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ICP-OES Analytical Needs in Agribusiness Determination of Macro and Trace Elements Levels in Honey from Lower Amazonian Region, Brazil
Gabriela Sousa Dourado, Victor Valentim Gomes, Maila Thais Vieira Maia, Arthur Abinader Vasconcelos, Kauê Santana da Costa, Kelson do Carmo Faial, Bruno Santana Carneiro, Paulo Sérgio Taube
April – June 2019 Volume 6 Number 23
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About Br. J. Anal. Chem. The Brazilian Journal of Analytical Chemistry (BrJAC) is a peer-reviewed scientific journal intended for professionals and institutions acting mainly in all branches of analytical chemistry. BrJAC is an open access journal which does not charge authors an article processing fee. Scope BrJAC is dedicated to the diffusion of significant and original knowledge in all branches of Analytical Chemistry. BrJAC is addressed to professionals involved in science, technology and innovation projects in Analytical Chemistry at universities, research centers and in industry. BrJAC publishes original, unpublished scientific articles and technical notes that are peer reviewed in the double-blind way. In addition, it publishes reviews, interviews, points of view, letters, sponsor reports, and features related to analytical chemistry. Manuscripts submitted for publication in BrJAC cannot have been previously published or be currently submitted for publication in another journal. For manuscript preparation and submission, please see the Guidelines for the Authors section at the end of this edition. When submitting their manuscript for publication, the authors agree that the copyright will become the property of the Brazilian Journal of Analytical Chemistry, if and when accepted for publication. BrJAC is Published by: VisĂŁo Fokka Communication Agency Publisher Lilian Freitas MTB: 0076693/ SP lilian.freitas@visaofokka.com.br Advertisement Luciene Campos luciene.campos@visaofokka.com.br ISSN 2179-3425 printed
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Editorial Board Editor-in-Chief
Marco Aurélio Zezzi Arruda Full Professor / Institute of Chemistry, University of Campinas, Campinas, SP, BR
Associate Editors
Cristina Maria Schuch Analytical Department Manager / Solvay Research & Innovation Center, Paris, FR Elcio Cruz de Oliveira Technical Consultant / Technol. Mngmt. at Petrobras Transporte S.A. and Aggregate Professor at the Post-graduate Program in Metrology, Pontifical Catholic University, Rio de Janeiro, RJ, BR Fernando Vitorino da Silva Chemistry Laboratory Manager / Nestle Quality Assurance Center, São Paulo, SP, BR Mauro Bertotti Full Professor / Institute of Chemistry, University of São Paulo, São Paulo, SP, BR Pedro Vitoriano Oliveira Full Professor / Institute of Chemistry, University of São Paulo, São Paulo, SP, BR Renato Zanella Full Professor / Dept. of Chemistry, Federal University of Santa Maria, RS, BR
Advisory Board
Adriano Otávio Maldaner Criminal Expert / Forensic Chemistry Service, National Institute of Criminalistics, Brazilian Federal Police, Brasília, DF, BR Auro Atsushi Tanaka Full Professor / Dept. of Chemistry, Federal University of Maranhão, São Luís, MA, BR Carlos Roberto dos Santos Director of Engineering and Environmental Quality of CETESB, São Paulo, SP, BR Gisela de Aragão Umbuzeiro Professor / Technology School, University of Campinas, Campinas, SP, BR Janusz Pawliszyn Professor / Department of Chemistry, University of Waterloo, Ontario, Canada Joaquim de Araújo Nóbrega Full Professor / Dept. of Chemistry, Federal University of São Carlos, São Carlos, SP, BR José Anchieta Gomes Neto Associate Professor / São Paulo State University (UNESP), Inst. of Chemistry, Araraquara, SP, BR José Dos Santos Malta Junior Pre-formulation Lab. Manager / EMS / NC Group, Hortolandia, SP, BR Lauro Tatsuo Kubota Full Professor / Institute of Chemistry, University of Campinas, Campinas, SP, BR Luiz Rogerio M. Silva Quality Assurance Associate Director / EISAI Lab., São Paulo, SP, BR Márcio das Virgens Rebouças Global Process Technology / Specialty Chemicals Manager - Braskem S.A., Campinas, SP, BR Marcos Nogueira Eberlin Full Professor / School of Engineering, Mackenzie Presbyterian University, São Paulo, SP, BR Maria das Graças Andrade Korn Full Professor / Institute of Chemistry, Federal University of Bahia, Salvador, BA, BR Ricardo Erthal Santelli Full Professor / Analytical Chemistry, Federal University of Rio de Janeiro, RJ, BR
Contents
Br. J. Anal. Chem., 2019, 6 (23)
Editorial Marco Aurélio Zezzi Arruda...................................................................................................................2-2
Interview Luiz Alberto Colnago, a prominent researcher in the Analytical Chemistry of Agricultural Products, gave an interview to BrJAC...............................................................................................................3-8 Point of View Analytical Demands for Residues and Contaminants in Food Products.........................................9-10 Renato Zanella
Letter Analytical Needs in Agribusiness.................................................................................................. 11-11 Fernando Vitorino da Silva
Articles Development of Spectrophotometric Method for the Determination of 17α-Methyltestosterone...12-19 Savaris, D. L.; Alberton, M. B.; de Matos, R.; Zalazar, M. F.; Duarte, D. J. R.; Lindino, C. A.
