order polynomial models obtained in this study were utilized for each response in order to determine the specified optimum conditions. Soaking time and water pH during construction of the overly plot while keeping the temperature at their optimum (35  C) in order to obtain protein, vitamin B1 and GABA contents higher than 45  C and 25  C, respectively. The zone of optimization as shown in the overlay plot depicts the water pH to be 5—6 and soaking time between 18 and 24 h. Three-dimensional plots at water temperature of 35  C revealed that the best conditions for improved nutritional composition (protein, vitamin B1, IP6 and GABA contents) of GBR were water pH of 6 and soaking time of 24 h for both KDML 105 (Figure 3) and Chainat 1 (Figure 4). Increased GABA content is one of the spectacular effects of water soaking to produce GBR (Saikusa et al., 1994; Maeda et al., 2001). The results of the present study confirmed these findings. At optimum water soaking conditions (pH 6, temperature of 35  C and soaking time of 24 h), GABA content of GBR of KDML 105 and Chainat 1 varieties exhibited almost similar increase (4—5 fold) as that found in Harimori variety (3—4 fold) (Maeda et al., 2001). However, the GABA content of the two Thai rice varieties was much higher than that found for the Japonica variety

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An Improved Process for High Quality and Nutrition of Brown Rice Production (a)

20 16 12

c

n

e

h

k

b

s v

8 4

GABA (mg/100 g)

GABA (mg/100 g)

(a)

20 16 12

r

n

i

12 h

v

4

12 h

24 h

24 h Hours

(b)

(b)

20

a

16 p

m

d

g

i u

8

x

GABA (mg/100 g)

GABA (mg/100 g)

20

4

a

16 12

p

m

g i

12 h

x

w

8 4

12 h

24 h

24 h

Hours

Hours (c) 20 16 r

o

i

l

f

t

c w

8 4

GABA (mg/100 g)

GABA (mg/100 g)

c

0

0

12

b

8

Hours

(c)

d

0

0

12

k

s

20 16 12

g

l

h

o

e

t

f u

8 4 0 12 h

0 12 h

24 h Hours

24 h Hours

Figure 1. GABA content of GBR of KDML 105 variety at water pH 3, 4, 6, 8 and temperature: (a) 25  C, (b) 35  C, (c) 45  C for soaking time 12 and 24 h.

at the same water soaking condition (Komatsuzaki et al., 2005). These results indicate genetic differences in GABA synthesis and accumulation due apparently to differences in protein content and its subsequent hydrolysis to amino acids, such as glutamate, which is the precursor of GABA. In the present study, KDML 105 variety was found to have higher GABA and protein contents than Chainat 1 variety for both GBR and ordinary (unsoaked) brown rice. GABA is mainly metabolized via a short pathway, called the GABA shunt, because it bypasses two steps of the tricarboxylic acid cycle, which is catalyzed by three enzymes, the cytosolic enzyme glutamate decarboxylase (GAD) and the

Figure 2. GABA content of GBR of Chainat 1 variety at water pH 3, 4, 6, 8 and temperature: (a) 25  C, (b) 35  C, (c) 45  C for soaking time 12 and 24 h.

mitochondrial enzymes GABA transaminase and succinic semialdehyde dehydrogenase (Shelp et al., 1999; Nicolas and Hillel, 2004). The increased GABA content of GBR at appropriate water pH has been attributed to increases in HĂž and activation of GAD, promoting the HĂž-consuming alpha-decarboxylation of glutamate which is catalyzed by GAD (Shelp et al., 1999). Favorable cytosolic pH may also be maintained under oxygen deficiency condition and GABA accumulation under this condition may result from both increased rate of synthesis and low rate of catabolism (Sousa and Sodek, 2002). High pH is promotive to thiamine phosphate pyrophosphorylase activity (Golda et al., 2004)

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Table 3. Model coefficient estimated by multiple linear regression for nutritional components of GBR of KDML 105 variety as affected by water pH (x1), temperature (x2), and soaking time (x3). Coefficient Constant (b0) Linear x1 x2 x3 Quadratic x12 x22 x32 Interaction x1x2 x1x3 x2x3 R2

Protein

Fat

Carbohydrate

Amylose

Vitamin B1

IP6

GABA

6.740**

ns

ns

ns

10.083**

6.232**

10.844**

— 0.167** 1.396**

ns ns **

ns ** ns

ns ns **

0.011** — 1.252**

— 2.934** 96.977**

4.925** — 0.154**

0.073** — 0.37**

ns ns ns

ns ns ns

ns ns ns

0.002** 0.0004** —

5.050** 0.094** —

0.427** — 0.001**

— 0.003** — 0.858

ns ns ns

ns ns ns

ns ns ns

— 0.0008** — 0.906

— 0.203** — 0.811

— 0.023** — 0.807

ns: not significant; **significant (p  0.01).

Table 4. Model coefficient estimated by multiple linear regression for nutritional components of GBR of Chainat 1 variety as affected by water pH (x1), temperature (x2) and soaking time (x3). Coefficient Constant (b0) Linear x1 x2 x3 Quadratic x12 x22 x32 Interaction x1x2 x1x3 x2x3 R2

Protein

Fat

Carbohydrate

Amylose

Vitamin B1

IP6

GABA

8.744**

ns

ns

ns

8.014**

7.265**

15.653**

— 0.170** 1.337**

ns ns **

ns ** ns

ns ns **

0.031** — 0.962**

— 4.339** 95.392**

5.648** — 0.906**

0.105** — 0.035**

ns ns ns

ns ns ns

ns ns ns

0.001** 0.0004** —

6.197** 0.111** —

0.498** — 0.018**

— 0.006** — 0.811

ns ns ns

ns ns ns

ns ns ns

— 0.0002** — 0.832

— 0.406** — 0.914

— 0.029** — 0.865

ns: not significant; **significant (p  0.01).

and this could account for the increased vitamin B1 content of GBR of both KDML 105 and Chainat 1 varieties when water pH 8 was used. At water pH 6, the vitamin B1 content of GBR could be increased by aeration treatment, as found in this study, but the treatment has to be weighed against the decrease in GABA content. Aeration allows respiration and formation of pyrimidine and thiazole, which could be converted to thiamine (Begley, 1996; Leonardi et al., 2003). On the other hand, the reduction of IP6 content of GBR of both KDML 105 and Chainat 1 varieties was anticipated. Germination activates phytase, which hydrolyzes IP6 into myo-inositol and inositol phosphate (IP1-IP5) (Oatway et al., 2001). The findings in this study, on the effects of germination on phytic acid, were in agreement with earlier reports. Agte and Sandhana (1997) have observed decrease in phytic acid contents of wheat batter by 40%. Oloyo (2004)

and Badau et al. (2005) reported that germination reduced phytic acid content in germinating seed, due to increased phytase activity. To maintain a higher level of IP6 in GBR, aeration could be applied during water soaking but similar to vitamin B1, the reduction in GABA content has to be considered. The increase in IP6 under aerated condition during water soaking has been reported to be due to the activation by two pathways (Cahoon and Tingey, 2006). The first pathway uses free myo-inositol as the initial substrate, with subsequent phosphorylation by phosphoinositol kinase. Contribution to the free myo-inositol pool is either by recycling from other pathways or by dephosphorylation of myo-inositol-1-phosphate. The alternate pathway uses myo-inositol-1-phosphate as the initial substrate, with subsequent phosphorylation by phosphoinositol kinase. The committed step for myo-inositol-1phosphate production is the NADþ-catalyzed oxidation

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An Improved Process for High Quality and Nutrition of Brown Rice Production (a)

(b) 10.8 10.4 10.0 9.6 9.2 8.8 8.4 8.0 24

Vitamin B1

Protein

0.68 0.64 0.60 0.56 0.52 0.48 0.44 24 8

8 7

Tim 18 e

6 5 12

4

pH

5 12

3

(c)

6

pH

4 3

(d) 18

660 620

16

IP6

14 GABA

7

Tim 18 e

12 10 8

580 540 500 460 420 24

24

8

8 7

Tim 18 e

6 5 12

4

7

Tim 18 e

pH

6 5 12

3

4

pH

3

Figure 3. Three-dimensional plots of water pH, soaking time and nutritional components: (a) protein; (b) vitamin B1; (c) GABA; and (d) phytic acid of GBR of KDML 105 variety at water temperature of 35  C.

of carbon 5 of the b-enantiomer of D-glucose-6phosphate. These reactions are catalyzed by myoinositol-1-phosphate synthase. During germination, hydrolytic enzymes are activated and one of these is the amylases which hydrolyze starches as well as amylose and amylopectin to simpler products (Oloyo, 2004). As a result, carbohydrate content decreases. This was also found in the present study. However, lipid and protein contents of GBR of both KDML 105 and Chainat 1 varieties increased relative to that of ordinary brown rice, indicating rapid turnover of free fatty acids and amino acids to form new lipid and protein compounds. Effects of Aeration Supplemental aeration during water soaking decreased the GABA content but increased the IP6 and vitamin B1 contents of GBR of the two varieties (Table 5). Protein and lipid contents of both varieties also decreased in response to aeration, while

carbohydrate and amylose contents increased, except that the increase in amylose content of Chainat 1 variety was not significant relative to that without aeration treatment. When compared to the unsoaked brown rice (control), it can be clearly seen that water soaking without aeration remarkably increased GABA content by about 4Â&#x2014;5 fold. Increases in vitamin B1, protein and lipid contents were less pronounced. On the other hand, IP6, carbohydrate and amylose contents decreased compared to that of the control. Furthermore, HPLC analysis of GABA content of GBR of KDML 105 variety at optimum water soaking condition (water pH 6, temperature of 35  C and soaking time of 24 h) showed little higher level (17.04 mg/ 100 g) than that obtained by UV-spectrophotometer (16.48 mg/100 g; Table 5). For Chainat 1 variety, HPLC and UV-spectrophotometer measurements of GABA content did not widely differ (14.49 mg/100 g and 14.50 mg/100 g). This finding could be concluded that the method for assay GABA in GBR by spectrophotometer is a rapid and simplified method because of

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W. WATCHARARPARPAIBOON ET AL. (a)

(b) 0.58 0.54

9.6 9.2 8.8

Vitamin B1

Protein

10.0

8.4 8.0 7.6 24

0.50 0.46 0.42 0.38 0.34 24 Tim 18 e

7

Tim 18 e

6 5 12

12

pH

4

6

5

4

pH

3

3

(c)

(d)

680 640

16 14 12 IP6

GABA

8

7

8

10

600 560

8

520 480

6 24

440 24

8

Tim

18

6

e 12

4

7

8 7

Tim 18 e

5 H p

5 4

12

3

6

pH

3

Figure 4. Three-dimensional plots of water pH, soaking time and nutritional components: (a) protein; (b) vitamin B1; (c) GABA; and (d) phytic acid of GBR of Chainat 1 variety at water temperature of 35  C. Table 5. Nutritional components of GBR of KDML 105 and Chainat 1 varieties at optimum water soaking conditions (pH 6, temperature of 35  C and soaking time of 24 h) with and without aeration. Rice variety

Water soaking condition

KDML 105

Optimum, without aeration Optimum, with aeration Control (unsoaked) Optimum, without aeration Optimum, with aeration Control (unsoaked) F-test C.V. (%)

Chainat 1

Lipid (%) 4.00 3.70 3.31 3.99 3.95 2.95 ** 0.52

a c d a b e

Protein (%) 10.50 8.51 7.18 9.80 8.59 6.92 ** 0.34

a d e b c f

Carbohydrate (%) 65.84 66.59 70.08 67.87 67.60 69.10 ** 0.06

c b a e f d

Amylose (%) 14.06 14.53 15.88 18.12 18.16 20.21 ** 0.32

e d c b b a

Vit. B1 (mg/100 g) 0.526 0.663 0.403 0.436 0.580 0.286 ** 2.01

c a e d b f

IP6 (mg/100 g) 501.06 702.66 825.00 486.03 633.00 795.66 ** 0.19

e c a f d b

GABA (mg/100 g) 16.48 10.20 3.77 14.50 8.69 3.21 ** 0.19

a c e b d f

Means in a column followed by the same letter are not significantly different based on DMRT, 5%. **significant (p  0.01).

its greater convenience and sensitivity. Moreover, HPLC analysis confirmed the reduction in GABA content in both varieties due to aeration during water soaking. With aeration, the decrease in GABA, as also found in the present study, has been attributed to the inhibition of alpha-decarboxylation of glutamate. At higher pH (e.g., pH 8), GABA synthesis also decreased since

GABA-pyruvate transaminase activity could have been promoted instead (Saikusa et al., 1994). Texture Quality of GBR Hardness of GBR decreased relative to that of ordinary or unsoaked brown rice in both varieties (Table 6).

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An Improved Process for High Quality and Nutrition of Brown Rice Production (a) 160

KDML 105

3

12 24 12 24 12 24 12 24

4 6 8

Control (unsoaked) Chainat 1

3

12 24 12 24 12 24 12 24

4 6 8

Hardness (kg force)1 0.47±0.05 0.38±0.03 0.49±0.02 0.47±0.04 0.58±0.03 0.55±0.04 0.52±0.03 0.51±0.04

cd d cd cd bc bc c c

0.65±0.04 b 0.62±0.04 0.60±0.06 0.70±0.01 0.66±0.02 0.76±0.09 0.73±0.04 0.71±0.01 0.71±0.07

bc bc b bc ab b b b

Control (unsoaked)

0.84±0.08 a

F-test C.V. (%)

** 2.11

1

Means±standard error. Means in a column followed by the same letter are not significantly different based on DMRT, 5%. **significant (p  0.01).

Table 7. Hardness of GBR of KDML 105 and Chainat 1 varieties at optimum water soaking conditions (pH 6, temperature of 35  C and soaking time of 24 h) with and without aeration. Rice variety

Water soaking condition

KDML 105

Optimum, without aeration Optimum, with aeration Control (unsoaked) Optimum, without aeration Optimum, with aeration Control (unsoaked) F-test C.V. (%)

Chainat 1

Hardness (kg force)1 0.55±0.04 0.58±0.04 0.65±0.04 0.73±0.04 0.73±0.04 0.84±0.08 ** 2.25

d cd bc b b a

1

Means±standard error. Means in a column followed by the same letter are not significantly different based on DMRT, 5%. **significant (p  0.01).

The magnitude of decrease in hardness was generally higher at lower water pH and longer soaking time. Aeration during water soaking did not greatly affect the texture of GBR of both varieties (Table 7). Between the two varieties, Chainat 1 GBR and ordinary grains had harder texture than KDML 105 variety. In addition to improved nutritional quality, GBR of both varieties had softer texture than the ordinary grains. KDML 105 brown rice (control) has relatively lower values of peak, breakdown, set back and final viscosities than Chainat 1 brown rice (control) due to much higher

80

100 80

60

60

40

40 20

20 0

Temperature (°C)

Soaking time (h)

Viscosity (RVU)

pH

100

120

0 0

5 Time (min)

10

(b) 160 140 120 100 80 60 40 20 0

120 100

Viscosity (RVU)

Water soaking condition Rice variety

120

140

80 60 40 20

Temperature (°C)

Table 6. Hardness of GBR of KDML 105 and Chainat 1 varieties as affected by water pH and soaking time at water temperature of 35  C.

0 0

5 Time (min)

10

Figure 5. Viscosity profiles of (a) KDML 105 and (b) Chainat 1 germinated rice flours determined by a Rapid Visco Analyser affected by water pH and soaking time at water temperature of 35  C. Control pH 3 12 h pH 3 24 h pH 4 12 h pH 4 24 h pH 6 12 h pH 6 24 h pH 8 12 h pH 8 24 h Temperature. amylose content of Chainat 1 than KDML 105 (Figures 5a and 5b). The pasting temperature was determined by using a GBR sample after the production of the germinated brown rice as a subject in accordance with a Rapid Visco Analyser. Compared with unsoaked brown rice of both varieties, as a control, GBR after production at the concentration tested resulted in a significant decrease in values of these parameters (peak, breakdown, set back and final viscosities), whereas the pasting temperatures were unaffected by germination production (Figure 5). The pasting temperature of GBR KDML 105 varied from 63.72  C to 63.86  C but the pasting temperature of GBR Chainat 1 varied from 79.75  C to 79.90  C. However, there was no significant difference between pasting temperature of rice flours from nongermination and germination brown rice. Set back value is a measure of retrogradation tendency, which appears to be related to the structure of amylose and amylopectin molecules tend to be retrograded rapidly (Akingbala and Rooney, 1987). The low set back shown for GBR flours was related to the low values of amylose after soaking at 24 h (Tables 1 and 2) of high molecular weight. It was a result of polysaccharide hydrolysis during germination. The hydrolysis of starch also contributed to the lower carbohydrate content of soaked samples (Table 5). Decrease in

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setback of starch paste from GBR flours was consistent with a decreased in cooked rice hardness in this study. Brown rice undergoes germination, and its texture became soft to a certain extent owing to the physiological activity of brown rice itself and activities of various endogeneous enzyme such as amylase (Kim et al., 2004). The good characteristics of starches by these germinating processes have shown the improvement of brown rice texture. Previous studies also found the improvement of organoleptic quality of GBR due to grain softening (Kim et al., 2004; Ohtsubo et al., 2005).

CONCLUSIONS Germination resulted in significant reduction in phytic acid. Phytic acid content in germinating seed decreased with increasing in germination time. However, germination could improve the nutritional quality of rice (KDML 105 and Chainat 1) in GABA content, protein and lipid contents. Optimal condition was achieved in soaking rice in water with pH 6 and temperature of 35  C for 24 h. Supplemental aeration during water soaking decreased GABA, protein and lipid contents but increased vitamin B1 and IP6 contents of two rice varieties. Cooked GBR of KDML 105 and Chainat 1 had softer texture than cooked ordinary brown rice.

REFERENCES AACC (2000). Approved Methods of the American Association of Cereal Chemists. Saint Paul, MN: American Association of Cereal Chemists. Abe T., Kurozumi Y., Yao W.B. and Ubuka T. (1998). Highperformance liquid chromatography determination of baminoisobutyric acid and g-aminobutyric acid in tissue extracts and urine of normal and (aminooxy) acetate-treated rats. Journal of Chromatography B 712: 43—49. Agte V.V. and Sandhana R.J. (1997). Effect of traditional food processing on phytate degradation of wheat and millets. Journal of Agricultural and Food Chemistry 45: 1659—1661. Akingbala J.O. and Rooney L.W. (1987). Paste properties of sorghum flour and starches. Journal of Food Processing and Preservation 11: 13—24. AOAC (1990). Official Methods of Analysis, 15th edn. Washington, DC: Association of Official Analytical Chemists. Aurisano N., Bertani A. and Reggiani R. (1995). Anaerobic accumulation of 4-aminobutyrate in rice seedlings; causes and significance. Phytochemistry 38: 1147—1150. Badau M.H., Nkama I. and Jideani I.A. (2005). Phytic acid content and hydrochloric acid extractability of minerals in pearl millet as effected by germination time and cultivar. Food Chemistry 92: 425—435. Batifoulier F., Verny M.A., Chanliaud E., Remesy C. and Demigne C. (2006). Variability of B vitamin concentrations in wheat grain, milling fractions and bread products. European Journal of Agronomy 25: 163—169. Begley P.T. (1996). The biosynthesis and degradation of thiamin (vitamin B1). Natural Product Reports 13: 177—185. Belanger F.C., Leustek T., Chu B. and Kriz A.L. (1995). Evidence for the thiamine biosynthetic pathway in higher-plant plastids and its development regulation. Plant Molecular Biology 29: 809—821.

 Bilgicli N. and Ibanoglu S. (2007). Effect of wheat germ and wheat bran on the fermentation activity, phytic acid content and colour of tarhana, a wheat flour—yoghurt mixture. Journal of Food Engineering 78: 681—686. Cahoon R.E. and Tingey S.V. (2006). Phytic acid biosynthesis enzymes. US Patent 7,102,058 B2. Golda A., Szydrowski P., Ostrowska K.K., Kozik A. and Rapalakozik M. (2004). Thiamin binding and metabolism in germinating seeds of selected cereals and legumes. Plant Physiology and Biochemistry 42: 187—195. Hiromichi A., Tomomi S., Hiroto S., Aya M., Mitsuo K., Sachiyuki T., Sachiko S., Keiko T. and Kenichi I. (2003). Germinated brown rice. US Patent 6,630,193. Juliano B.O. (1971). A simplified assay for milled-rice amylose. Cereal Science Today 16: 334—340. Kim S.Y., Park J. and Byun S.J. (2004). Method for preparing germinated brown rice having improved texture and cookability without microbial contamination and a germinated brown rice obtained therefore. US Patent 0105921 A1. Komatsuzaki N., Tsukahara K., Toyoshima H., Suzuki T., Shimizu N. and Kimura T. (2005). Effect of soaking and gaseous treatment on GABA content in germinated brown rice. Journal of Food Engineering 78: 556—560. Leonardi R., Fairhurst S.A., Kriek M., Lowe D.J. and Roach P.L. (2003). Thiamine biosynthesis in Escherichia coli: Isolation and initial characterization of the ThiGH complex. FEBS Letters 539: 95—99. Liu L.L., Zhai H.Q. and Wan J.M. (2005). Accumulation of g-aminobutyric acid in giant-embryo rice grain in relation to glutamate decarboxylase activity and its gene expression during water soaking. Cereal Chemistry 82: 191—196. Liu S., Zhang Z., Liu Q., Luo H. and Zheng W. (2002). Spectrophotometric determination of vitamin B1 in a pharmaceutical formulation using triphenylmethane acid. Journal of Pharamaceutical and Biomedical Analysis 30: 685—694. Maeda H., Nemoto H., Iida S., Ishii T., Nakagawa N., Hoshino T., Sakai M., Okamo M., Shinoda H. and Yoshida T. (2001). A new rice variety with giant embryos, Haiminori. Breeding Science 51: 211—213. Mendenhall W. and Sincich T. (1996). A Second Course in Statistics: Regression Analysis. New Jersey: Prentice Hall Inc., p. 899. Nicolas B. and Hillel F. (2004). GABA in plants: Just a metabolite? Trends in Plant Science 9: 110—115. Oatway L., Vasanthan T. and James H. (2001). Phytic acid. Food Reviews International 17: 419—431. Ohtsubo K., Suzuki K., Yasui Y. and Kasumi T. (2005). Biofunctional component in the process pre-germinated brown rice by a twin-screw extruder. Journal of Food Composition and Analysis 18: 303—316. Oloyo R.A. (2004). Chemical and nutritional quality changes in germinating seeds of Cajanus cajan L. Food Chemistry 85: 497—502. Saikusa T., Horino T. and Mori Y. (1994). Distribution of free amino acids in the rice kernel and kernel fractions and the effect of water soaking on the distribution. Journal of Agricultural and Food Chemistry 42: 1122—1125. SAS Institute (1997). SAS User’s Guide: Statistics. North Calorina: SAS Institute, Inc., USA. Shelp B.J., Bown A.W. and McLean M.D. (1999). Metabolism and functions of gamma-aminobutyric acid. Trends in Plant Science 4: 446—452. Soon S.J. and Lee S.H. (1999). Method for germinating dehulled brown rice. US Patent 5,862,627. Sousa C.A.F. and Sodek L. (2002). The metabolic response of plants to oxygen deficiency. Brazilian Journal of Plant Physiology 14: 83—94. StatSoft’s Ltd (1995). Statistica, Version 5.0, USA. Zhang L., Hu P., Tang S., Zhao H. and Wu D. (2005). Comparative studies on major nutritional components of rice with a giant embryo and a normal embryo. Journal of Food Biochemistry 29: 653—661.

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Food Science and Technology International http://fst.sagepub.com/

Temperature and Ultra Low Oxygen Effects and Involvement of Ethylene in Chilling Injury of 'Rojo Brillante' Persimmon Fruit B. Orihuel-Iranzo, M. Miranda, L. Zacarías and M.T. Lafuente Food Science and Technology International 2010 16: 159 DOI: 10.1177/1082013209353221 The online version of this article can be found at: http://fst.sagepub.com/content/16/2/159

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Temperature and Ultra Low Oxygen Effects and Involvement of Ethylene in Chilling Injury of ‘Rojo Brillante’ Persimmon Fruit B. Orihuel-Iranzo,1,* M. Miranda,1 L. Zacarı´ as2 and M.T. Lafuente2 1

2

Anecoop S. Coop., Monforte 1 Entlo. 46010 Valencia, Spain Instituto de Agroquı´mica y Tecnologı´a de Alimentos (IATA), Consejo Superior de Investigaciones Cientı´ficas (CSIC), PO Box 73, 46100 Burjasot, Valencia, Spain The effects of storage temperature, inhibition of ethylene action by treatment with 1-methylcyclopropene (1-MCP) and ultra low oxygen (ULO) atmosphere on chilling injury (CI), fruit firmness and ethylene production in the astringent ‘Rojo Brillante’ persimmon fruit were investigated. CI symptoms were manifested as a very dramatic loss of firmness after fruit transfer from cold storage to shelf-life conditions (18  C). During cold storage, fruit softening appeared more rapidly in fruit stored at the intermediate temperature of 10  C than at 1 C or 14.5  C. Ethylene production increased with storage time at the chilling temperature (1  C) but a sharp increase took place upon fruit transfer from 1  C to ambient temperature. This ethylene increase was accompanied by a loss of fruit firmness associated with chilling damage development. A pre-treatment with the competitive inhibitor of ethylene action 1-MCP, at 1 mL/L, reduced firmness loss and mitigated CI damage but considerably increased ethylene production in fruit transferred to shelf-life conditions after a prolonged cold storage period. Collectively, these results suggest a role of ethylene in the reduction of flesh firmness and consequently in the induction of CI in persimmon fruit. Moreover, ethylene exerts a negative feedback regulation of cold-induced ethylene biosynthesis. Storage of ‘Rojo Brillante’ persimmon fruit under ULO (1.3—1.8% O2, v/v) atmosphere did not affect the incidence of CI but reduced fruit astringency, suggesting that ULO may be an alternative postharvest storage system for ‘Rojo Brillante’ persimmon fruit. Key Words: persimmon, chilling injury, ethylene, 1-methylcyclopropene, ultra low oxygen, ‘Rojo Brillante’

INTRODUCTION Persimmon fruits are prone to develop chilling injury (CI) when stored at temperatures below 15  C (MacRae, 1987; Ben-Arie and Zutkhi, 1992; Collins and Tisdell, 1995, 1996; Woolf et al., 1997; Crisosto et al., 2001). CI in persimmon has been mainly studied in the cv. ‘Fuyu’, which shows a number of features that are also typical of CI in many other fruits, including the manifestation of CI symptoms after transfer from cold storage to room temperature (MacRae, 1987; Ben-Arie and Zutkhi, 1992; Collins and Tisdell, 1995; Woolf et al., 1997).

*To whom correspondence should be sent (e-mail: borihuel@citrosol.com). Present address: Productos Citrosol S.A., P. Alameda s/n, 46721 Potries, Valencia, Spain. Received 4 February 2009; revised 30 March 2009. Food Sci Tech Int 2010;16(2):0159–9  SAGE Publications 2010 Los Angeles, London, New Delhi and Singapore ISSN: 1082-0132 DOI: 10.1177/1082013209353221

Sensitivity to CI may vary from season to season, within growing areas and with the ripening stage of the fruit at harvest. In addition, storage at moderate cold storage temperatures, around 5  C, has been shown to be more detrimental than lower temperatures (MacRae, 1987; Ben-Arie and Zutkhi, 1992; Collins and Tisdell, 1995; Crisosto et al., 2001). In fruits of different persimmon varieties, either astringent or nonastringent, reduction of pulp firmness and consequent fruit softening with internal browning are the main symptoms of CI (MacRae, 1987; Ben-Arie and Zutkhi, 1992; Woolf et al., 1997; Salvador et al., 2004a). ‘Rojo Brillante’ is an astringent persimmon cultivar originated in La Ribera del Xu´quer (Valencia, Spain), which is highly demanded because of its excellent nutritional and organoleptic qualities. Current postharvest practices and handling of ‘Rojo Brillante’ fruit in Spain include the removal of astringency by ethanol or CO2 treatments and subsequent storage at low temperatures. However, fruit storage at temperatures below 8  C generates CI, especially after fruit transfer to shelf-life conditions at room temperatures (Arnal and Del Rı´ o, 2004a; Salvador et al., 2004a). In ‘Fuyu’ persimmon it

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has been demonstrated that there is a dramatic increase in ethylene production in chilled fruits and that the increase is well related to the level of CI damage (MacRae, 1987; Woolf et al., 1997). Furthermore, treatment with the ethylene action inhibitor 1-methylcyclopropene (1-MCP), either before or after cold storage, reduced the loss of fruit firmness associated with the development of CI injury symptoms in ‘Rojo Brillante’ persimmon, without affecting other fruit quality parameters (Salvador et al., 2004b, Besada et al., 2008). These results clearly indicate that ethylene plays an important role in the development of CI in ‘Rojo Brillante’ persimmon fruit, but the precise mechanism by which ethylene production is coordinated with the manifestation of CI symptoms after simulation of shelf-life in chilled fruits is poorly understood. Furthermore, the effect of 1-MCP on chilling-induced ethylene production in persimmon fruits is still unknown. The objective of this study was to better understand the role of fruit ethylene production in the CI response of ‘Rojo Brillante persimmon and to evaluate the effect of storage temperature. The effect of ultra low oxygen (ULO), as an alternative storage technology, on CI and other quality characteristics of ‘Rojo Brillante’ persimmon fruit was also investigated.

MATERIALS AND METHODS Plant Material and Storage Conditions

production. Other replicate samples were transferred to 18  C for 3 days to simulate a shelf-life period. Removal of astringency was performed by maintaining the fruit in containers with an atmosphere of 95% CO2 (v/v) for 24 h at room temperature. The different experiments were repeated at least twice in two different seasons, and although the results were quantitatively different, the same patterns of changes and differences were consistently obtained. Methods Treatment of Persimmon Fruit with 1-MCP In these experiments, ‘Rojo Brillante’ persimmon fruits with a yellow-orange skin color, harvested in the first week of November were used. After selection by uniformity in size and absence of visual defects, fruits were randomized in four replicates of 50 fruits each per storage temperature or treatment. 1-MCP was applied in 150 L sealed containers in which fruits were exposed to an atmosphere containing 1 mL/L 1-MCP (Rohm and Hass Co). The appropriate amount of 1-MCP powder to generate the desired concentration in each chamber was placed in a sealed tube and 3 mL of warm water was injected through a septum. The tube was shaken and then opened inside the container just before its closure. The persimmon fruits were exposed to 1-MCP for 16 h at 20  C. The experiment was repeated three times in three consecutive seasons. Storage of Persimmon Fruit in ULO Atmospheres

‘Rojo Brillante’ persimmon fruits (Diospyros kaki L.) were harvested at yellow-orange color stage from commercial orchards located at Ribera del Xu´quer (Valencia, Spain) in the second week of November. Fruits were delivered to the laboratory, for selection to eliminate fruits with visible defects that could accelerate firmness loss, for randomization and then stored in three cold storage rooms of approximately 9 m3 capacity set at 1  C, 10  C and 14.5  C, respectively. In experiments in which astringent fruits were used, they were cold stored at the different temperatures, within 4—8 h after harvesting. Otherwise, the fruits were maintained for 4 days at 14.5  C, astringency was removed and they were cold stored together with the astringent fruits for the same periods of time. Relative humidity during storage was maintained at 90%. To avoid ethylene accumulation in the storage rooms, a Conserver 21 ethylene absorber cartridge (Agrotech 2000 SL, Madrid, Spain) was placed in each room and the rooms were continuously ventilated. At different periods of time during storage, ethylene was measured inside the storage rooms to make sure it had not accumulated. After different storage periods, replicate fruit samples were taken for analysis of fruit quality or ethylene

To evaluate the effect of fruit storage in ULO atmosphere, ‘Rojo Brillante’ persimmon fruits were harvested at the yellow-orange color stage orchard in the second week of November. Fruits were taken to the laboratory where, after selection to eliminate fruits with visible defects that could accelerate firmness loss, they were randomized in experimental batches of 40 fruits each. Half of the fruits were stored in ULO at 0—1  C and at 14.5  C, and the other half were cold stored as air controls at the same temperatures, and in the same cold rooms, for either 10 or 24 days. ULO conditions were created in four drums of 100 L each by connecting them to a N2 generating system, which maintained O2 concentration throughout the experiment between 1.3 and 1.8% (v/v). O2 concentrations were measured daily with a digital Draeger unit (accuracy ± 0.3% v/v), Draeger, Germany. The ULO drums were closed after 24 h to ensure that pulp temperatures were reached before the initiation of the experiment. In the cold room set at 14.5  C a Conserver 21 ethylene absorber cartridge (Agrotech 2000 SL, Madrid, Spain) was placed. Fruits were taken from each room and ULO drum to perform the different determinations at the reported periods, and transferred for 3 days to 18  C.

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Temperature and Ultra Low Oxygen Effects on Persimmon Fruit

Two experiments were performed with fruits from two different orchards in two different seasons. Analytical Determinations Fruit Firmness Fruit firmness was determined with either an Effegi penetrometer model FT011 equipped with an 8 mm diameter plunger, or with a Penefel digital penetrometer (Copa Technologie, CTIFL, France), also equipped with an 8 mm diameter plunger. Two measurements per fruit were performed, one in each cheek of the fruit and approximately at the fruit equator, after peel removal. Fruit firmness of a fruit is the average of both measurements. Results are expressed in Newtons (N) and each datum is the mean ± SE of at least six fruits. Peel Color The color of the peel was measured using a Minolta 508i colorimeter and an 8 mm diameter reader. For each fruit, color was measured, as the Hunter parameters L, a and b, on three positions around the equatorial surface of the fruit. Data are expressed as the a/b ratio of six fruits ± SE. Ethylene Production Ethylene production was determined in persimmon fruits, treated and nontreated with 1-MCP, at both the storage temperature and after transferring the fruit to 18  C to simulate shelf-life conditions. Fruits were sealed in 1.7 L glass jars for 2 h, and after that period 1 mL air samples were withdrawn with a hypodermic syringe and their ethylene content determined in an Autosample Perkin-Elmer gas chromatograph equipped with a 1 m  2 mm activated alumina column (80/100 mesh) and a flame ionization detector. Nitrogen was the carrier gas and the temperature of the column was maintained at 140  C. Three replicate samples of three fruits each were used for ethylene determination. Results are expressed as nL/g h ± SE. Tannin Content The percentage of soluble tannins in relation to the fresh fruit weight was determined semi-quantitatively by determining the astringency of the fruit with filter paper previously impregnated with 5% w/v Cl3Fe (Taira, 1995). The relationship between fruit astringency and tannin content determined by the Folin-Denis method (Taira, 1995) for the ‘Rojo Brillante’ persimmon had been experimentally established previously (OrihuelIranzo et al., 2003).

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Statistical Analysis In some cases, the data were subjected to analysis of variance and means were compared by the least significant difference test tested at a significance level of 0.05. To analyze the effect of storage temperature on firmness evolution, linear regressions were performed and the differences in the slopes tested by the t-test (p < 0.05) of the fitted interaction regression model. Statistical analysis was performed using S-PLUS Version 7.0 statistical software (Insightful Corp., Seattle, WA, USA).