Evaluation of Calcium Alginate Microparticles for Copper Preconcentration prior to F AAS Measurements in Fresh Water......................................................................................................20-28 Colombo, M. B. A.; Porto, L. E. O.; de Carvalho, G. G. A.; Petri, D. F. S.; Oliveira, P. O.
Determination of Macro and Trace Element Levels in Honey from the Lower Amazonian Region, Brazil.............................................................................................................................................29-44 Dourado, G. S.; Gomes, V. V.; Maia, M. T. V.; Vasconcelos, A. A.; da Costa, K. S.; Faial, K. C.; Carneiro, B. S.; Vasconcelos Junior, N. T.; Taube, P. S.
Technical Note Pseudo-univariate Calibration as an Analytical Tool to Determine Antioxidant Activity: An alternative to DPPH method applied to the Evaluation from extracts of Turmeric Powder.................................45-50 Laczkowski, M. S.; Gonçalves, T. R.; Gomes, S. T. M.; Março, P. H.; Valderrama, P.; Matsushita, M.
Feature Pittcon Conference & Expo 2019 Celebrated 70 Years of Laboratory Science and Instrumentation Innovation......................................................................................................................................51-54 Sponsor Reports Multi-residue Pesticide Screening in Cereals using GC-Orbitrap Mass Spectrometry..................55-62 Thermo Scientific
Total Elemental Analysis of Food Samples for Routine and Research Laboratories using the Thermo Scientific iCAP RQ ICP-MS...........................................................................................................63-69 Thermo Scientific
Simplifying Mixed-Food Microwave Sample Preparation for ICP-MS Analysis.............................70-74 Milestone
Contents
Releases 1 Edition of the “Young Scientist in Analytical Chemistry” Award will take place at the 6th Analitica Latin America Congress..................................................................................................................... 75 st
Don’t miss the Largest Meeting of Analytical Chemistry in Latin America - 6th Analitica Latin America Congress............................................................................................................................................ 76 Thermo Scientific Exactive GC Orbitrap GC-MS System - The Frontier of Routine GC-MS.............. 78 Thermo Scientific iCAP RQ ICP-MS - Simplicity, Productivity and Robustness for routine labs in Agribusiness....................................................................................................................................... 80 Milestone ultraWAVE is the Benchmark in Microwave Digestion....................................................... 82 Pittcon Conference & Exposition........................................................................................................ 84 SelectScience® Pioneers Online Communication and Promotes Scientific Success since 1998.....................................................................................................................................86 CHROMacademy Helps Increase your Knowledge, Efficiency and Productivity in the Lab............... 88 Women in Science: The Federal University of Pelotas discusses the Challenges and Perspectives of Including New Talent.......................................................................................................................... 90 Notices of Books
............................................................................................................. 91
Periodicals & Websites ............................................................................................................. 93 Events
............................................................................................................. 94
Guidelines for the Authors ........................................................................................................ 96
Editorial
Br. J. Anal. Chem., 2019, 6 (23) pp 2-2 DOI: 10.30744/brjac.2179-3425.editorial.mazezzi
Marco AurĂŠlio Zezzi Arruda Full Professor Institute of Chemistry, University of Campinas (Unicamp) Campinas, SP, Brazil zezzi@unicamp.br Besides ensuring compliance with food and trade laws, food analysis also provides information about processing, contamination of foodstuffs, chemical composition, and quality control, all considered as important branches of analytical chemistry. Chemical composition and physical and sensory properties answer specific questions for quality control and regulation, and the choice of an analytical method for food analysis is dictated by the nature of sample (i.e., raw material, presence of oil, fat or fibers, solid or liquid, among others). With this perspective, this issue of BrJAC is devoted to some of these aspects for the benefit of the readers and for laboratories devoted to this task. Since its inception as the Brazilian journal devoted to all branches of analytical chemistry, Prof. Lauro Kubota has been the pioneer Editor-in-Chief of BrJAC. Prof. Kubota is now leaving this position, and I am taking over from him as Editor-in-Chief, with this issue. On behalf of the analytical chemistry community, I congratulate Prof. Kubota for the great and successful work as Editor-in-Chief of BrJAC. I hope to continue his example, albeit providing some changes naturally imposed due to the dynamism of the area. Enjoy the reading.
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Interview
Br. J. Anal. Chem., 2019, 6 (23) pp 3-8 DOI: 10.30744/brjac.2179-3425.interview.lacolnago
Luiz Alberto Colnago, a prominent researcher in the Analytical Chemistry of Agricultural Products, gave an interview to BrJAC Luiz Alberto Colnago Senior Researcher Embrapa Instrumentação, São Carlos, SP, Brazil luiz.colnago@embrapa.br Born in Itarana (Espírito Santo, Brazil), Luiz Alberto Colnago graduated in Pharmacy at the Faculty of Pharmacy and Biochemistry of Espírito Santo (FAFABES) in 1976. He obtained his Master’s and PhD degrees in Chemistry, both from the “Instituto Militar de Engenharia” of Rio de Janeiro (RJ, Brazil), in 1979 and 1983, respectively. He developed postdoctoral works in Biomolecular Magnetic Resonance at the University of Pennsylvania, Philadelphia, USA, in two periods: 1983 to 1985 and 1991 to 1992. Since 1986 he has been a researcher at the Brazilian Agricultural Research Corporation (Embrapa), and since 1993 a guest advisor at the Institute of Chemistry and the Institute of Physics, both at the University of São Paulo (USP) in São Carlos, SP, Brazil. Dr. Colnago is a specialist in in vivo or in vitro Nuclear Magnetic Resonance (NMR) applied to biological systems and in the development of NMR instrumentation for analysis of chemicals, food, fuels and other products. His researches also include the development and application of other physical methods for the analysis of biological systems of agricultural interest. His contribution to science was recognized through awards such as: “Highlight in NMR” (2014) by the Association of Magnetic Resonance Users (AUREMN); the “Postgraduate Award”, by the Institute of Chemistry of the University of São Paulo in São Carlos (IQSC/USP), for a publication that presented the biggest impact factor in 2012 in the area of Analytical and Inorganic Chemistry; the “Order of Merit Commendation Domingos Martins”, by the Legislative Assembly of the State of Espírito Santo, Brazil (2010); and prizes from the Embrapa Awards System (2001 and 1999). Dr. Colnago and his team are proposing the use of low field NMR using permanent magnets to investigate adulterations in fresh and processed foods, biofuels and biomaterials, and to monitor electrochemical reactions in situ, among other applications. In addition to the reduced cost of analysis, which can be tens of times lower than the costs of superconducting NMR equipment, low field NMR equipment allows fast and non-invasive analysis of in natura foods such as fruits, meats and seeds, as well as in the determination of the quality of industrialized foods such as olive oils, mayonnaise, Variety of products that can be analyzed by low sauces and jellies, without violation of the packaging field NMR 3
Interview
seal, i.e., directly in the commercial packaging. The researcher’s dream is that one day these NMR devices can be in supermarkets so that consumers can assess the quality of the products they are buying, just as they do today with balances to check the weight. The researcher also participated in a project funded by the Bill and Melinda Gates Foundation, which aimed to help reduce hunger in Africa by improving the transportation and conservation of food produced in the sub-Saharan region. The project had a team of experts in agriculture, basic sciences and engineering to propose innovative solutions for the production chains of milk, maize and cassava, which are the main foodstuffs for people in sub-Saharan Africa. How did your career begin? What were your motivations? My interest in science and technology began in my childhood. I spent a good part of my childhood in the garages of my uncle Florival, and a neighbor, Aristeu. In these workshops, they repaired virtually all types of devices – electrical and mechanical – such as watches, radios and electric and combustion engines, among other machines and equipment. During high school I was interested in the study of medicinal plants and I decided to do the course of Pharmacy. In this course, the Professor of Instrumental Analysis, João Batista, who was a PhD student at the “Instituto Militar de Engenharia” (IME) in Rio de Janeiro, convinced me to obtain a Master’s degree in Chemistry. In the experimental Master’s work, guided by Professor Richard Hollins, I worked on organometallic reactions with thallium. Because thallium is a very toxic element and its NMR spectra have non-standard NMR characteristics, I was trained to do my own spectroscopic analysis. In my doctoral program, under the guidance of Professor Peter Seidl, I began to work even further with NMR (carbon 13), in the development of rapid and non-destructive methods to determine the content and quality of oils in intact seeds. The purpose of these studies was to find high quality seeds for the production of biodiesel (prooil) and also to study the metabolism of these seeds in vivo during germination. At this time, in the late 1970’s, pulsed-NMR instruments were one of the few analytical instruments controlled by computers. In these instruments, the computer was also used to perform the Fourier transform of the data acquired in time domain. Since the electronics of integrated circuits was a technology still in its infancy, it had many technical defects. With this, the NMR instruments needed a lot of corrective maintenance, which was very expensive. So, I decided to do postdoctoral work at the University of Pennsylvania in the group of Professor