RESULTS AND DISCUSSION Effect of Storage Temperatures on Fruit Quality in Astringent and Non-astringent ‘Rojo Brillante’ Persimmon Fruits Astringent and nonastringent (astringency removed by previous treatment with CO2) ‘Rojo Brillante’ persimmon fruits were cold stored at 1  C, 10  C and 14.5  C for different periods of time. At any of these temperatures, fruit softened during the cold storage period and the loss of flesh firmness of both astringent and nonastringent fruit could be fitted to a first order line with high correlation coefficients (R2) ranging from 0.97 to 0.76 (Figures 1a and 1b). The adjustment of the data to linear functions allows comparison of the rate of softening at the three storage temperatures, showing that it was always significantly slower at 1  C, and higher at 10  C, than at 14.5  C. However, no significant (p < 0.05) difference in the rate of firmness loss was found between the two types (astringent and nonastringent) of fruit when stored at the same temperature (Figures 1a and 1b). In a similar experiment, we also verified that, after only 15 days of storage, fruit firmness at 5.5  C was lower than at 1  C and 14.5  C (Sureda, 2000; Orihuel-Iranzo, 2006). It seems, therefore, that the higher rates of flesh softening for the ‘Rojo Brillante’ persimmon take place at intermediate cold storage temperatures such as 10  C or 5.5  C, and that this behavior is the same for astringent and nonastringent fruit. These results are similar to and confirm previous results showing that flesh firmness of ‘Rojo Brillante’ persimmon fruit was lower at 8  C than at 1  C and 15  C after 34 days of cold storage (Arnal and Del Rı´ o, 2004b). Our results also indicated that the ‘Rojo Brillante’ persimmon cultivar behaves similarly to other persimmon cultivars. Thus, ‘Fuyu’ and ‘Suruga’ persimmon flesh firmness was higher at 20  C than at 10  C, and higher at 10  C than at 5  C (MacRae, 1987; Collins and Tisdell, 1995). This response of persimmon fruit to storage temperature is different than that of many other fruits, both sensitive and nonsensitive to chilling. The rate of fruit softening usually increases with temperature (Mitchell and Kader, 1989; Eccher Zerbini et al., 2006)

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because higher temperatures increase pectin solubilization and degradation of different cell wall components by the action of various cell wall-degrading enzymes. In persimmon fruit, this does not appear to be the case, and we therefore hypothesized that in this fruit, and particularly in the ‘Rojo Brillante’ cultivar, the softening process may be affected by both the CI sensitivity of the fruit and the storage temperature. Thus, loss of firmness was higher at 10 than at 14.5  C but also higher than at 1  C before transfer of fruits to 18  C (Figure 2). Also, our results showed that whether the tannins are in soluble or insoluble form does not influence the softening rate. Although both insolubilization of tannins and fruit softening may occur simultaneously during fruit maturation (Kitagawa and Glucina, 1984), in our experimental conditions these processes appear to be independent. This finding is in agreement with the accepted fact that tannin insolubilization can take place through several mechanisms, polymerization

induced via acetaldehyde or by binding of tannins to cell wall fragments (Taira and Ono, 1997). We insolubilized tannins by a CO2 treatment that produces acetaldehyde (Taira, 1995; Taira and Ono, 1997), a mechanism that bears no relation to fruit softening. Color changes during fruit storage at 1  C were much slower than at 10  C and 14.5  C. Thus, an increase in color from an a/b ratio of approximately 0.8, which corresponds to a yellow color, to nearly 2, corresponding to red color, was observed in astringent fruit stored for 58 days at 10  C and 14.5  C. In fruit stored for the same period at 1  C, the a/b ratio increased only to 1.2, corresponding to pale orange color (data not shown). As in the case of fruit softening, prior exposure of the fruit to CO2 treatment for astringency removal was not relevant to the evolution of fruit color. At each temperature, the color evolution was very similar for both astringent and nonastringent fruits (data not shown).

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Figure 1. Firmness loss of ‘Rojo Brillante’ persimmon fruits during cold storage at 1  C, 10  C and 14.5  C and 90% HR. Each point in the graph is the average of 6 determinations. Rates of firmness loss are obtained by adjusting to linear functions by linear regression. (a) Astringent fruit; (*) T ¼ 14.5  C, y ¼ 42.81—0.53x, R2 ¼ 0.97; (#) T ¼ 10  C, y ¼ 44.50—0.70x, R2 ¼ 0.94; (m) T ¼ 1  C, y ¼ 42.81—0.53x, R2 ¼ 0.97. (b) Nonastringent fruit; (*) y ¼ 39.9—0.33x, R2 ¼ 0.76; (#) y ¼ 37.73—0.57x, R2 ¼ 0.95; (m) y ¼ 42.83—0.93x, R2 ¼ 0.94.

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Figure 2. Firmness of ‘Rojo Brillante’ persimmon fruits before (bars in black) and after (bars in white) being stored for 31 days at 1  C, 10  C, and 14.5  C and then transferred to 18  C for 3 days. Each firmness value in the graph is the average of 6 determinations. Means for each value with the same letter are not significantly different at 5% level. (a) Results for still astringent persimmon fruits. (b) Results for fruits where astringency had been removed before cold storage. (œ) 31 days, (square white) 31 þ 3 days.

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Temperature and Ultra Low Oxygen Effects on Persimmon Fruit

Chilling disorders in persimmon were clearly manifested when the fruit was transferred from the chilling to the room temperature (MacRae, 1987; Ben-Arie and Zutkhi, 1992; Woolf et al., 1997). Therefore, we examined the effect of transferring both astringent and nonastringent ‘Rojo Brillante’ fruit stored for 31 days at 1  C, 10  C or 14.5—18  C for 3 days. Loss of firmness upon transfer to 18  C was dependent on the previous storage temperature. Firmness loss was much higher in fruits previously stored at 1  C than in those stored at 10  C (Figure 2). In contrast, no significant firmness loss was observed after transferring astringent fruit from 14.5  C to 18  C and only a slight decrease occurred in nonastringent fruit. Although this decrease was statistically significant (figure 2), it is noteworthy that this behavior was not observed in other experiments. These results, therefore, reinforce the idea that the chemical state of the tannins in the fruit flesh does not affect the changes in fruit firmness that occur at the three storage temperatures tested. The textural changes appear to be associated not only with the development

50

of melting or gellification in chilling-injured fruit that is initiated in the perimeter of the rind but also in 24—72 h melting of the pulp (Figures 4a and 4b). In contrast to ‘Fuyu’ and ‘Suruga’ persimmon, the ‘Rojo Brillante’ cultivar did not develop rind staining (BenArie and Zutkhi, 1992; Clark and Forbes, 1994) as a CI symptom. The loss of fruit firmness during the shelf-life period is technologically and commercially more important than the loss during fruit storage at low temperature. In fruit stored at 1  C for more than 10 days the reduction of flesh firmness after the shelf-life period was of such magnitude (final fruit firmness of less than 10 N) that the fruits were not of commercial value (Figure 3). These results indicate that fruit firmness is a measurable manifestation of CI in ‘Rojo Brillante’ persimmon that may be provoked by only 10 days at 1  C. Therefore, it appears that ‘Rojo Brillante’ persimmon is much more sensitive to CI than the ‘Fuyu’ and ‘Suruga’ cultivars, which can withstand more than 1 month at 0  C without developing CI (MacRae, 1987; Collins and Tisdell, 1995). Effect of 1-MCP on Chilling-induced Ethylene Production and Fruit Deterioration in ‘Rojo Brillante’ Persimmon

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Figure 3. Loss of firmness of ‘Rojo Brillante’ persimmon fruits when transferred, after several storage periods at 1  C (solid line), to 18  C for 3 days (broken line). Each firmness value in the graph is the average of 6 determinations; the vertical bars represent the SE of the mean.

(a)

The rate of ethylene production of freshly harvested ‘Rojo Brillante’ persimmon fruit (and of fruit stored for 7 days at 20  C) was very low (Orihuel-Iranzo, 2006), but persimmon fruits of this (Orihuel-Iranzo, 2006) and other cultivars (Kitagawa and Glucina, 1984; Nakano et al., 2002; Kubo et al., 2003; Nakano et al., 2003a, b) are very sensitive to ethylene. As ethylene production may increase in response to CI in many fruits (Lyons and Breidenbach, 1987) and it has been demonstrated to regulate changes in cell wall-degrading enzymes in persimmon (Kubo et al., 2003, Xu et al., 2004), we hypothesized that endogenous ethylene may play a role in the onset of the rapid softening, which originates the

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Figure 4. Incipient and developed symptoms of CI in ‘Rojo Brillante’ persimmon fruits. Symptoms are initiated in the inner perimeter of the fruit (a) and in just 24—72 h melting of the pulp is evident (b). Downloaded from fst.sagepub.com at HINARI on February 22, 2011

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Figure 5. Ethylene production in ‘Rojo Brillante’ persimmon when transferred for 24, 48 and 96 h to 18  C after 25 days of cold storage at 1  C and 14.5  C, and after being treated or not with 1 mL/L 1-MCP when stored at 1  C. Each value in the graph is the average of 3 determinations; the vertical bars represent the SE. (¨) T ¼ 14.5  C, (#) T ¼ 1  C, (m) 1 MCP þ 1  C.

‘collapse’ of the fruit, during the shelf-life period at nonchilling temperature. Ethylene production of ‘Rojo Brillante’ fruit stored for 25 days at 1  C (Figure 5) was still very low (around 0.02 nL/gh) but increased markedly and transiently when the fruit was transferred from 1  C to 18  C, reaching a maximum of about 0.6 nL/gh by 48 h, which is in agreement with previous results found in ‘Fuyu’ persimmon fruit (MacRae, 1987; Woolf et al., 1997). In contrast, ethylene production in fruit maintained at the nonchilling temperature (14.5  C) did not increase during the shelf-life period at 18  C and was always lower than that of fruit stored at the chilling temperature. We also studied the effect of applying 1MCP, a competitive inhibitor of ethylene action that has been shown to alleviate CI in ‘Rojo Brillante’ persimmon (Salvador et al., 2004b, Besada et al., 2008), on both the rate of chilling-induced ethylene production and the CI manifestations. Applying 1 mL/L of 1-MCP transiently increased the rise in ethylene production at the fruit transfer from low temperature (1  C) to 18  C in spite of reducing the fruit collapse (Figure 7). Thus, the maximum ethylene production was reached in the 1MCP-treated fruit 1 day after transfer and was slightly higher (0.8 nL/gh) than that reached 2 days after transfer in the nontreated fruit (Figure 5). To further confirm this effect, we examined the effect of 1-MCP on ethylene production in fruit exposed to 0  C for 20, 40 and 60 days and then transferred to 18  C for 24 h. The rate of ethylene production upon fruit transfer to 18  C increased with the duration of the cold storage period in both nontreated and 1-MCP-treated fruit, ethylene production being always higher in fruit treated with 1MCP prior to cold storage (Figure 6). The greatest differences were found in fruit stored for 40 days at 0  C.

20

40 Time (days at 0 °C)

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Figure 6. Effects of 1-MCP treatment and time of storage at 0  C on ethylene production in ‘Rojo Brillante’ persimmon measured 24 h after transferring the fruit to 18  C. White bars correspond to fruit treated with 0.75 mL/L 1-MCP and black bars to control fruits not treated with 1-MCP. Each value in the graph is the average of three determinations; the vertical bars represent the SE.

After this period, ethylene production in 1-MCP-treated fruit was almost three times higher than that of the nontreated fruit. In addition, we also observed that treating ‘Rojo Brillante’ persimmon fruit with 1-MCP prior to cold storage at 1  C reduced CI, eliminated flesh gellification and there was a significant retention of fruit firmness when transferred to room temperature (Figure 7). Collectively, these results suggest that the increase in ethylene production upon transfer from chilling to nonchilling temperature is part of the fruit chilling response since it did not occur at nonchilling temperatures, and it also appears to be responsible for the collapse of the fruit. The fact that the rate of softening at 1  C is much lower than the rate of softening at 10  C (Figure 3) seems to be only a result of the fact that at 1  C the biochemical events leading to the transient peak in ethylene production cannot take place. The data also indicated an involvement of negative feedback regulation in chilling-induced ethylene production in persimmon fruit. Negative feedback control of ethylene production has been recognized as occurring in a variety of fruits and plant tissues (Yang and Hoffman, 1984; Lafuente et al., 2001; Owino et al., 2002; Zheng et al., 2006). For an effective inhibition of ethylene perception, 1-MCP should shut down the ethylene feedback system and an increase in ethylene production would be expected. Thus, the 1-MCP concentrations used in the present study appeared to be effective in inhibiting ethylene action in ‘Rojo Brillante’ persimmon fruit since they enhanced the cold-induced ethylene production

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Figure 7. Effects of 1-MCP treatment on evolution of firmness of ‘Rojo Brillante’ persimmon fruits when transferred, after 25 days of cold storage (CS) at 1  C to 18  C for 3 and 7 days of shelf-life (SL). Fruits were treated with 1 mL/L 1-MCP (white bars) or not treated (black bars) Each firmness value in the graph is the average of 12 determinations, and the vertical lines represent the SE. Means for each value with the same letter are not significantly different at 5% level. The firmness of the control fruits after 7 days at 18  C could not be measured because of their extreme softness. (Figures 5 and 6). Therefore, considering that 1-MCP blocks ethylene receptors in plant cells (Prange and DeLong, 2003), the rise in ethylene observed in the 1-MCP treated ‘Rojo Brillante’ fruit is probably not favoring the collapse of the fruit associated with CI. This finding also has an important practical consequence: 1-MCP treated and nontreated ‘Rojo Brillante’ fruits should not be stored together, because the higher rates of ethylene production in the 1-MCP treated fruits will increase softening in the nontreated fruits. We also found that the 1-MCP treatment did not alter the evolution of other fruit quality processes such as weight loss, in agreement with Salvador et al. (2004a), also astringency and decay (data not shown), as observed in ‘Triumph’ persimmon (Tsviling et al., 2003). These results suggest that the low levels of ethylene normally evolving in this fruit may be sufficient to originate cell wall modifications leading to fruit softening without affecting other physiological mechanisms involved in other important fruit quality parameters, and they further confirm the high potential of the inhibitor of ethylene action, 1-MCP, in reducing chilling symptoms and extending the storage period of chilling sensitive persimmon cultivars without inducing deleterious effects on fruit quality.

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Figure 8. Effects of ULO storage on evolution of firmness of ‘Rojo Brillante’ persimmon fruits at 1  C and 14.5  C. After 10 and 24 days of cold storage (CS), data in black bars, the fruits were transferred for 3 days to 18  C in shelf-life testing to perform the final determinations (gray bars). Each firmness value in the graph is the average of 10 determinations, and the vertical lines represent the SE. Means for each value with the same letter are not significantly different at 5% level.

Effects of Ultra Low Oxygen Storage on Fruit Quality in ‘Rojo Brillante’ Persimmon The effect of ULO atmospheres on the quality of ‘Rojo Brillante’ persimmon fruit during storage at 1  C and 14.5  C for up to 24 days was evaluated. Figure 8 displays changes in flesh firmness in fruits maintained under ULO (1.3—1.8% O2, v/v) or regular atmospheric conditions and the additional shelf-life simulation period. The results indicated that ULO cold storage did not alter ‘Rojo Brillante’ persimmon flesh firmness changes either at 14.5  C or at 1  C. Furthermore, the very rapid softening occurring as a consequence of CI when the fruits were transferred from 1  C to 18  C took place equally in fruit stored under the ULO atmosphere. However, ULO storage had a positive effect on reducing fruit astringency, though the removal of astringency by very low O2 concentrations depended on the storage temperature. Changes in soluble tannins during fruit atmospheric storage at 14.5  C and 1  C were similar, though the loss of astringency during the shelf-life period was higher in fruit previously held under atmospheric conditions at 1  C (Figure 9). This effect may be

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Figure 9. Effects of ULO storage on the evolution of the soluble tannins content (% of fresh weight) of ‘Rojo Brillante’ persimmon fruits at 1  C and 14.5  C. After 10 and 24 days of CS, data in black bars, the fruits were transferred for 3 days to 18  C in shelf-life testing to perform the final determinations (gray bars). Each value in the graph is the average of 10 determinations, and the vertical lines represent the SE. Means for each value with the same letter is not significantly different at 5% level. related to the increase in ethylene occurring in coldstored fruit transferred to 18  C (Figure 5). ULO storage had a marked effect on fruit astringency in fruit kept at 14.5  C, as ULO considerably favored the conversion of tannins from soluble to insoluble during storage at this temperature and during the additional shelf-life simulation period. Thus, after 24 days of storage, the percentage of soluble tannins for fruit held under ULO conditions was 0.15%, while the percentage for the control fruit was 0.5%. Moreover, the reduction in fruit astringency after transfer to 18  C continued in the fruit stored under ULO but not in the fruit under normal atmosphere. At the end of the storage and shelf-life period, the percentage of soluble tannins was 0.05%, which is below the astringency detection threshold (0.10%) for consumers (Kato, 1984; Ben-Arie et al., 1991). These results are in agreement with the fact that astringency may also be removed in persimmon fruit with high N2 concentrations at temperatures close to 25  C (Eaks, 1967; Kitagawa and Glucina, 1984; Taira, 1995; Arnal and Del Rı´ o, 2003). These results and the fact that neither decay nor ethylene accumulation was detected in the ULO storage at 14.5  C during the 24 day period (data not shown) revealed that, although ULO does not reduce chilling-induced collapse

in ‘Rojo Brillante’ persimmon, it has a high potential for doing in the case of storage of ‘Rojo Brillante’ persimmon at the nonchilling temperature of 14.5  C. In addition, these conditions may also be used as an alternative system for removing fruit astringency in the commercial post-harvest handling of this astringent cultivar. Additionally, these results relate to the several mechanisms that reduce astringency in persimmon fruit. While the reduction in soluble tannins in ULO stored fruit at 14.5  C may be due to the tannin polymerization induced by acetaldehyde accumulation (Taira, 1995), the reduction in soluble tannins in the control fruit at 1  C may be due to the fruit softening and tissue collapse that CI causes via the binding of soluble tannins to cell wall fragments degraded by softening (Taira and Ono, 1997).

CONCLUSIONS From the overall results obtained in this work we can conclude that: (1) Fruit softening of ‘Rojo Brillante’ persimmon takes place during cold storage, this effect being higher at the chilling-threshold temperature (10  C) than at the control nonchilling temperature (14.5  C); (2) The chilling sensitivity of ‘Rojo Brillante’ persimmon appears to be very high as chilling-induced fruit firmness loss and tissue collapse are dramatic in this cultivar. Thus, only 10 to 20 days at 1  C are needed for tissue collapse to be complete upon fruit transfer to shelf-life temperature; (3) The degree of tannin polymerization does not appear to influence the rates of fruit softening occurring at the chilling and nonchilling temperatures tested; (4) 1-MCP mitigated the development of CI in spite of stimulating chillinginduced ethylene, which might be related to the fact that chilling-induced ethylene biosynthesis is subjected to negative feedback regulation in mature ‘Rojo Brillante’ fruit; (5) Storage of ‘Rojo Brillante’ persimmon fruit under ULO atmosphere does not reduce chilling-induced collapse, but at nonchilling temperatures it stimulated astringency removal and thus may be used as an alternative system in commercial post-harvest handling and storage of ‘Rojo Brillante’ persimmon fruit.

REFERENCES Arnal L. and Del Rı´ o M.A. (2003). Removing astringency by carbon dioxide and nitrogen-enriched atmospheres in persimmon fruit cv. ‘Rojo Brillante’. Journal of Food Science 68: 1516—1518. Arnal L. and Del Rı´ o M.A. (2004a). Effect of cold storage and removal astringency on quality of persimmon fruit (Diospyros kaki, L.) cv. ‘Rojo Brillante’. Food Science and Technology International 10: 179—185. Arnal L. and Del Rı´ o M.A. (2004b). Quality of persimmon fruit cv. Rojo Brillante during storage at different temperatures. Spanish Journal of Agricultural Research 2: 243—247.

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Temperature and Ultra Low Oxygen Effects on Persimmon Fruit Ben-Arie R. and Zutkhi Y. (1992). Extending the storage life of ‘Fuyu’ persimmon by modified-atmosphere packaging. HortScience 27: 811—813. Ben-Arie R., Zutkhi Y., Sonego L. and Klein J. (1991). Modified atmosphere packaging for long-term storage of astringent persimmons. Postharvest Biology Technology 1: 169—179. Besada C., Arnal L. and Salvador A. (2008). Improving storability of persimmon cv. Rojo Brillante by combined use of preharvest and postharvest treatments. Postharvest Biology Technology 50: 169—175. Clark C.J. and Forbes S.K. (1994). Nuclear magnetic resonance imaging of the development of chilling injury in ‘Fuyu’ persimmon (Diospyros kaki). New Zealand Journal of Crop and Horticultural Science 22: 209—215. Collins R.J. and Tisdell J.S. (1995). The influence of storage time and temperature on chilling injury in Fuyu and Suruga persimmon (Diospyros kaki L.) grown in subtropical Australia. Postharvest Biology Technology 6: 149—157. Collins R.J. and Tisdell J.S. (1996). Predicting the storability of Suruga persimmons. Postharvest Biology Technology 7: 351—357. Crisosto C.H., Mitcham E.J. and Kader A.A. (2001). Indicadores ba´sicos del manejo post-cosecha del caqui. Available at: http:// postharvest.ucdavis.edu/Produce/ProduceFacts/espanol/ Caqui.html (accessed February 1, 2010). Eaks I.L. (1967). Ripening and astringency removal in persimmon fruits. Journal of the American Society of Horticultural Science 91: 868—875. Eccher Zerbini P., Vanoli M., Grassi M., Rizzolo A., Fibiani M., Cubeddu R., Pifferi A., Spinelli L. and Torricelli A. (2006). A model for the softening of nectarines based on sorting fruit at harvest by time-resolved reflectance spectroscopy. Postharvest Biology Technology 39: 223—232. Kato K. (1984). The condition of tannin and sugar extraction, the relation of tannin concentration to astringency and the behaviour of ethanol during the de-astringency by ethanol in persimmon fruits. Journal Japanese Society Horticultural Science 53: 127—134. Kitagawa H. and Glucina P.G. (1984). Persimmon Culture in New Zealand. Wellington, New Zealand: Science Information Publishing Centre. Kubo Y., Nakano R. and Inaba A. (2003). Cloning of genes encoding cell wall modifying enzymes and their expression in persimmon fruit. Acta Horticulturae 601: 49—55. Lafuente M.T., Zacarias L., Martı´ nez-Te´llez M.A., Sa´nchez-Ballesta M.T. and Dupille E. (2001). Phenylalanine ammonia-lyase as related to ethylene in the development of chilling symptoms during cold storage of citrus fruit. Journal of Agricultural Food Chemistry 49: 6020—6025. Lyons J.M. and Breidenbach R.W. (1987). Chilling injury. In: Weichmann J. (ed.), Post Harvest Physiology of Vegetables, New York: Marcel Dekker Inc., pp. 305—326. MacRae E.A. (1987). Development of chilling injury in New Zealand grown ‘Fuyu’ persimmon during storage. New Zealand Journal of Experimental Agriculture 15: 333—344. Mitchell F.G. and Kader A.A. (1989). Factors affecting deterioration rate. In: LaRue J.H. and Scott Johnson R. (eds), Peaches, Plums, and Nectarines; Growing and Handling for Fresh Market, Oakland: University of California, Division of Agriculture and Natural Resources, pp. 165—178. Nakano R., Harima S., Kubo Y. and Inaba A. (2003b). Involvement of stress-induced Ethylene biosynthesis in fruit softening of Saijo persimmon fruit. Acta Horticulturae 601: 219—226.

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Nakano R., Inoue S., Kubo Y. and Inaba A. (2002). Water-stress induced ethylene in the calyx triggers autocatalytic ethylene production and fruit softening in ‘Tonewase’ persimmon grown in a heated plastic-house. Postharvest Biology Technology 25: 293—300. Nakano R., Ogura E., Kubo Y. and Inaba A. (2003a). Ethylene biosynthesis in detached young persimmon is initiated in calyx and modulated by water loss from the fruit. Plant Physiology 131: 276—286. Orihuel-Iranzo B. (2006). Dan˜o por frı´o en el caqui cv. ‘Rojo Brillante’, estrategias y tratamientos para su conservacio´n frigorı´fica. PhD Thesis, Universidad Polite´cnica de Valencia, Valencia, Spain. Orihuel-Iranzo B., Caus-Pertegaz J. and Planells-Balsalobre A. (2003). Characterization and measurement of astringency and tannin content in ‘Rojo Brillante’ persimmon. Acta Horticulturae 601: 227—231. Owino W.O., Nakano R., Kubo Y. and Inaba A. (2002). A differential regulation of genes encoding ethylene biosynthesis enzymes and ethylene response sensor ortholog during ripening and in response to wounding in avocados. Journal of the American Society of Horticultural Science 127: 520—527. Prange R.K. and DeLong J.M. (2003). 1-Methylcyclopropene: the ‘Magic Bullet’ for horticultural products. Chronica Horticulturae 43: 11—14. Salvador A., Arnal L., Monterde A. and Cuquerella J. (2004b). Reduction of chilling injury symptoms in persimmon fruit cv. Rojo Brillante by 1-MCP. Postharvest Biology Technology 33: 285—291. Salvador A., Cuquerella J., Martinez-Ja´vega J.M., Monterde A. and Navarro P. (2004a). 1-MCP preserves the firmness of stored persimmon Rojo Brillante. Journal of Food Science 69: 69—73. Sureda M.P. (2000). Estudios sobre el necrosamiento interno y vida commercial del Caqui Rojo Brillante. Valencia. Spain: Trabajo fin de carrera, Universidad Polite´cnica de Valencia. Taira S. (1995). Astringency in persimmon. In: Linskens H.F. and Jackson J.F. (eds), Fruit Analysis, Berlin: Springer, pp. 97—110. Taira S. and Ono M. (1997). Reduction of astringency in persimmon caused by adhesion of tannins to cell wall fragments. Acta Horticulturae 436: 235—241. Tsviling A., Nerya O., Gizis A., Sharabi-Nov A. and Ben-Arie R. (2003). Extending the shelf-life of ‘Triumph’ persimmons after storage, with 1-MCP. Acta Horticulturae 599: 53—58. Yang S.F. and Hoffman N.E. (1984). Ethylene biosynthesis and its regulation in higher plants. Annual Review of Plant Physiology 35: 155—189. Woolf A.B., Ball S., Spooner K.J., Lay-Yee M., Ferguson I.B., Watkins C.B., Gunson A. and Forbes S.K. (1997). Reduction of chilling injury in the sweet persimmon Fuyu during storage by dry air heat treatments. Postharvest Biology Technology 11: 155—164. Xu C.-G., Nakatsuka A. and Itamura H. (2004). Effects of 1-methylcyclopropene (MCP) treatment on ethylene production, softening, and activities of cell wall degrading enzymes in ‘Saijo’ persimmon fruit after removal of astringency with dry ice. Journal Japanese Society Horticultural Science 73: 184—188. Zheng Q.L., Nakatsuka A., Matsumoto T. and Itamura H. (2006). Pre-harvest nickel application to the calyx of ‘Saijo’ persimmon fruit prolongs post-harvest shelf-life. Postharvest Biology Technology 42: 98—103.

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Food Science and Technology International http://fst.sagepub.com/

Characterization of a Spray-Dried Soymilk Powder and Changes Observed During Storage G. Osthoff, A. Hugo, P. van Wyk, M. de Wit and S. Meyer Food Science and Technology International 2010 16: 169 DOI: 10.1177/1082013209353236 The online version of this article can be found at: http://fst.sagepub.com/content/16/2/169

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Characterization of a Spray-Dried Soymilk Powder and Changes Observed During Storage G. Osthoff,1,* A. Hugo,1 P. van Wyk,2 M. de Wit1 and S. Meyer1 1

Department of Microbial, Biochemical and Food Biotechnology, University of the Free State Bloemfontein, South Africa 2 Centre for Confocal and Electron Microscopy, University of the Free State, Bloemfontein South Africa

Physical characterization of a soymilk powder was carried out by electron microscopy. Chemical characterization was analyzed by proximate analysis, mineral composition by atomic absorption spectrometry, fatty acid composition by gas chromatography and protein composition by electrophoresis. The powder consists of large granules of 60—80 mm, which may be hollow, with smaller granules of 10—20 mm attached to them. Powder particles are covered by a layer of fat. During storage at 25  C fat is spreading over the surface, while at 12  C the fat is contracting. This change affected chemical stability, resulting in high level of fat oxidation when stored at 4  C or 25  C as well as a decrease in unsaturated fatty acids. Storage also affected the chemical properties of the re-constituted soymilk; the pH of a 12% soy powder suspension increased from 6.68±0.05 to 7.06±0.08 after 12 months of storage. Storage temperature did not affect the pH of the suspension and this change could also not be ascribed to protein aggregation. Key Words: soy, soymilk, powder, spray drying, okara, surface properties

INTRODUCTION Soy is highly desired for its high content of nutritious protein as well as niacin and phytochemicals. It is used in a wide range of foods such as meat products, baked goods and infant foods. Not being of animal origin, it lacks lactose and cholesterol, therefore is less of a problem with dietary disorders or intolerances (Liu, 1997). To be a convenient food ingredient, soy is available as flours, powders or isolates. A very versatile powder is spray-dried soymilk extract. The only drawback of soy is the high content of anti-nutritional factor in the form of trypsin inhibitors (Kunitz, 1945; Birk et al., 1967), which fortunately is almost completely removed during soymilk processing. Spray drying is a method applied to dry a wide variety of food extracts. The resulting powders are conveniently stored, transported and handled. The best known product is spray-dried cow’s milk. Due to the industrial importance and complexity of milk and its resulting spray-dried powder, most research of all food powders *To whom correspondence should be sent (e-mail: osthoffg.sci@ufs.ac.za). Received 21 January 2009; revised 2 April 2009. Food Sci Tech Int 2010;16(2):0169–10 ß SAGE Publications 2010 Los Angeles, London, New Delhi and Singapore ISSN: 1082-0132 DOI: 10.1177/1082013209353236

has been conducted on this food regarding the formation, structure and behavior of particles. It has been reported that milk powder particles may consist of a lactose matrix with proteins entrapped as micelles (King, 1965) or as an amorphous protein network (Kher et al., 2007) with lactose being distributed in the outer layers as amorphous crystals (Hindmarsh et al., 2007), and fat being partially incorporated in fat droplets and partially spread over inner and outer surfaces (Onwulata et al., 1996; Kim et al., 2002). Surface properties of the particles as determined by the fat coating affected the physical properties of the powder, such as wetting properties (Kim et al., 2002). This has been established by producing food models consisting of different amounts of fats and carbohydrates with cow’s milk as basis, and changing the spray drying process with all possible conditions. Although excellent research has been published on soymilk powder, the details established for cow’s milk powder has not yet been achieved. A spray drying system for soymilk was described (Perez-Munos and Flores, 1997a), and the expected particle size characterized as between 36 and 137 mm (Perez-Munos and Flores, 1997b). Variations on the preparation of soymilk before drying to improve the quality of the powder have been studied (Ang et al., 1986; Jinapong et al., 2008) and particle sizes altered by fluidized bed agglomeration (Jinapong et al., 2008). With the use of soymilk extract as food ingredient (Specialized Protein Products, Potchefstroom,

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South Africa) has developed an extraction process to produce a soymilk based on the traditional process described by Wilson (Wilson, 1995) which is spray dried. The remaining fibrous residue, or okara, is also utilized in food products. It was envisaged that the powder will also be used in foods for infants and children; so the antioxidants were omitted. Because the soy cultivars produced in South Africa differ from those used elsewhere in the world, the aim of this study was therefore to characterize the powdered product and determine changes during storage conditions. The results obtained are, however, not just of limited use to the specific powder reported here, but also to soymilk powder in general, regarding the structural composition of powder particles, the surface properties and changes to be anticipated during storage.

was removed by tapping the button against a hard object. Mounted samples were blasted with compressed air and sputter coated with gold with a Polaran E5000 Sputter Coater (Biorad, UK), three times for 45 s and again three times for 60 s. Photographs were taken at different enlargements for analysis. Broken granules were obtained by grinding soymilk powder with a mortar and pestle. A hexane washed powder of soymilk powder was prepared by dispersing 2 g soymilk powder in 25 mL hexane and shaking for 1 h at room temperature. The sample was filtered through Whatman no. 1 filter paper and dried at room temperature. This is a modification of the method described by Onwulata et al. (1996), who used carbon tetrachloride as solvent. Broken and hexane washed granules were prepared for electron microscopy as described above.

MATERIALS AND METHODS

Moisture Content, Water Activity, pH, Ash and Minerals

Materials Raw soy (SB), the spray-dried full-fat soymilk powder (MB) and the okara (OK; fibrous residue) of one production batch was obtained from Specialized Protein Products, Potchefstroom, South Africa. Soymilk was prepared by a process developed by this company and entails extraction of the soymilk by selective solubility and separation from the okara by centrifugation followed by spray drying. The process is based on the traditional soymilk preparation process described by Wilson (1995). Spray drying of soymilk and okara was carried out in an industrial spray drier (Stork, Netherlands) with seven lances. The lance openings were 0.71 mm, the flow of 6800 L/h, a pressure of 145 Pa, and the temperatures 200  C at the inlet, 70  C at the outlet and 40  C at the shake bed were applied. Samples of 1 kg of freshly prepared soymilk powder was stored at different temperatures (12  C, 4  C and 25  C). Powder was sealed in PTFE bags after excess air was pressed out to mimic the conditions in industrially packed bags. The samples were stored for a maximum period of 12 months and analyzed every 3 months during storage. The raw soy was ground in a feed grinder for chemical analysis. Methods Morphological Properties of Soymilk Powder by Electron Microscopy Soymilk powder was subjected to scanning electron microscopy on a Jeol 6400 WINSEM scanning electron microscope (Tokyo, Japan). Soymilk powder samples were mounted on 10 mm pin stubs (Agar Scientific, South Africa) with epoxy glue (Steel quickset, Pratley, South Africa). After setting of the glue, excess powder

The moisture content of 2 g samples was determined by drying to constant mass at 110  C according to method AOAC (2000). The water activity of the sample was determined at 25  C with a Novasina thermoconstanter. Suspensions of 1%, 12% and 16% (w/v) soymilk powder were mixed with a vortex, followed by ultrasonification in a UMC5 bath (Ultrasonic Manufacturing Company, Krugersdorp, South Africa) for 5 min and left at room temperature for 30 min before the pH was determined at 20  C with Hanna instruments 8521 pH meter (UK) with temperature probe. The ash and mineral content of 2 g samples were determined by digestion with HNO3 and incineration at 550  C for 12 h according to James (1995). Mineral composition was determined by atomic absorption spectroscopy on a Varian AA-300 spectrometer (USA). Lipids Analysis Extraction of total fat from powder was performed quantitatively according to Folch et al. (1957) using chloroform and methanol in a ratio of 2:1. Total extractable fat content was determined by weighing and expressed as g fat/kg powder. Extracted lipids were subjected to silicic acid column chromatography according to Rouser et al. (1976). A known weight of extracted lipids (Âą150 mg) was dissolved in chloroform and fractionated by using a column (25  100 mm) of silicic acid (mesh size of 100Â&#x2014;200) (SIL-350, Sigma, USA) activated by heating at 110  C overnight. Successive applications of chloroform, acetone and methanol produced fractions containing neutral lipids (triacylglycerols), glycolipids plus sphingolipids (referred to as glycolipids) and polar lipids (phospholipids), respectively. The extracts were dried under vacuum in a rotary evaporator and then

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A Spray-Dried Soymilk Powder

in a vacuum oven at 50  C for 3 h with phosphorus pentoxide as moisture adsorbent. The weight of each fraction was determined and each fraction expressed as percentage (w/w) of extracted fat. Total fat and lipid fractions from column chromatography (Âą10 mg) were methylated to prepare fatty acid methyl esters (FAME) for gas chromatographic (GC) analysis by using methanol-BF3 (Slover and Lanza, 1979). The FAME were quantified using a Varian GX 3400 flame ionization GC (USA), with a fused silica capillary column, Chrompack CPSIL 88 (100 m length, 0.25 mm ID, 0.2 mm film thickness). The column was heated from 40  C to 230  C at 4  C/min by holding the temperature stable for 2 min at the beginning and 10 min the final temperatures. The FAME in hexane (1 mL) was injected into the column using a Varian 8200 CX auto sampler with a split ratio of 100:1. The injection port and detector were both maintained at 250  C. Hydrogen was used as the carrier gas at 45 psi with nitrogen as makeup gas. Chromatograms were recorded using Varian Star Chromatography Software. Identification of sample FAME were made by comparing the relative retention times of FAME peaks from samples with those of standards obtained from SUPELCO (18919, USA). From the storage study, only the unsaturated fatty acids, linoleic (C18:2) and linolenic (C18:3), were reported. These unsaturated fatty acids were also selected to verify the oxidation shown by the TBA values. The thiobarbituric acid reactive substances (TBA value) were determined on a 5 g soymilk powder sample according to the acid extraction method of Raharjo et al. (1992). The peroxide value of the samples was determined on an extracted fat sample according to AOAC (2000). Protein Analysis Crude protein (CP) content was determined by a Kjeldahl procedure (AOAC, 2000), applying a nitrogen (N) conversion factor of 6.25. Soy samples were prepared for electrophoresis by dialysis according to Hames and Rickwood (1990). One gram soy sample was suspended in 8 mL 0.0625 M Tris-HCl buffer (pH 6.8) and dialysed in a cellulose ester dialysis membrane of 30 000 molecular mass exclusion (Labretoria, South Africa) against 0.0625M TrisHCl buffer (pH 9.8) for 3 h. SDS-Polyacrylamide gel electrophoresis (PAGE) was carried out according to the procedures of Hames and Rickwood (1990) using a Hoefer Mighty Small Electrophoresis Unit (USA) with Ovalbumin (43 000 Da), Bovine serum albumin (65 000 Da) and phosphorylase (94 000 Da) as markers (Sigma, USA). Dialysed samples were diluted fourfold with 0.0625M Tris-HCl (pH 6.8) containing 2% sodium dodecyl sulphate (SDS), 5% 2-mercaptoethanol, 10% glycerol and