Stanley Opella, who developed his own NMR spectrometers. In the postdoctoral work, in addition to doing the experimental part in solid-state NMR analysis of virus structure, I started to study electronics and helped with the construction and maintenance of the various home-made spectrometers in the laboratory. Upon returning to Brazil, I spent a brief period as a teacher at the “Instituto Militar de Engenharia”, and then I joined the newly created Research and Development Support Unit for Agricultural and Livestock Instrumentation (UAPDIA), currently Embrapa Instrumentation, in São Carlos (SP, Brazil). In this unit, I began to develop general instrumentation and, in the second year, I began to develop a low field NMR spectrometer for non-destructive determination of the oil content in corn seeds, under the order of Professor Geraldo Toselo, from the Department of Genetics of the “Luiz de Queiroz” Superior School of Agriculture (Esalq), a unit of the University of São Paulo (USP) in Piracicaba, SP, Brazil. This instrument was totally designed and built in the laboratories of Embrapa. We developed the magnet, the transmitter, the receiver, NMR probes, computer interface and the data Dr. Luiz Alberto Colnago and R&D FIT director at the Embrapa laboratory processing and control software. Then I started to develop a 4
Interview
prototype of a new NMR spectrometer, funded by the Brazilian Innovation Agency (Finep). This prototype was transferred to a private company, Gil Equipamentos Industriais, which started the serial production of this instrument. However, because of the high cost of this device at that time, and the lack of specialized personnel in the research and development laboratories, this company sold four instruments. With the knowledge acquired in the construction of this spectrometer, I started to make instrumentation dedicated to the development of new high-speed analytical methods that could not be performed on existing commercial spectrometers. Currently, these methods are being incorporated into the productive sector by a new Brazilian company, Fine Instruments Technology (FIT), which has already sold tens of low field NMR spectrometers to companies in Brazil and abroad. For you, what has changed in students’ profile, ambitions and performance since the beginning of your career? At the beginning of my career, Brazil was still crawling in science and only a few universities offered Masters and, more rarely, Doctoral programs. Most university professors did not hold a Master’s degree, much less a PhD degree. Thus, the job market for Masters and PhDs as professor-researchers at universities or research institutes was very broad. In many cases, PhDs could be hired for “notorious knowledge” without public tender. Since then, scientific research has multiplied hundreds of times and Brazil is already moving toward the stage of scientific maturity. Postgraduate programs have increased greatly in the country. According to data released in 2016 by the Center for Strategic Studies and Management Science (CGEE), a study center linked to the Brazilian Ministry of Science, Technology, Innovation and Communications (MCTIC), Brazil formed about 50,000 Masters and approximately 17,000 Doctors in 2014, which is five times more than it was in 1996. However, the low economic development in Brazil and the consequent difficulty of placing new graduates in the labor market, have led many graduates to enroll in postgraduate courses, even if they do not have the desired profile for a scientific career. However, it has not been easy to get a job, even though you have a PhD degree. This is because state universities or even private universities and research institutes already have their professors-researchers with PhD degrees practically complete. In addition, a very small number of postdoctoral fellowships are being offered, which has led many PhDs to work in subemployments, which sometimes do not even require a high school course. Could you briefly comment on recent developments in agribusiness research, considering your contributions? I consider the introduction of new methods of low field NMR for rapid and non-invasive analyses of food and other agricultural materials is the main contribution of the research group I coordinate. We have already shown that the low field NMR has the potential to rapidly determine some food properties, such as the degree of sweetness of a fruit or even the juice potential that this fruit, even without touching it. What are your lines of research? Could you exemplify a few of them? My main lines of research involve the use of NMR in practically all its core areas, such as low field NMR relaxometry, high resolution NMR spectroscopy in solids, liquids and heterogeneous materials, and Dr. Luiz Alberto Colnago and the low field NMR spectrometer developed NMR imaging, known as magnetic resonance imaging. Besides the by him, at the Embrapa lab applications in issues involving agriculture and livestock, we also carry out studies of physical and chemical phenomena induced by the magnetic field of the spectrometers. Recently, we observed that when analyzing electrochemical reactions in situ by NMR (EC-NMR), 5
Interview
the reactions in this condition are much faster than reactions performed outside the spectrometer. This increase in the reaction speed occurs because of the simultaneous presence of the electric field of the electrochemical system and the magnetic field of the spectrometer. When these two fields are present, the magnetic hydrodynamic effect (MHD) is obtained, which causes the reaction medium to move similarly to mechanical agitation. With this, the electrochemical reaction inside the NMR spectrometer becomes much faster than outside it. Do you stay informed about the progress of scientific research in Brazil? For you, what are the latest advances and challenges in scientific research in Brazil? As I said earlier, scientific-academic research has made great advances in the country over the past 30 years. Nowadays, we have developed studies in practically all areas and subareas of knowledge. However, there is still little scientific and technological research in private companies, which leads Brazil to continue importing most technologies in the industrial sector. Could you briefly describe NMR technology? NMR is a spectroscopy technique based on the absorption of electromagnetic radiation in the radio frequency region. The main difference between NMR and spectroscopy in the visible, ultraviolet and infrared regions is that the observation of the sample signal must be subjected to a magnetic field so that the energy levels are separated. In addition, the separation and width of the spectral lines and the detection limits are dependent on the strength of the magnetic field. Thus, a spectrometer with high resolution and low detection limit operates with high magnetic field, which is only achieved with superconducting magnets. These magnets have to be continuously cooled with liquid helium, which is an imported and expensive material, a factor that leads to one of the main difficulties of maintaining this kind of spectrometer in Brazil. In this way, we opted for the construction of such as whole fruits and processed foods of up low field instruments, with permanent magnets, to hundreds of grams. For example, relaxometry which are cheaper and with can be used to determine the lower maintenance costs. sweetness of a fruit, since the “… we opted for the construction In these instruments, we relaxation of water in a sweet of low field instruments, with do not have the spectral fruit is usually much less than the permanent magnets, which resolution necessary to see relaxation of water in a fruit that are cheaper and with lower the different absorptions due is not sweet. The relaxation of maintenance costs …” to the chemical differences fat in meat is also quite different in the molecules, nor the from the relaxation of water in limits of detection of the the same meat, and this can superconducting devices. Therefore, the low be used to quantify the fat content in a piece of field analyses are based on the relaxation times meat, even if it is in a package. and the use of large volume and mass samples In your opinion, will there be more investment in biodiesel production or will fossil fuel consumption continue? The first time I worked with biodiesel in the early 1980s, the use of biodiesel was mainly based on the economic and strategic aspects, because of Brazil’s heavy dependence on imported oil, which was very expensive. With the fall in the price of oil in the following years, the Brazilian program for biodiesel was extinguished, but this program has resurfaced in the last decade because of the importance of using biodiesel for the environment. In addition to the economic and strategic aspects, the need to search for renewable and less polluting fuels was highlighted. Thus, I believe that the production and commercialization of biodiesel will not end while much of the transportation of consumer goods and people is carried out by diesel-powered vehicles in Brazil. I believe that the biodiesel/diesel mix, with a 6
Interview
higher proportion of biodiesel, should occur in the region of the country where the raw material, mainly soy, is produced, that is, in the Midwest. This would minimize both the transportation cost of diesel to the biodiesel-producing regions, and the transportation of biodiesel to all regions of the country. That is, the use of biodiesel would have greater economic advantage. Today, what is the path Brazil should take to produce biodiesel? Is it still soybeans? I believe that there is currently no viable alternative high productivity, but has high water demand to large-scale biodiesel production and is limited to the Amazon other than soybeans. Although Region; macaúba, another “I believe that there is currently soybeans produce less than palm tree with high potential no viable alternative to largehalf a ton of oil per hectare, it is a for producing vegetable oil, can scale biodiesel production other crop that extends to all regions be grown in all regions of the than soybeans.” of Brazil, and there is already country; however, it still needs a a great deal of knowledge lot of research and technology about its entire production chain. Other options to achieve large-scale production and could could be: the palm tree called “dendezeiro”, from replace soy. which the “dendê” oil is extracted, which has You have received some awards. What is it like to receive this kind of recognition? What is the importance of these awards in the development of science and new technologies? Yes, I received awards from scientific societies and the Brazilian Legislative Power. Awards are always an incentive, not only for those who receive them, but also for researchers who seek to have their work recognized at some point. So, awards are very important in any area of