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0.002% Bromophenol Blue and heated in a boiling water bath for 3 min. Samples were loaded onto electrophoresis gels consisting of a 12.5% (w/v) resolving gel (3M Tris-HCl, 30% Acrylamide, 0.8% bisacrylamide, 10% SDS, 1.5% ammonium persulphate and TEMED at pH 8.8) and a 4% stacking gel (0.5M Tris-HCl, 30% Acrylamide, 0.8% bisacrylamide, 10% SDS, 1.5% ammonium persulphate and TEMED at pH 6.8). Electrophoresis was carried out at 4  C and 100Â&#x2014;200 V with 0.025M Tris, 0.192M Glycine and 1% SDS (pH 8,3) (diluted 1:4) as running buffer. Gels were stained with 0.1% Coomassie Brilliant Blue R-250 and destained with 12.5% isopropanol in 10% acetic acid. Protein bands were identified according to Fontes et al. (1984). Urea-PAGE was done according to a method used by Fontes et al. (1984) in the same apparatus and with the same markers as above. Dialysed samples were diluted 4-fold with 0.75% Tris-HCl, 48% urea and 0.2% bromophenol-blue (pH 7.6). Electrophoresis was carried out at 15  C using a 10% (w/v) separating gel (30% acrylamide:bisacrylamide, 4.6% Tris HCl, 24% urea, TEMED and 10% ammonium persulphate at a pH of 8.8) and a 4% (w/v) stacking gel (1.08% Tris, 36% urea, 0.55% boric acid, 0.092% EDTA, 2.922% acryl amide, 0.078% bisacrylamide, TEMED and 10% ammonium persulphate) with and without the presence of 2-mercaptoethanol in the sample buffer. Gels were run in 0.0925% EDTA, 1.079% Tris, 0.55% Boric acid at pH 8.4 (diluted 1:4 with) at 210 V. Gels were stained as described above. Protein bands were identified according to Fontes et al. (1984). The determination of lipoxygenase activity in the soy samples was based on the method of Ali Asbi et al. (1989). Approximately 16 g sample is suspended in 100 mL distilled water and the pH adjusted to 9.0. After extraction for 1 h, the extract was filtered through cotton wool, and 0.2 mL extract was diluted with 2.8 mL 0.2 M borate buffer (pH 9.0), which was filtered through a 0.45 mm syringe filter (Microsep, USA). A 0.1 mL aliquot of this filtrate was allowed to react with 1.4 mL 0.017% linoleic acid (Sigma, USA), and the absorbance at 234 nm was recorded at 10 s intervals to a constant reading, against 0.017% linoleic acid in distilled water as blank. The procedure was repeated with a commercial lipoxygenase (Sigma; EC. 1.13.11.12) as positive control. The specific activity was expressed as units per gram soymilk powder. Statistical Analysis of Stability Data All analyses were carried out on triplicate samples. Statistical analyses of the data were carried out by using a one-way analysis of variance (ANOVA) procedure. The Tukey-Kramer multiple comparison test was used to identify differences between treatment means (NCSS, 2004).

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Table 1. The chemical composition of soybean, soymilk powder and okara.

RESULTS AND DISCUSSION Morphological Properties of Soymilk Powder by Eelectron Microscopy The morphological characteristics of the spray-dried soymilk powder as determined by electron microscopy are shown in Figures 1a—1c. The powder appears as large granules of approximately 60—80 mm in diameter with smaller granules of 10—20 mm in size attached on, or embedded in, them (Figure 1a). This is comparable with the particle sizes of spray-dried soymilk reported by Perez-Munos and Flores (1997b). The surface of the powder particles appears smooth and velvet like with large uneven indentations. The ground soymilk powder (Figure 1c) shows broken granules that are porous and that may even be hollow with a wall thickness of 10—30 mm. The hexane washed soymilk powder (Figure 1b) shows a more solid surface, in contrast to the smooth velvet like surface of the unwashed sample. There are also small pits and pores visible, which are not obvious in the unwashed sample. After hexane washing, the smaller powder particles also seem to be loosely attached to the larger ones rather than being embedded. This would suggest that there is a lipid layer on the surface of the particles which can be removed with hexane, and that this lipid layer also penetrates pits and pores. A similar layer of fat was reported on milk powder particles (Onwulata et al., 1996; Kim et al., 2002), for which the spray drying conditions are similar to the ones used in the production of the soymilk powder.

(a)

Parameter Moisture (g/100 g) Water activity Total solids (g/100 g) Ash (%) Minerals (g/kg) P Ca K Mg Na Cu Fe Zn Mn Protein (g/100 g) Lipoxygenase activity (units/g solid) Relative Lipoxygenase activity (% raw soy) Fat (g/100 g) % Lipid fractions Neutral Lipids (%) Glycolipids (%) Phospholipids (%) pH soy powder suspension 1% suspension 12% suspension 16% suspension

Soybean

Soymilk powder

Okara

8.29 — 91.71 4.82

4.32 0.325 95.68 6.53

4.89 0.581 95.11 3.16

5.380 1.948 17.318 2.063 0.445 0.010 0.206 0.044 0.031 32.54 27.68

6.992 2.662 15.417 2.098 0.431 0.017 0.081 0.039 0.030 37.81 0.1979

4.595 3.601 5.644 1.449 0.589 0.003 0.081 0.031 0.024 29.90 —

100.00

0.72

—

18.64

22.44

9.01

91.00 2.66 6.97 — — — —

94.03 2.52 4.03 — 6.65 6.78 6.70

90.92 1.88 4.08 — — — —

(b)

(c)

Figure 1. (a) Electron micrograph of freshly spray dried soymilk powder, (b) electron micrograph of broken particles of soymilk powder, (c) electron micrograph of hexane rinsed particles of soymilk powder. Downloaded from fst.sagepub.com at HINARI on February 22, 2011

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Table 2. The fatty acid composition of lipid fractions of soybean, soymilk powder and okara. FA in fat extract (g/100 g) Total fat Fatty acid C12:0 C14:0 C16:0 C16:1 C17:0 C18:0 C18:1 C18:1 C18:2 C18:3 C20:0 C20:1 C22:0 C24:0

c9

c9 c7 c9, 12 c9, 12, 15 c11

Neutral lipids

Glycolipids

Phospholipids

SB

MB

OK

SB

MB

OK

SB

MB

OK

SB

MB

OK

ND 0.08 10.04 ND 0.14 4.62 19.81 0.65 53.73 9.90 0.35 0.12 0.42 0.13

ND 0.05 9.73 ND 0.05 4.48 18.44 0.67 55.42 10.15 0.34 0.10 0.37 0.16

ND 0.13 9.85 0.02 0.11 4.41 17.76 0.69 55.24 10.79 0.34 0.11 0.37 0.13

ND 0.14 9.51 ND 0.05 4.55 20.26 0.64 53.82 9.99 0.37 0.13 0.39 0.12

ND 0.15 9.41 ND 0.03 4.49 18.96 0.67 55.23 10.16 0.32 0.13 0.38 0.08

ND 0.16 9.34 ND 0.08 4.36 18.34 0.65 55.27 10.84 0.34 0.11 0.39 0.11

0.93 1.23 18.40 ND ND 7.14 12.76 0.95 48.72 8.11 0.33 ND 0.87 0.38

1.00 1.42 18.81 0.35 0.25 8.59 17.08 0.93 43.50 6.00 0.09 0.11 0.63 0.42

1.47 1.74 19.67 0.09 0.30 9.49 15.98 0.87 41.99 5.97 0.18 ND 1.01 0.36

ND 0.15 17.22 ND ND 4.70 7.87 1.26 57.73 9.87 0.62 ND 0.62 0.14

ND ND 16.82 ND ND 5.09 7.24 1.19 59.06 9.79 0.69 ND 0.69 0.12

ND ND 16.97 ND ND 5.03 6.43 1.28 58.63 10.68 0.86 ND 0.86 0.12

SB ¼ soybean, MB ¼ full-fat soymilk powder, OK ¼ Okara.

Since granules may be hollow, it is suggested that the structure of the powder particles that are formed during spray drying is a matrix, consisting of protein and fiber residues, in which small fat droplets are embedded, while the remaining fat spreads over the surface of the granules. The mechanism of such a fat distribution through formation of droplets and spreading is possible when taking into account that the melting/setting point of soy oil is 10  C to 16  C (Pryde, 1980). The proteins of the soymilk powder are in a dissolved or dispersed state in water, which form the basis of the matrix of the particles, which is similar to the structure of milk powder suggested by Kher et al. (2007). No crystals of minerals are visible on the surface of the soymilk powder particles, which suggest that minerals are dispersed throughout the matrix. A further comparison with milk powder regarding pores is that the free fat found in full-fat milk powder may be responsible for the presence of pores or cracks on the surface of such powder particles (Millqvist-Fureby et al., 2001; Schoonman et al., 2001; Millqvist-Fureby, 2003).

Chemical Characterization The chemical composition of the full fat soymilk powder, the soy it was extracted from and the fibrous residue, are presented in Tables 1 and 2, and the fractionation of proteins are given in Figures 2a—2b. All the composition parameters of the raw soy were within the limits published in the literature (Smith and Circle, 1980; Snyder and Kwon, 1987). The soybean under study can therefore be viewed as a typical soybean sample, and will not be discussed in more detail. The composition of the okara and the soymilk powder also was comparable to

examples in the literature (Wilson, 1995; Ma et al., 1997). The moisture content and water activity of the soymilk powder and okara fall within the limits of 2.5—5.0% and 0.6, respectively, which is ideal to store powdered products (Toro-Vazquez et al., 2000). The ash content of 6.53% of soymilk powder and 3.16% of okara suggest that the minerals of the soybean mainly find their way into the soymilk powder. The detail of the mineral compositions will not be discussed, as this may vary depending on the origin of the crop or the specific cultivar (Smith and Circle, 1980). The fat content of 22.44% of the soymilk powder suggested that the fat mainly found its way into the soy powder during processing, compared to the 9.01% in the okara. The same accounts for the neutral and glycolipids, with 94.03% and 2.52% found in the soy milk powder and 90.92% and 1.88% in the okara, respectively. The phospholipids are divided equally between the soymilk powder and the okara at approximately 4%. The fatty acid content of the lipid fractions are given in Table 2. For the soymilk powder the contents were 55.42% of C18:2 and 10.15% of C18:3 for the total fat, 43.50% of C18:2 and 6.0% of C18:3 for the glycolipids, 59.06% of C18:2 and 9.79% of C18:3 for the phospholipids, and 55.23% of C18:2 and 10.16% of C18:3 for the neutral lipids. The equivalent amounts in the okara were 55.24% of C18:2 and 10.79% of C18:3 for the total fat, 41.99% of C18:2 and 5.97% of C18:3 for the glycolipids, 58.63% of C18:2 and 10.68% of C18:3 for the phospholipids, and 55.27% of C18:2 and 10.84% of C18:3 for the neutral lipids. It is therefore not only the amounts of lipid types that are divided at different levels, but also the specific fatty acid composition of each. The protein content of the soymilk powder was 37.81% and 29.90% for the okara, which shows that

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Figure 2. (a) SDS-PAGE electrophoretogram of soy bean extract (SB) and fresh soymilk powder (MB) and okara (OK), (b) Urea-PAGE electrophoretogram of soy bean extract (SB) and fresh soymilk powder (MB) and okara (OK). the proteins of the soybeans are also mainly extracted into the soymilk powder during processing (Table 1). For electrophoresis on SDS-PAGE (Figure 2a), the protein aggregates were disrupted by 2-mercaptoethanol, which means that glycinin, for example, is broken up into its acidic (A1a, A1b, A2, A3, A4, A5) and basic (B1a, B1b, B2, B3, B4) subunits, when identified according to Fontes et al. (1984). The electrophoretogram shows that all the acidic and basic as well as all the alpha and beta protein subunits are present, and that the soymilk powder contains more glycinin acidic polypeptides than the fibrous residue. While the protein aggregates are disrupted for SDSPAGE, electrophoresis on Urea-PAGE (Figure 2b)

without 2-mercaptoethanol keeps the protein aggregates intact, and shows that the proteins in soy powder and fibrous residue occur in their natural aggregated form such as the glycinins and conglycinins. Urea-PAGE also shows that the okara contains the same proteins as the soymilk powder, but in lower amounts, and that trypsin inhibitors are virtually absent in both products. Compared to the raw soy, soymilk powder contains very low amounts of trypsin inhibitor, b-amylase and alcohol dehydrogenase (ADH). Lipoxygenase was visible in the PAGE analysis, meaning that it was not removed during processing. Very low levels thereof could still promote lipid oxidation, so that it is necessary to determine its activity.

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A Spray-Dried Soymilk Powder (a)

175

(b)

(c)

Figure 3. (a) Electron micrograph of soymilk powder stored 12 months at 12  C, (b) electron micrograph of soymilk powder stored 12 months at 4  C, (c) electron micrograph of soymilk powder stored 12 months at 25  C.

The lipoxygenase specific activity of the soybean sample was 27.68 units/g soy and that of the soymilk powder was 0.1979 units/g soy, which is 0.72% of the original level (Table 1), which is very low and should not cause a problem during storage (Snyder and Kwon, 1987). It therefore seems as if the lipoxigenase present in the soy milk powder and detected by PAGE is inactivated during processing. The last chemical parameter to be determined was the pH of the soymilk powder suspensions (Table 1) because the pH may affect its physical properties. The pH of a 1% soy powder suspension was 6.65, which is comparable with 6.6Â&#x2014;6.7 of whole and skim milk powder (ToroVazquez et al., 2000). The concentration of a suspension affects the pH, and the pH of 12% and 16% soymilk powder was 6.78 and 6.70, respectively. As reference, the pH of a 16% soybean suspension was also determined, and was 6.50. Changes of Soy Powder During Storage Morphological Changes The morphological properties of soymilk powder were followed by scanning electron microscopy every 3 months over the 12-month storage period. The results for storage at 12 months at the three temperatures are shown in Figures 3aÂ&#x2014;3c. Morphological changes observed for soymilk powder stored at 12  C for 12 months resulted in the pits on the surface of the particles becoming more prominent, while storage at 4  C and

25  C resulted in a waxy and smooth surface. Changes in the physical appearance of the surface of powder particles were observed in spray-dried protein stabilized emulsions containing casein, lactose and palm oil/rape seed oil (Millqvist-Fureby, 2003). It was found that the fat is covering the particle, and that the crystal type of the fat is changed during storage from the less stable aand b0 -forms to the b0 and even the stable b-form. This was especially visible during storage at 37  C, where it was observed that the surface coverage was reduced due to the shrinkage by crystallization, while at 4  C no change in surface appearance and also crystal habit was observed (Millqvist-Fureby, 2003). These results seem to be in contrast with our observations, where changes in surface appearance were observed at 4  C. However, the fat in the described powder differed from that of the soymilk powder by being mainly hydrogenated palm oil, which has a melting range from 40  C to 55  C (Toro-Vazquez et al., 2000; Millqvist-Fureby, 2003), while the melting range of soy fat is much lower at 10  C to 16  C (Pryde, 1980). The changes in the soymilk powder would concurrently occur at lower temperatures. The observations of the soymilk powder under study may indicate an inward migration or contraction of the fat material when stored at 12  C, which is below the melting/setting point of soy oil, while storage at 4  C and 25  C resulted in a waxy and smooth particle surface, suggesting the outward migration of fatty material to cover the outer surface of the granules. This change was already observed after 9 months of storage at 25  C

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G. OSTHOFF ET AL. (b) 11.0

(a) 57 c bc

bc

bc

bc bc

54

MB Day 0 MB –12°C MB 4°C MB 25°C

53

b ab

b

a

3 6 9 Months of storage

8.0

12

1.6

g

1.4

def ce

1.2 ab

ab

ab

bcd ac

fg

bcd bcd

ab

0.8

a

MB Day 0 MB –12°C MB 4°C MB 25°C

0.6 0.4 0.2 0

(e) 7.2

3 6 9 Months of storage

0

(d) Peroxide value (milliequivalents peroxide/1000 g)

0

1.8 TBA value (mg malonaldehyde / 1000 g)

b

MB Day 0 MB –12°C MB 4°C MB 25°C

8.5

(c)

12 d

3 6 9 Months of storage

12

180 160

g

140 f

120 100 80

e

e

60

d

40 20 0

bc

c b a

a

0

bc

MB Day 0 MB –12°C MB 4°C MB 25°C

b

a

3 6 9 Months of storage

12

(f) 7.10

7.1

7.05

7.0

7.00 pH

c

6.9 pH

b b ab

9.0

50

b ab

6.8

6.95 6.90

a

6.7

6.85

6.6

6.80

6.5

6.75

6.4

b

9.5

51

0.0

b

10.0

52

1.0

b b b

ab

55 % C18:2

10.5

bc bc ab

% C18:3

56

c bc bc

0

3

6

9

12

Months of storage

6.70

–12°C

4°C

25°C

Temperature of storage

Figure 4. Effect of time and temperature of storage of soymilk powder over a period of 12 months on: (a) C18:2 FFA content, (b) C18:3 FFA content, (c) TBA value, (d) peroxide value of the total fat content. (e) effect of time of storage on the pH values (Means with different superscripts are significantly different at p < 0.001). (f) The effect of temperature of storage on the pH values (Differences are not significant).

(electron micrograph not shown). Regarding the crystallization/melting properties of fats in spray-dried dairy powders, it was found that the fat on the surface and the centre of a particle does not differ (Kim et al., 2005). If this is true for all powders, it may thus be assumed that the mentioned crystallization and migration of the fat on the surface of the soymilk powder may also occur on the surfaces of inner spaces. Chemical Changes Since no antioxidants were present in the soymilk powder under study, such a spread of fat over the surface may subject it to oxidation (Granelli et al., 1996). The effect of storage at different temperatures on fat

composition and oxidation is presented in Figures 4a—4f. Two unsaturated fatty acids were selected to investigate the effect of oxidation on them, being C18:2 and C18:3. Figure 4a shows that time and temperature of storage affected the C18:2 content of the soymilk powder. Although not significant, storage at 12  C from 9 to 12 months resulted in a decrease of this fatty acid from 55.31±0.22% to 54.2±0.16% after 9 months. A similar, but significant (p < 0.001), trend was observed for the C18:3 which decreased from 10.13±0.17% to 9.52±0.09% after 12 months of storage at 25  C (Figure 4b). While the changes in the content of these two fatty acids may suggest that only the storage at high temperatures such as 25  C may affect the lipid quality

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A Spray-Dried Soymilk Powder

177

Figure 5. Urea-PAGE Electrophoretogram of soymilk powder (MB) and soymilk powder stored at 9 and 12 months at 12  C, 4  C and 25  C, with molecular size markers in lane 1.

of the soy powder, TBA value and peroxide value show that this product is also sensitive to storage at lower temperatures. The TBA values (Figure 4c) of the soymilk powder also showed that the fat is subject to oxidation after 9 months storage at 25  C with a significant (p < 0.001) increase from 0.89±0.09 mg to as high as 1.44±0.10 mg malonaldehyde/kg. TBA values were significantly (p < 0.0001) higher for samples stored for 12 months at 25  C compared to samples stored at 12  C and 4  C for 12 months. The initial peroxide value of 6.9 meq/kg fat of the fat in the soymilk powder was in the same range as has been reported by other researchers (Zia-ur-Rehman et al., 2004; Popoola et al., 2007). The peroxide values determined confirmed that the fat may oxidize over a short storage time such as 3 months at temperatures above 0  C (Figure 4d), while storage at 12  C showed the best stability, resulting in detectable lipid oxidation after 9 months of storage. The changes observed were significant (p < 0.001) with soymilk powder stored at 25  C reaching 151.53±1.32 meq/kg of sample. The soymilk powder stored at 12  C from 3 months of storage onward, had a significantly (p < 0.001) lower peroxide value than the powder stored at 4  C or 25  C. The powder stored at 4  C also had significantly (p < 0.001) lower peroxide values compared to the powder stored at 25  C, during storage from 3 months onward. One explanation of the slower oxidation at the lower temperatures might be that the fat is in a solid state, while at 25  C, it is liquid, able to spread over the particle as explained above, and therefore subjected to oxidation at high temperatures. The storage of the soy powder affected the pH of powder suspension. It was significantly (p < 0.001) affected by storage time (Figure 4e), where the pH of

a 12% soy powder suspension increased from 6.68±0.05 to 7.06±0.08 after 12 months of storage. Storage temperature did not affect the pH of such a suspension (Figure 4f). Proteins may contribute to this change, as it is known that soy proteins may aggregate during storage (Snyder and Kwon, 1987) and that such aggregation may affect the isoelectric strength and pH of a suspension (Kella et al., 1989; Jean et al., 2006). To investigate the possibility of aggregation, electrophoresis was employed. On SDS-PAGE the same protein bands as observed in Figure 2a were seen, which is not unexpected, as the proteins are disrupted into sub-fragments due to the disruption of disulphide bonds by 2-mercaptoethanol. With Urea-PAGE this is not the case and aggregates should be detectable. However, no signs of additional protein bands were observed by this method (Figure 5). Lipid oxidation may also lead to changes in pH of a soy powder suspension; however, a decrease in pH would be expected due to the release of acidic compounds (Snyder and Kwon, 1987). The origin of the increase in pH can therefore not be explained with the available data.

CONCLUSIONS A soymilk powder was characterized physically by electron microscopy and chemically. Powder particles were found to be covered by a layer of fat, the physical properties of which can change depending on storage conditions, which in turn has an effect on the oxidation of the lipids. Storage also affects the properties of re-suspended powder such as the pH. The latter could not be ascribed to aggregation of proteins.

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Liu K.S. (1997). Soybeans. Chemistry, Technology and Utilization. New York: Chapman and Hall, pp. 465—480. Ma C.Y., Liu W.S., Kwok K.C. and Kwok F. (1997). Isolation and characterization of proteins from soymilk residue (okara). Food Research International 29: 799—805. Millqvist-Fureby A. (2003). Characterization of spry-dried emulsions with mixed fat phases. Colloids and Surfaces B 31: 65—79. Millqvist-Fureby A., Elofsson U. and Bergenstahl S. (2001). Surface composition of spray-dried milk protein-stabilised emulsions in relation to pre-heat treatment of proteins. Colloids and Surfaces B 21: 47—58. NCSS (2004). Statistical System for Windows, Number Cruncher Statistical System. Kaysville, Utah, USA: NCSS. Onwulata C.I., Smith P.W., Cooke P.H. and Holsinger V.H. (1996). Particle structures of encapsulated milk fat powders. Lebensmittelwissenschaft und Technologie 29: 163—172. Perez-Munos F. and Flores R.A. (1997a). Characterization of a spray drying system for soymilk. Drying Technology 15: 1023—1043. Perez-Munos F. and Flores R.A. (1997b). Particle size of spray-dried soymilk. Applied Engineering in Agriculture 13: 647—652. Popoola T.O.S., Kolapo A. L. and Afolabi O.R. (2007). Biochemical deterioration of soybean daddawa: a condiment. Journal of Food Agriculture and Environment 5: 67—70. Pryde E.H. (1980). Physical propeties of soybean oil. In: Erickson S.R., Pryde E.H., Brekke O.L., Mounts T.L. and Falb R.A. (eds), Handbook of Soy Oil Processing and Utilization. St Louis: American Soybean Association; American Oil Chemists Society, pp. 44—45. Raharjo S., Sofos J.N. and Schmidt G.R. (1992). Improved speed, specificity and limit of determination of an aqueous acid extraction thiobarbituric acid-C18 method for measuring lipid peroxidation in beef. Journal of Agricultural and Food Chemistry 40: 2182—2185. Rouser G., Kritchevsky G. and Yamamoto A. (1976). Column chromatographic and associated procedures for separation and determination of phospholipids and glycolipids. In: Marinetti G.V. (ed.), Lipid Chromatographic Analysis 3. New York: Marcel Dekker, pp. 99—162. Schoonman A., Mayor G., Dillmann M.L., Bisperink C. and Ubbink J. (2001). The microstructure of foamed maltodextrin/sodium caseinate powders: a comparative study by microscopy and physical techniques. Food Research International 34: 913—929. Slover H.T. and Lanza E. (1979). Quantitative analysis of food fatty acids by capillary gas chromatography. Journal of the American Oil Chemists’ Society 56: 933—943. Smith A.K. and Circle S.J. (1980). Soybeans: Chemistry and Technology, Vol. 1. Westport: AVI Pub. Co. Snyder H.E. and Kwon T.W. (1987). Soybean Utilization. New York: AVI Pub. Co. Toro-Vazquez J.F., Bricen˜o-Montelongo M., Dibildox-Alvarado E., Charo´-Alonso M. and Reyes-Herna´ndez J. (2000). Crystallization kinetics of palm stearin blends with sesame seed oil. Journal of the American Oil Chemists’ Society 77: 297—310. Wilson L. (1995). Soy foods. In: Erickson D.R. (ed.), Practical Handbook of Soybean Processing and Utilization. Chicago: AOCS Press and the United Soybean Board, pp. 428—459. Zia-ur-Rehman., Habib F. and Shah W.H. (2004). Utilization of potato peels as a natural antioxidant in soy bean oil. Food Chemistry 85: 215—220.

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Fractal Dimension and Mechanism of Aggregation of Apple Juice Particles E.I. BenĂ­tez, J.E. Lozano and D.B. Genovese Food Science and Technology International 2010 16: 179 DOI: 10.1177/1082013209353240 The online version of this article can be found at: http://fst.sagepub.com/content/16/2/179

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Fractal Dimension and Mechanism of Aggregation of Apple Juice Particles E.I. Benı´ tez,1 J.E. Lozano2 and D.B. Genovese2,* 1

Universidad Tecnolo´gica Nacional, Facultad Regional Resistencia, French 414 (H3500CHJ) Resistencia — Chaco, Argentina 2 PLAPIQUI (UNS-CONICET), Camino La Carrindanga km 7, (8000) Bahı´a Blanca, Argentina Turbidity of freshly squeezed apple juice is produced by a polydisperse suspension of particles coming from the cellular tissue. After precipitation of coarse particles by gravity, only fine-colloidal particles remain in suspension. Aggregation of colloidal particles leads to the formation of fractal structures. The fractal dimension is a measure of the internal density of these aggregates and depends on their mechanism of aggregation. Digitized images of primary particles and aggregates of depectinized, diafiltered cloudy apple juice were obtained by scanning electron microscopy (SEM). Average radius of the primary particles was found to be a ¼ 40±11 nm. Maximum radius of the aggregates, RL, ranged between 250 and 7750 nm. Fractal dimension of the aggregates was determined by analyzing SEM images with the variogram method, obtaining an average value of Df ¼ 2.3±0.1. This value is typical of aggregates formed by rapid flocculation or diffusion limited aggregation. Diafiltration process was found to reduce the average size and polydispersity of the aggregates, determined by photon correlation spectroscopy. Average gyration radius of the aggregates before juice diafiltration was found to be Rg ¼ 629±87 nm. Average number of primary particles per aggregate was calculated to be N ¼ 1174. Key Words: apple juice, particles, aggregation, fractal, diafiltration

INTRODUCTION Cloudy apple juice is considered to be a colloidal dispersion where the continuous medium is a solution of pectin, sugars and malic acid and the dispersed matter is mainly formed by cellular tissue comminuted during fruit processing (Benı´ tez and Lozano, 2007). To obtain a clear juice these suspended particles have to be removed. This process is known as clarification, or fining, one of the most important unit operations in apple juice processing. To obtain a completely transparent liquid, the first step is to destabilize the dispersion. This procedure also helps to remove active haze precursors, decreasing the potential for haze formation during storage and providing a more limpid juice (Hsu et al., 1987, 1989, 1990). Therefore, the fining step is an important procedure that should be carefully controlled during the processing of clarified apple juice.

*To whom correspondence should be sent (e-mail: dgenovese@plapiqui.edu.ar). Received 24 November 2008; revised 2 April 2009. Food Sci Tech Int 2010;16(2):0179–8 ß SAGE Publications 2010 Los Angeles, London, New Delhi and Singapore ISSN: 1082-0132 DOI: 10.1177/1082013209353240

The colloidal particles are mainly composed of carbohydrates and proteins and form a negative charged colloid (Yamasaki et al., 1964; Dietrich et al., 1996; Benı´ tez et al., 2007a). Cloud particles are modeled to consist of negatively charged, partly demethoxylated pectin wrapped around a core of positively charged carbohydrates and protein (Beveridge, 1997). Depectinization partially degrades pectin and exposes the protein core. Then, aggregation between polycations and polyanions occurs, making possible the colloid flocculation (Belitz et al., 2004). Nevertheless, some cellular particulate material still remains in suspension after this treatment, causing an appreciable, unwanted turbidity in the juice (Will et al., 1993; Benı´ tez et al., 2007a, b). Flocculation is a transient, non-equilibrium process that eventually converts a dispersion of individual particles into a disordered solid. At intermediate stages, aggregates range in size from a few to many thousands of particles and in structure from dense flocs to tenuous networks (Russel et al., 1989). In aqueous systems, any organic or inorganic entity in the size range of 1 nm to 1 mm form inherently unstable suspensions due to their tendency to undergo conformational changes, aggregate and finally sediment (Buffle and Leppard, 1995). Coagulation—sedimentation processes depend not only on the physical properties of the colloidal material, such as size, density, compactness and rigidity, but also on the chemical properties of the surface (Liang and

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Morgan, 1990), which determine the strength of interactions between colloidal particles (Buffle and Leppard, 1995). Benı´ tez et al. (2007a) studied those interaction forces in apple juice and concluded that particles were very hydrophilic and inherently stable, which was attributed to an immobilized water layer coating them. Colloidal material with particle sizes <0.1 mm are generally very unstable and tend to quickly associate to form primary aggregates in the size range between 0.1 and 1 mm. Once formed, these primary aggregates are typically stable for more extended periods of time (Lencki and Riedl, 1999). Recent experimental and theoretical results found in the literature show that the aggregation of colloidal particles leads to the formation of fractal structures, that is, highly branched aggregates. In agreement, it has been observed (Beveridge, 1999) that particles of the apple juice haze resemble fractile clusters formed by the diffusion limited aggregation (DLA) mechanism, wherein a diffusing particle ‘hits and sticks’ to the cluster in the same position where it arrived. The fractal dimension Df reflects the internal structure of the flocs and depends on the rate and mode of Brownian (colloidal) aggregation. DLA is a regime of rapid, irreversible flocculation with no subsequent rearrangement. The presence of an energy barrier between particles slows down the collision frequency, resulting in slow flocculation or reaction limited aggregation (RLA). In general, DLA yield structures with Df ¼ 2.5 if the flocs grow by adding one particle at a time, but this value drops to Df ¼ 1.75 if cluster—cluster aggregation predominates. On the other hand, RLA gives values of Df ¼ 2.0—2.2. (Russel et al., 1989; Berli et al., 1999). The fractal concept was first proposed by Mandelbrot (1983), who introduced dimensions between the conventional Euclidean dimensions of 1, 2 and 3 to describe structures that are not Euclidean lines, surfaces or solids. Fractal dimension indicates the degree to which an image or object outline deviates from smoothness and regularity. The higher Df, the more irregular the structure (Nagai and Yano, 1990). A characteristic of fractal objects is to display self-similarity or scaling behavior (Meakin, 1988; Genovese and Rao, 2003), the attribute of having the same appearance at all magnifications. Most of the fractals observed in nature are the result of complex physical processes, which modify the morphology of the matter at random. Fractal dimensions have been successfully used to describe the ruggedness and geometric complexities of both natural and synthetic particles (Peleg and Normand, 1985; Yano and Nagai, 1989; Nagai and Yano, 1990; Graf, 1991; Genovese and Rao, 2003). The objective of this work was to determine the fractal dimension of apple juice aggregates in order to: (a) characterize their morphology, (b) infer their mechanism of aggregation and (c) estimate the number of primary particles that compose each aggregate.

MATERIALS AND METHODS Calculation Method of the Fractal Dimension and Number of Particles per Aggregate The fractal dimension was determined by applying the variogram method to scanning electron microscopy (SEM) images of the aggregates (Burrough, 1981; Bonetto and Ladaga, 1998; Bianchi and Bonetto, 2001; Bonetto et al., 2002). The variogram method is based on the calculation of the variance V of the brightness level distribution of a sample surface image: V¼

nX

o z2i  s2H

ð1Þ

where zi is the bright level difference between two different positions in a digital image for a step i of length s, measured in pixels or microns. Here i is the step between two points of analysis and s is the length between these two points. The brackets denote the expectation value (Bonetto and Ladaga, 1998; Bianchi and Bonetto, 2001; Bonetto et al., 2002). The value of H is obtained from the slope of log(V) versus log(s), as shown in Figure 2. This value is related to the fractal dimension as follows: Df ¼ 3  H

ð2Þ

Since H varies between 0 and 1 (Mandelbrot and Van Ness, 1968; Mandelbrot, 1983), Df varies between 2 (flat surface) and 3 (totally rough surface). For fractal aggregates, the number of primary particles in each aggregate, N, may be related to its fractal dimension, Df, its radius of gyration, Rg, and the average primary particle radius, a (Ko¨ylu¨ et al., 1995; Jones, 1999; Sorensen and Hageman, 2001):  N ¼ Kg

Rg a

Df ð3Þ

where Kg is the fractal pre-factor, whose value depends on the properties of the system and must be determined experimentally. Also, the number of primary particles in an aggregate is related to its projected area, Aa, and the average cross section of the primary particles, Ap, as follows (Ko¨ylu¨ et al., 1995):  ! Aa N ¼ Ka Ap

ð4Þ

where ! is the projected area exponent and Ka is the projected area pre-factor. Ka is a proportionally constant of order unity and in some cases it is assumed to be equal to one. For example, Ka ¼ 1.00 when

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5 < N < 250 (Samson et al., 1987) and Ka ¼ 1.15 when 5 < N < 10 000 (Ko¨ylu¨ et al., 1995) for soot aggregates. The main difficulty in the analysis of the SEM images of aggregates is that three-dimensional morphological information must be inferred from two-dimensional (projected) images. It has been found that parts of the cluster can randomly screen other parts during twodimensional image formation. This problem has been well studied by a number of workers and is included in the ! parameter determination (Ko¨ylu¨ et al., 1995). The other problem is that the actual radius of gyration of each aggregate is required (Equation (3)), whose values are not readily available from the projected images. Accordingly, several studies suggested that any characteristic dimension of an aggregate can be used instead of the radius of gyration Rg. In some cases the outer radius of an aggregate (RL  L/2) has been adopted, where L is the maximum length of the projected image and the number of primary particles in each aggregate is determined as follows:

N ¼ KL

 Df RL a

ð5Þ

where KL is a proportionality constant. Assuming Ka ¼ 1 in Equation (4) and combining with Equation (5) yields:  Df =! Aa RL ¼ K1=! L Ap a

 Df Rg a

ð9Þ

where Dh is the hydrodynamic diameter, the most appropriate particle size to use in equations relating to fluid—particle interactions; that is an equivalent sphere diameter derived from a measurement technique involving hydrodynamic interaction between the particle and fluid (Benı´ tez et al., 2007b). Photon correlation spectroscopy (PCS) is widely used as an analytical tool to measure Dh (Xu, 1998). Combining Equations (8) and (9), the following expression may be obtained:  1=2 3 Dh Rg ¼ 5 2

ð10Þ

Even though apple juice particles do not strictly fulfill the ideal conditions required for Equation (10) to be valid, it was used as a first approximation to estimate their average radius of gyration from hydrodynamic diameter values obtained from PCS measurements. The value of Kg required in Equation (7) may be calculated by applying the values of KL and Df in Equation (13), which is obtained as follows. Combining Equations (3) and (5):  Df Kg RL ¼ KL Rg

ð11Þ

Also, Rg and RL may be related through Df with the following expression (Ko¨ylu¨ et al., 1995):   RL Df þ 2 1=2 ¼ Df Rg

ð12Þ

Combining Equations (6) and (7) yields:   Kg Df þ 2 Df =2 ¼ Df KL

ð13Þ

With this method it is possible to morphologically characterize the aggregates, combining the information obtained from the analysis of their projected images and the PCS technique.

ð7Þ Sample Preparation for Scanning Electron Microscopy

For spheres of geometrical radius R (Anonymous, 1996): Rg ¼ ð3=5Þ1=2 R

R  Dh =2

ð6Þ

Values of Aa, Ap, RL and a may be obtained by conventional microscopy analysis, like statistical analysis of SEM digital images with software ad hoc. Then, values of ! and KL may be  obtained from the slope and intercept of a logðAa Ap Þ versus logðRL =aÞ plot. Using these values in Equation (4), or Equation (5), the number of primary particles in each aggregate, N, may be obtained. However, for practical purposes it would be worth to obtain a unique, representative parameter to characterize all the aggregates of the sample. One possibility is to use the average radius of gyration, Rg in Equation (3) to calculate an average number of particles per aggregate, N:

N ¼ Kg

If solvation of solvent and some other interaction effects are neglected (Anonymous, 1996), then:

ð8Þ

Cloudy apple juice (cv. Granny Smith) was obtained from a juice factory (Jugos S.A., Rı´ o Negro, Argentina). The juice was depectinized with a commercial pectolytic enzyme (Solvay 5XLHA; 20 mg/L, 2 h at 50  C), the supernatant was separated from the degraded pectin

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sediment and subjected to diafiltration in a lab-scale Õ equipment Osmonic Sepa CF (Osmonics, Minnetonka, MN, USA) with 100 kDa cut off polysulphone membranes. The juice (1000 mL) was diafiltered with distilled water until reaching a constant conductivity of 0.06 mS/cm. The objective of the diafiltration process was to eliminate most of the natural solutes of apple juice (mainly sugars), avoiding sample agglomeration in the next steps of the preparation process. The result of diafiltration is the isolation of the insoluble solids of the juice in almost pure water (Benı´ tez and Lozano, 2006). To obtain SEM images of aggregates, samples of diafiltered juice were fixed with 2.5% glutaraldehyde in phosphate buffer pH 7.2. Same droplets of the fixed material were put in glass coverslips with polylysine film for 1 h. Polylysine is a polymeric substance that possesses an attractive potential (Thomas et al., 1996). The negatively charged aggregates are attracted to the polylysine. Then, the coverslip was washed with the same buffer, dehydrated with 25%, 50%, 75%, 80% and three times with 100% solutions of acetone. Finally the coverslips were desiccated with a critic point drier (Polaron E3000 CPD, EEUU) with acetone and CO2 as intermediate fluids. The samples were gold sputtered with an automatic sputter coater (Sputter Coater, Pelco 91000) and analyzed by SEM (LEO, EVO 40, Cambridge, Ing.) at 20 kV accelerating voltage. To obtain SEM images of the primary particles, diafiltered juice was previously diluted 1 : 100 and sonicated for 30 min in order to re-disperse the aggregates. Afterwards, sample preparation for SEM followed the same procedure as described in the previous paragraph. Analysis of Particles and Aggregates Twenty-six (26) SEM images of the aggregates were analyzed with the variogram method for the determination of Df, using the software FERImage (Bianchi and Bonetto, 2001) This software is freely available at http:// www.cindeca.org.ar/programas.htm. Ninety-five (95) aggregates were digitized for the determination of Aa and RL and more than 500 primary particles were digitalized for the determination of Ap and a. The aggregates were manually segmented from several images. The aggregates with the best separation were selected. The outer radius of each aggregate was determined as RL  L/2, where L is the maximum length of the projected image. The values of a were determined as the equivalent surface radius, or radius of a sphere having the same projected area as the particle. Digital SEM images of the aggregates and primary particles were statistically analyzed with the AnalySIS 2.1 version (Soft-imaging Software Gmbh). Particle size distribution and average hydrodynamic diameter, Dh, of the diafiltered juice were measured at 25 C (10 replicates) in a Malvern Zetasizer 3000 particle analyzer (Malvern Instrument Inc., London, UK).

RESULTS AND DISCUSSION Figure 1 shows a SEM image of apple juice aggregates obtained after diafiltration and gold sputter-coated. It can be observed that small primary particles were aggregated into fractile clusters or ‘islands’, some of them interconnected, in agreement with the type of structure observed by Beveridge (1999) in apple juice haze particles. In that work, the island building units were envisioned as envelopes of close-packed condensed tannin droplets bounded by partially coalesced droplets at the tannin/water interface. Figure 2 displays the application of the variogram method for the determination of the fractal dimension. The average fractal dimension value obtained by statistical analysis of the 26 SEM images was Df ¼ 2.3±0.1. This value (close to 2.5) indicates that the aggregates were formed by rapid flocculation or diffusion limited aggregation (DLA), by incorporation of individual particles and not by aggregate—aggregate interaction (a Df value close to 1.75 would be obtained in this case (Russel et al., 1989). This is in agreement with the observations of Beveridge (1999) on apple juice haze particles. According to experimental observations, the flocculation process took place very quickly, immediately after enzymatic treatment and then continued slowly. The maximum radius of the aggregates, RL, ranged between 250 and 7750 nm. It can be observed (Figure 3) that their fractal dimension did not vary significantly with their size (variation coefficient CV < 5%). This result indicates that primary particles aggregated into structures with similar configurations at different length scales, in agreement with the ‘selfsimilarity’ property of fractals. Dispersed primary particles were obtained after dilution and sonication of the diafiltered juice (Figure 4). The average cross-section of the primary particles was determined to be Ap ¼ 4960 nm2. Their equivalent surface radius was a ¼ 40±11 nm. Calculation of the average number of particles per aggregate (Equations (3)—(13)), requires as a first step the determination of the empirical constants ! and KL. This was done by fitting the experimental data with Equation (6) (Figure 5), obtaining the following correlation (R2 ¼ 0.931):     Aa RL ¼ 1:589  log log þ 0:0039 Ap a

ð14Þ

This gives values of ! ¼ 1.45 and KL ¼ 1.01. Then, from Equation (13) the value of Kg ¼ 2.08 was obtained. As previously stated, cloudy apple juice had to be diafiltered as a sample preparation requirement for SEM analysis. Consequently, the effect of the diafiltration process on the parameters involved in Equation (7)

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Mechanism of Aggregation of Apple Juice Particles

183

Figure 1. SEM image of colloidal aggregates, obtained after apple juice diafiltration and gold sputter. Magnification: 17530  . Scale: 2 mm ¼ 113 pixels.

Figure 2. Image of the variogram method applied to the aggregate shown on the left, with the software FER image. has to be evaluated before calculating the average number of particles per aggregate (N). In order to do that, the average radius of gyration of the aggregates (Rg ) and their polydispersity () were monitored along

the diafiltration process. The average hydrodynamic diameter of each sample was measured and used in Equation (10) to calculate Rg . Values of  were obtained from the standard deviation or ‘width’ of the size

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184

distribution of each sample (McClements, 1999). Progress of the diafiltration process was represented as the equivalent volume EV, defined as the volume of water added per initial volume of juice. Both Rg (Figure 6) and  (Figure 7) underwent a pronounced fall at the beginning of the diafiltration process, followed by a smooth decrease until the end of the process. This was attributed to mechanical re-dispersion of

the aggregates due to hydrodynamic stresses suffered during the diafiltration process, resulting in a smaller average size and a more homogeneous size distribution. Benı´ tez et al. (2007b) reported that cloudy apple juice (CAJ) and its diafiltered juice (DJ) had similar average particle sizes, but CAJ was more polydisperse than DJ. Nevertheless, the diafiltration process applied in this work reduced 15% the Rg value of the apple

4.5

2.6

4

2.4

3.5

2.2

3

Log (Aa/Ap)

Df

2.8

2 1.8 1.6

2.5 2 1.5

1.4

1

1.2

0.5

1

0

2

4

6

8

0

0

0.5

RL (mm)

1

1.5

2

2.5

Log (RL/a)

Figure 3. Fractal dimension of the aggregates plotted against their maximum radius. Horizontal line represents the average value.

Figure 5. Correlation between experimental data from the analysis of 95 SEM images of aggregates, fitted with Equation (14).

Figure 4. SEM image of primary particles, obtained after dilution and sonication of diafiltered apple juice. Magnification: 7000  . Scale: 2 mm ¼ 52 pixels. Downloaded from fst.sagepub.com at HINARI on February 22, 2011

Mechanism of Aggregation of Apple Juice Particles

juice aggregates. Consequently, the value of gyration radius before diafiltration should be used for the calculation of N with Equation (7), namely Rg ¼ 629±87 nm. The other parameters to be evaluated in Equation (7) are Df, a and Kg. First, due to the self-similarity property of fractal objects, the Df value of apple juice aggregates was not significantly affected by changes in their size or size distribution (Figure 3) and then it should not change by diafiltration. Second, the primary particles are not expected to break down by hydrodynamic stresses and then their size is supposed to remain constant during diafiltration. Finally, the value of Kg depends on Df and KL (Equation (13)). The value of KL depends on Aa, Ap, RL and a (Equation (6)). If, as stated in the previous paragraph, values of Df and a were not affected by diafiltration, then values of Ap should not be affected either. However, the other two parameters, related to the aggregates (RL and Aa), are expected to suffer some change during diafiltration. This change could not be calculated directly from microscopic analysis. Then, based on the assumption that the relative change in RL values was proportional to the relative increase in Rg values and that the relative change in

700

Rg (nm)

650 600 550

185

Aa values was proportional to the second power of the relative increase in Rg values, it was roughly estimated that the diafiltration process would produce an increase of 8% on the value of KL (and Kg). For practical purposes, this change was considered to be negligible for the calculation of N. Consequently, values of Df, a and Kg obtained from analysis of SEM images of diafiltered juice, were considered to be the same as those of regular cloudy apple juice and used in Equation (7) to calculate the average number of primary particles per aggregate, resulting in N ¼ 1174. The most representative values obtained for the aggregates were: Df(—) 2.3±0.01; Rg(nm) 629±87; a(nm) 40±11; N(—) 1174.

CONCLUSIONS The formation of fractal aggregates in cloudy apple juice by a mechanism of diffusion limited aggregation (DLA) has been proposed previously (Beveridge, 1999). This work confirmed and quantified those observations by obtaining a fractal dimension value which corresponds to the DLA mechanism. A modified method to calculate the average number of primary particles per aggregate was proposed and used, combining analysis of digitalized SEM images and particle size measurements with the PCS technique. These results contribute to the study and understanding of the structure, aggregation and stability of apple juice particles, which is of paramount importance for the production of cloudy and clarified juices.

500

NOMENCLATURE

450 0

5

10

15

20

EV

Figure 6. Average gyration radius of aggregates as a function of equivalent volume.

100 s (nm)

80 60 40 20 0

0

5

10

15

20

EV

Figure 7. Standard deviation of aggregates size as a function of equivalent volume.

a ¼ Average radius of primary particles (nm) Aa ¼ Projected area of the aggregates (nm2) Ap ¼ Projected area of the primary particles (nm2) Df ¼ Fractal dimension Dh ¼ Hydrodynamic diameter (nm) H ¼ Hurst exponent i ¼ Positions in a digital image Ka ¼ Projected area prefactor Kg ¼ Fractal prefactor KL ¼ Proportionality constant N ¼ Number of primary particles in each aggregate N ¼ Average number of primary particles per aggregate R ¼ Geometrical radius of a sphere (nm) Rg ¼ Gyration radius (nm) Rg ¼ Average gyration radius of the aggregates (nm) RL ¼ Maximum radius of the aggregates (nm) s ¼ Length between two different positions in a digital image (pixel)

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V ¼ Variance of the brightness level distribution of a sample surface image zi ¼ Bright level in a position i in a digital image Greek letters  ¼ Polydispersity (nm) ! ¼ Projected area exponent

ACKNOWLEDGMENT This research was supported by a grant (PICT 0908016; BID 1201/OC-AR) from the Agencia Nacional de Promocio´n Cientı´fica y Tecnolo´gica of Argentina.

REFERENCES Anonymous (1996). Zetasizer 1000/2000/3000. PCS theory. In: Malvern Instruments Manual Number MAN 0152, Issue 1.1, Worcestershire, UK: Malvern Instruments Ltd. Belitz H.D., Grosch W. and Schieberle P. (2004). Enzymes: Food Chemistry, 3rd edn. New York: Springer Verlag. Benı´ tez E.I., Genovese D.B. and Lozano J.E. (2007a). Effect of pH and ionic strength on apple juice turbidity: application of the extended DLVO theory. Food Hydrocolloids 21: 100—109. Benı´ tez E.I., Genovese D.B. and Lozano J.E. (2007b). Scattering efficiency of a cloudy apple juice: effect of particles characteristics and serum composition. Food Research International 40: 915—922. Benı´ tez E.I. and Lozano J.E. (2006). Influence of the soluble solids on the zeta potential of a cloudy apple juice. Latin American Applied Research 36: 163—168. Benı´ tez E.I. and Lozano J.E. (2007). Effect of gelatin on apple juice turbidity. Latin American Applied Research 37: 261—266. Berli C.L.A., Deiber J.A. and An˜o´n M.C. (1999). Heat-induced phenomena in soy protein suspensions. Rheometric data and theoretical interpretation. Journal of Agricultural and Food Chemistry 47: 893—900. Beveridge T. (1997). Haze and cloud in apple juices. Critical Reviews in Food Science and Nutrition 37: 75—91. Beveridge T. (1999). Fractile images and apple juice haze. Food Research International 31: 411—414. Bianchi F.D. and Bonetto R.D. (2001). FERImage: an interactive program for fractal dimension, dper and dmin calculation. Scanning 23: 193—197. Bonetto R.D., Forlerer E. and Ladaga J.L. (2002). Texture characterization of digital images witch have a periodicity or quasi-periodicity. Measurement Science and Technology 13: 1458—1466. Bonetto R.D. and Ladaga J.L. (1998). The variogram method for characterization of scanning electron microscopy images. Scanning 20: 457—463. Buffle J. and Leppard G. (1995). Characterization of aquatic colloids and macromolecules. 1. Structure and behavior of colloidal material. Environmental Science and Technology 29: 2169—2175. Burrough P.A. (1981). Dimensions of landscapes and other environmental data. Nature 294: 240—242. Dietrich H., Gierschner K., Pecoroni S., Zimmer E. and Will F. (1996). New findings regarding the phenomenon of cloud stability. Flu¨ssiges Obst 63: 7—10.

Genovese D.B. and Rao M.A. (2003). Role of starch granule characteristics (volume fraction, rigidity and fractal dimension) on the rheology of starch dispersions with and without amylose. Cereal Chemistry 80: 350—355. Graf J.C. (1991). The importance of resolution limits to the interpretation of fractal descriptions of fine particles. Powder Technology 67: 83—85. Hsu J.C., Heatherbell D.A. and Yorgey B.M. (1987). Heat-unstable proteins in wine. I. Characterization and removal by bentonite fining and heat treatment. American Journal of Enology and Viticulture 38: 11—15. Hsu J.C., Heatherbell D.A. and Yorgey B.M. (1989). Effects of fruit storage and processing on clarity, proteins and stability of Granny Smith apple juice. Journal of Food Science 54: 660—662. Hsu J.C., Heatherbell D.A. and Yorgey B.M. (1990). Effects of variety, maturity and processing on pear juice quality and protein stability. Journal of Food Science 55: 1610—1613. Jones A.R. (1999). Light scattering for particle characterization. Progress in Energy and Combustion Science 25: 1—53. Ko¨ylu¨ U.O., Xing Y. and Rosner D.E. (1995). Fractal morphology analysis of combustion-generated aggregates using angular light scattering and electron microscope images. Langmuir 11: 4848—4854. Lencki R. and Riedl K. (1999). Effect of fractal flocculation behavior on fouling layer resistance during apple juice microfiltration. Food Research International 32: 279—288. Liang L. and Morgan J. (1990). Chemical aspects of iron oxide coagulation in water, laboratory studies and implications for natural systems. Aquatic Science 52: 32—55. Mandelbrot B.B. (1983). The Fractal Geometry of Nature. New York: W.H. Freeman and Co. Mandelbrot B.B. and Van Ness J.W. (1968). Fractional Brownian motions, fractional noises and applications. SIAM Review 10: 422—437. McClements D.J. (1999). Food Emulsions: Principles, Practice and Techniques, 1st edn. Boca Raton, Florida: CRC Press, pp. 8—10. Meakin P. (1988). Fractal aggregates. Advances in Colloid and Interface Sciences 28: 249—331. Nagai T. and Yano Y. (1990). Fractal structure of deformed potato starch and its sorption characteristics. Journal of Food Science 55: 1334—1337. Peleg M. and Normand M.D. (1985). Characterization of the ruggedness of instant coffee particle shape by natural fractals. Journal of Food Science 50: 829—831. Russel W.B., Saville D.A. and Schowalter W.R. (1989). Colloidal Dispersions. Cambridge, UK: Cambridge University Press, p. 525. Samson R.J., Mulholland G.W. and Gentry J.W. (1987). Structural analysis of soot agglomerates. Langmuir 3: 272—281. Sorensen C.M. and Hageman W.B. (2001). Two-dimensional soot. Langmuir 17: 5431—5434. Thomas N.E., Coakley W.T. and Winters C. (1996). Contact formation in polylysine-mediated membrane-glass interaction. Colloids and Surfaces B 6: 139—147. Will F., Mischler M. and Do¨rreich K. (1993). Pilot scale isolation and separation of apple juice colloids. Lebensmittel Wissenschaft und Technologie 27: 292—294. Xu R. (1998). Shear plane and hydrodynamic diameter of microspheres in suspension. Langmuir 14: 2593—2597. Yamasaki M., Yasui T. and Arima K. (1964). Pectic enzymes in the clarification of apple juice. Agricultural and Biological Chemistry 28: 779—787. Yano T. and Nagai T. (1989). Fractal surface of starchy materials transformed with hydrophilic alcohols. Journal of Food Engineering 10: 123—133.

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Rapid Determination of Docosahexaenoic Acid in Powdered Oil by Near-Infrared Spectroscopy Yang Meiyan, Li Jing, Nie Shaoping, Hu Jielun, Yu Qiang, Xie Mingyong, Xiong Hua, Deng Zeyuan and Zheng Weiwan Food Science and Technology International 2010 16: 187 DOI: 10.1177/1082013209353379 The online version of this article can be found at: http://fst.sagepub.com/content/16/2/187

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Rapid Determination of Docosahexaenoic Acid in Powdered Oil by Near-Infrared Spectroscopy Yang Meiyan, Li Jing, Nie Shaoping, Hu Jielun, Yu Qiang, Xie Mingyong,* Xiong Hua, Deng Zeyuan and Zheng Weiwan State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, China Near-infrared spectroscopy (NIRS) was used as a rapid and nondestructive method to determine the content of docosahexaenoic acid (DHA) in powdered oil samples. A total of 82 samples were scanned in the diffuse reflectance mode by Nicolet 5700 FTIR spectrometer and the reference values for DHA was measured by gas chromatography. Calibration equations were developed using partial least-squares regression (PLS) with internal cross-validation. Samples were split in two sets, one set used as calibration (n ¼ 66) whereas the remaining samples (n ¼ 16) were used as validation set. Two mathematical treatments (first and second derivative), none (log(1/R)) and standard normal variate as scatter corrections and Savitzky—Golay smoothing were explored. To decide upon the number of PLS factors included in the PLS model, the model with the lowest root mean square error of cross-validation (RMSECV ¼ 0.44) for the validation set is chosen. The correlation coefficient (r) between the predicted and the reference results which used as an evaluation parameter for the models is 0.968. The root mean square error of prediction of the final model is 0.59. The results reported in this article demonstrate that FT-NIR measurements can serve as a rapid method to determine DHA in powdered oil. Key Words: near-infrared spectroscopy, partial least-squares regression, docosahexaenoic acid, powdered oil

INTRODUCTION Docosahexaenoic acid (DHA, C22 : 6n-3), one of the major omega-3 polyunsaturated fatty acids in the brain, is important for brain development and plasticity, and provides support to learning and memory events in animal models of Alzheimer’s disease (Hashimoto et al., 2002; Lim et al., 2005) and brain injury (Wu et al., 2004). DHA can affect neural function by enhancing synaptic membrane fluidity and function (Jump, 2002), regulating gene expression (Duplus et al., 2000; Ikemoto et al., 2001), mediating cell signaling and enhancing long-term potentiation. As the importance of DHA in human nutrition is well recognized, it is widely used in milk product in recent years, especially in Follow Up Formula. DHA was added to milk powder in the form of powdered oil, which enjoys an excellent reputation as a good *To whom correspondence should be sent (e-mail: myxie@ncu.edu.cn). Received 22 December 2008; revised 14 April 2009. Food Sci Tech Int 2010;16(2):0187–7 ß SAGE Publications 2010 Los Angeles, London, New Delhi and Singapore ISSN: 1082-0132 DOI: 10.1177/1082013209353379

material with multiple functions, so it is necessary to determine the DHA content of powdered oil. Fatty acid content was usually determined by gas chromatography equipped with a flame ionization detector and a capillary column. However, this method requires multi-step sample preparation. For example, fatty acids should be converted to fatty acid methyl esters (FAMEs) before GC analysis. This analysis method is time-consuming, expensive, labor-intensive and also destructive. Thus, a rapid and nondestructive method is in high demand to evaluate DHA content in powdered oil. There is also a need for the powdered oil processing industry to have tools that available for real time control of production lines, which can check whether in-process material, during a given processing step, meets the necessary content to reach a predetermined quality standard in the final product. Near-infrared spectroscopy (NIRS) has many advantages compared with standard techniques. The analysis is carried out without using hazardous chemicals, and it is time-saving, low-cost, and nondestructive. Besides, it can be applied to on-line and off-line analysis. NIR reflectance spectroscopy has proved to be a rapidresponse analytical tool for food products (Blanco and Villarroya, 2002). Various reports have indicated the feasibility of NIR reflectance spectroscopy in estimating carotenoids content in maize (Brenna and Berardo, 2004),

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fatty acid composition in soybean seed (Choung et al., 2005) and intact rapeseed (Wu et al., 2006; Kim et al., 2007), and fatty acid content in intact seeds (Koprna et al., 2006). However, it has never been used to assess fatty acid content of powdered oil, such as DHA content which is important in milk powder production. The aim of this study was to explore the potential of models using NIR reflectance spectroscopy to estimate different concentration of DHA in powdered oil. All those powdered oil samples in this research were prepared in our own laboratory.

out using a temperature program as follows: initial temperature 45  C, hold 4 min, ramp at 13  C/min to 175  C, hold 27 min, ramp at 4  C/min to 215  C, hold 35 min. The standard used to draw calibration curve is docosahexaenoic acid methyl ester which was purchased from Sigma (St. Louis, MO). A series of standard concentration is 0.2, 0.4, 0.8, 1.6, 3.2 and 6.4 mg/mL, respectively. The identification of DHA in the analyzed oil samples was carried out by the comparison with retention times of standard. Quantification of DHA was performed using the external calibration curve. Spectra Collection

MATERIALS AND METHODS Samples Eighty-two (n ¼ 82) powdered oil samples were prepared by spray dried process. Before this process was carried out, the water phase, alga oil (contain DHA) and emulsifier should be mixed homogeneously and formed to an oil/water (O/W) system. The concentration levels of DHA of these samples were in the range of 5—12%. They were randomly divided into two sets, 66 samples were used to develop the calibration model, while the remaining 16 samples were used as validation set. All the samples were stored under 8  C before analysis. Methods GC Analysis for Docosahexaenoic Acid Ten mg of oil (accurate to 0.1 mg, extracted from the powder) were transferred into a 10 mL Teflon screw cap tube. Then 2 mL hexane and 1 mL methanolic sodium methoxide solution (27 mg/mL) were added in sequence, and the tube was sealed afterwards, the solution was mixed thoroughly by vortexing for 2 min. One milliliter of saturated sodium chloride solution was added and shaken vigorously for 30 s to terminate the reaction. After 30 min, the upper layer was transferred into a test tube, followed by addition of a small amount of anhydrous sodium sulfate to remove excessive moisture, then the dry hexane solution was transferred to a 1.5 mL glass vial for GC analysis (Cantellops et al., 1999). The analysis of FAMEs was performed on Agilent 6890 N gas chromatograph equipped with flame ionization detector and an Agilent autosampler 7683-B injector (Agilent Technologies, Little Fall, NY, USA). A fusedsilica capillary column CP Sil-88 (100% cyanopropylsilicone; 100 m  0.25 mm i.d., 0.20 mm film thickness, Varian Inc.) was used for the separation of fatty acid methyl esters. Hydrogen was used as a carrier gas at a flow rate of 1.8 mL/min. The injection volume was 0.2 mL with the spiltless mode. The temperature of the injector and detector was set at 250  C. The analysis was carried

Infrared spectra were scanned on a Nicolet 5700 FTIR spectrometer, using a BaselineTM Diffuse Reflectance Accessory and an InGaAs detector (Madison, USA). The IR measurements were performed within the region 4000—10 000 cm1. Gain was selected automatically. Happ—Genzel apodization was applied, mode zero filling was disabled, and the interferometer mirror speed was set at 1.5798 cm/s. Each spectra was obtained by an average of 64 scans with a resolution of 16 cm1. About 1g of the sample in powder form was individually filled in a glass sample vial (2 cm in diameter and 1.2 mm in wall thickness). The corresponding amount of powder was densely packed into the cup and compressed by closing it. Each sample spectra was collected four times at the room temperature (between 21  C and 25  C). The mean of those four spectra which were collected from the same sample is made to increase the signal to noise ratio and ready for use in the subsequential analysis. All spectra were collected by OMNIC7.0 software and recorded as log(1/R), where R is the relative reflectance. Data Analysis NIR spectra are affected by both the concentration of the chemical constituents and the physical properties of the sample, and the latter properties account for the majority of the variance among spectra while the variance due to chemical composition is considered to be small (Tigabu and Oden, 2002). It is necessary to perform mathematical pre-treatments to reduce the systematic noise, such as baseline variation, light scattering, path length differences and so on (Naes et al., 2002), and enhance the contribution of the chemical composition. In this study, four data pre-processing methods were applied comparatively, which were SNV, first derivative, second derivative and smoothing. SNV is a mathematical transformation method of the log(1/R) spectra used to remove slope variation and to correct for scatter effects (Chen et al., 2006). By calculating first and second derivatives, baseline drifts are eliminated and small spectral differences are enhanced.

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Calibration model between chemical data and NIR spectra were developed using partial least-squares (PLS) regression. Validation for the PLS analysis was performed by leave-one-out cross-validation method, in this method, one sample at a time was left out of the calibration set and then using the remaining samples to build a PLS model. The model was then tested on the sample that has been left out. This process was repeated until all of the samples in the calibration set had been used as the validation sample (Andersen et al., 1999). In order to avoid over-fitting of the models, the PRESS (predicted residual error sum of squares) function was used to select the optimum number of factors to use in a PLS method (Naes et al., 2002). The pre-process and calibration were carried out by using the TQ Analysis software. Calibration equations were computed by using the raw spectra (log(1/R)). Evaluation of the Calibration Model The best predicted calibration model was selected on the basis of minimizing the root mean square error of cross-validation (RMSECV) and increasing the correlation coefficient (r) between the measured and predicted parameters. The root mean square error of calibration (RMSEC) and the root mean square error of prediction (RMSEP) were used as criteria to evaluate the performance of calibration and the accuracy of equation. However, if there is a large difference between RMSEC and RMSECV may indicates that too many potential variables are used in the model and the noise is modeled (Lammertyn et al., 1998; McGlone et al., 2002; Gomez et al., 2006; Zou et al., 2006).

RESULTS AND DISCUSSION Near-infrared Spectra and GC Chromatogram Information It is generally known that analysis of individual components in these samples is practically impossible if only

with current analytical techniques. For this reason, when vibration techniques such as NIRS are used to determine a specific component, the resulting spectra are always greatly overlap, thus chemometric calibration techniques are necessary to extract relevant information. All the spectra have similar pattern (Figure 1). It is particularly hard to locate spectral information related to DHA content because of the complexity of the sample. However, the relevant information was believed to be exist in the spectrum and should be able to be extracted using PLS regression. Contributions from the DHA can be observed at 8265, 6847.4, 5840.1, 5172.6, 4726.6, 4329 and 4258 cm1. With respect to DHA, the absorption band at 8265 cm1 is attributed to CH stretch second overtone for CH, CH2, CH3 and CH ¼ CH structure. Band at 4726.6 cm1 is due to CH ¼ CH and C—H stretch combination tone, and two absorption bands at 4329.1 and 4258.1 cm1 are arising from CH combination and deformation tones. The wavelength of 6847 cm1 is the characteristic of CH3 and CH2 stretches. At 5840.1 cm1 correspond to CH stretch. Absorption produced at 5172.6 cm1 is the first overtone of the CH bond. The main absorption peaks of the samples were observed in the range of 4000—8500 cm1 (Figure 2) instead of the whole NIR region. The regions we chose to develop the model were suggested by the software automatically, they are respectively 4142—4177, 5789—5989, 6969—7062 cm1. Figure 3 displays GC chromatograms of the standard and a sample, which were used to identify and quantify the contents of DHA in our samples. The equation of the standard curve is y ¼ 1155.7x þ 50.908 (r ¼ 0.9999), from which it was obtained that the DHA content of samples employed in calibration and validation sets ranged from 5.79% to 11.61% and 6.3% to 10.37%, respectively. The average values are individually 8.37% and 8.58% with the RSD values of 1.35% and 1.07%, respectively. The detection limit of this method is 0.22 mg/L. The recovery for the DHA determination ranges from 90.2 % to 101.4% with a relative standard deviation of 1.4%.

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Figure 1. Near-infrared spectra of all powdered oil samples. Downloaded from fst.sagepub.com at HINARI on February 22, 2011

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0.010 0.005 0.000 −0.005 −0.010 −0.015 9000

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Figure 2. Processed spectra of all powdered oil samples (SNV þ first derivative þ Savitzky—Golay smoothing).

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Figure 3. GC chromatograms of the standard and a sample.

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Partial Least-squares Regression Calibration Model RMSECV

The numbers of factor, spectra pretreatment and spectral range were optimized to develop the PLS model by using the TQ Analyst 7.0 software package. The TQ Analyst is a powerful yet versatile software package for developing analytical methods for spectroscopic applications, including mid-infrared, near-infrared, far-infrared and Raman. The Professional Edition contains all of the algorithms that are typically used for calculating components concentrations and classifying spectra based on a set of standards.

1.2 1.0 0.8 0.6 0.4 0

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Figure 4. Effect of number of PLS factors on RMSECV for calibration model.

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Table 1. Calibration results of models using PLS. Model 1 2 3 4 5 6 7 8

Pretreatment

Factors

Corr. coeff. (r)

RMSEC

RMSEP

RMSECV

None 1st-Der þ No smoothing 1st-Der þ SG (9, 3) 2nd-Der þ No smoothing SNV SNV þ 1st-Der þ No smoothing SNV þ 1st-Der þ SG (9, 3) SNV þ 1st-Der þ N.D smoothing (3, 5)

8 4 4 2 5 5 5 4

0.964 0.966 0.949 0.923 0.921 0.977 0.968 0.928

0.35 0.34 0.41 0.51 0.52 0.28 0.33 0.49

0.63 0.69 0.70 0.59 0.60 0.66 0.59 0.60

0.53 0.51 0.50 0.92 0.57 0.45 0.44 0.55

12

1st-Der: first derivative; 2nd-Der: second derivative; SG: Savitzky—Golay smoothing; N.D smoothing: Norris derivative smoothing; SNV: standard normal variate; Corr.Coeff: correlation coefficient of calibration; RMSEC: root mean square error of calibration; RMSEP: root mean square error of prediction; RMSECV: root mean square error of cross-validation.

Calculated

Corr. coeff.: 0.96866 RMSEC: 0.333

5

Calibration Validation Correction

12

5

12

Actual Corr. coeff.: 0.94296 RMSECV: 0.446

Calculated

y = 0.9703x+ 0.2196

6

Calibration 6

Actual

12

Figure 5. Calibration result from the optimum PLS model.

Select the Optimum Number of Factor In the application of the PLS algorithm, it is generally known that the spectral range and the number of PLS factors are critical parameters. It has been previously determined that calibration performance depends on the spectral range used (Chung et al., 1998). The optimum number of factors was identified as the number of factors that gives a minimum root mean square error of cross-validation (RMSECV). Figure 4 shows RMSECV plotted as a function of the number

of PLS factors used for the determination of DHA content within the selected range. As expected, the RMSECV decreases sharply with the initial factors and gradually decreases as more DHA dependent spectral variation is incorporated into the calibration model. As the number of factors increases further, RMSECV begins to increase at 6 PLS factors. An increase in the RMSECV indicates that the data have been over-fitting by incorporating spectral information into the model that is not related to DHA. In this case, 5 PLS factors was chosen as the optimum.

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Select the Best Pretreatment Model

CONCLUSION

To enhance the reliability and veracity of the model, we carry out several different pretreatments of the spectra such as first derivative, second derivative, SNV, Savitzky—Golay smoothing, Norris derivative smoothing and so on. The performance of the final PLS calibration models was evaluated by the following methods. First, the quality of the calibration model was quantified by the root mean square error of calibration (RMSEC), the root mean square error of prediction (RMSEP) and the correlation coefficient (r) between the predicted and measured parameters. For calibration, a leave-one-sample out cross-validation was performed. In addition, the root mean square error of cross-validation (RMSECV) was used to determine the optimal model without ‘overfittedness’ or ‘underfittedness’. As far as the above concerned, eight different calibration models were constructed based on PLS with different spectra pretreatments and the number of factors and validated using cross-validation. The evaluation indexes of these models were summarized in Table 1. The calibration revealed that the different pretreatment had influence on the performance of model. The effect is probably contributed to the different signal-to-noise ratio of preprocessing procedures. It can be seen that Model 4 has the highest RMSECV as well as big difference between RMSEC and RMSECV, this maybe due to the fact that the second derivative brings more baseline noise, the signal strength decreases dramatically and the signalto-noise ratio decrease. From the results of Model 7 and 8, we can see that the processing method of Savitzky—Golay smoothing is better than Norris derivative smoothing, since the latter remove more spectral information so that lower the accuracy of calibration model. For the RMSECV of Model 5 and 8 is 0.576, 0.55, respectively. It is higher than the other models except for Model 4. It can be concluded that these models may not be chosen. However the Model 7 showed a satisfied performance with high correlation coefficient, the lowest RMSECV and small difference between RMSEC and RMSECV, which was superior to other models. The results also indicated that SNV pretreatment can improve calibration accuracy by eliminating noise and extracting information from spectral data. So the Model 7 was used for the calibration. The satisfied calibration and prediction result of the PLS model can be seen in Figure 5. The model has high correlation coefficient (0.968) and low RMSECV (0.44), RMSEP (0.59), respectively for calibration and prediction.

Near Infrared spectroscopy combined with a suitable preprocessing method and PLS regression is shown to be a promising method for predicting the content of DHA of powdered oil. The DHA content can be predicted well. The pretreatment of the data influences the performance of the model. In general, SNV combined with first derivation and Savitzky—Golay smoothing achieve a higher model accuracy compared to other pretreatments. Using NIRS, samples can be measured in less than 2 min, for the analysis of numerous samples, this method can replace chromatographic methods such as GC. And this rapid analysis will improve the efficiency of quality control and assurance. The development of this NIR equation for DHA is only a first step though the NIRS is a practical method. Therefore, future study will be directed to developing calibration models using more diverse samples which are distributed in a large concentration range and collected under various conditions.

ACKNOWLEDGMENTS This study is financially supported by National Key Technology R and D Program (2006BAD27B04), Changjiang Scholars and Innovative Research Team in University (No: IRT0540), and Nanchang University Testing Fund (No. 2008034).

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Food Science and Technology International 2010 Vol16 Issue2