science. I believe that the award/recognition of the work of researchers and professors is far below what would be desired. Even with the difficulties of doing research in a developing country such as Brazil, there are many important studies being carried out in our country, and they are not always recognized by the academic community, civil society or federal agencies. Each award granted to a researcher arouses in many young people the desire to also receive recognition. With this, talents so necessary to research can be attracted to scientific and technological areas. For you, what is the importance of the support of funding agencies for the scientific development of the country? In a country with scarce private incentives, the state development agencies are practically the only sources of resources for scientific and technological research. There would be no research in Brazil without the contribution of agencies such as the National Council for Scientific and Technological Development (CNPq), the Coordination for the Improvement of Higher Education Personnel (CAPES), the Brazilian Innovation Agency (FINEP) and the State Research Foundations (FAPs). The State of São Paulo Research Foundation (FAPESP) has reduced, but has not stopped, funding good research projects even in critical periods of the economy. The maintenance of funding, although at levels lower than necessary, is the only way to give survival to the research of the country, producing new researchers and generating new advances in knowledge. The breakdown of this financing chain is one of the main problems of research in Brazil. At the moment, the investment in scientific research in Brazil is being reduced. How do you see this situation, and what would you say to young researchers? Federal investment in research and development in Brazil has always faced oscillations, with short periods of high investment and long periods of low investment. From 2004 to 2013, mainly, we observed that the Federal Government invested heavily in scientific research, with great renovation and expansion of laboratories and research centers throughout the country, and great investment in 7
Interview
the training of personnel, including training abroad. In this period of strength, the country received hundreds of scientific delegations from developed countries, who sought collaborations in the most varied areas of knowledge. The great amount of equipment in the laboratories and the great facility for obtaining resources for research and for the training of personnel impressed the visitors. However, there is currently a resource constraint, both for the maintenance of the implanted infrastructure and for the provision of research grants to follow the continuous flow of personnel training. Currently, there are a large number of Masters and PhDs (about 70,000) graduated annually in the country, who are having difficulty working in their areas and who sometimes accept underemployment. Practically, there are no jobs in public and private universities for newly graduated PhDs, just as there are few jobs in industries and service areas for researchers/PhDs. However, just regret is not enough. During crises, great advances are made. With the falling employability rate, one way would be to invest in innovative entrepreneurship, even with all the challenges of Brazil’s costs, such as bureaucracy, high cost of credit and other difficulties. To boost these initiatives, federal agencies could offer incentives with subsidized or non-refundable resources, as does FAPESP for many technological innovation projects in São Paulo State. Investment in creating innovative small companies, which is becoming a trend in Brazil, is one of the most important ways to increase the employability of most of the PhDs being prepared in the country.
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Point of View
Br. J. Anal. Chem., 2019, 6 (23) pp 9-10 DOI: 10.30744/brjac.2179-3425.point-of-view-rzanella
Analytical Demands for Residues and Contaminants in Food Products Renato Zanella Full Professor Laboratory for Pesticide Residue Analysis (LARP) Chromatography and Mass Spectrometry Research Group (CPCEM) Department of Chemistry, Federal University of Santa Maria Santa Maria, RS, Brazil renato.zanella@ufsm.br Food marketing increasingly requires the rapid and comprehensive analyses of residues and contaminants, particularly pesticides, veterinary drugs, mycotoxins and heavy metals, in fresh foods as well as in processed products. To safeguard the well-being of consumers, good production practices and adequate analytical control are crucial to enable food producers to gain access to more markets, contributing to economic development. Today, consumers have the right to expect the foods they consume are accessible, safe and of high quality. Food safety and quality are topics of growing interest to consumers, government agencies and producers. Controlling food residues and contaminants requires broad and sensitive multiresidue methods to confirm their occurrence with confidence and at very low concentration levels, combining modern sample preparation techniques with quick and cost-effective determinations. Residues are organic compounds that appear in food because of their use during production or storage, such as pesticides and veterinary drugs. Contaminants are compounds generated naturally during production, storage or food processing, such as mycotoxins and marine biotoxins, or by anthropogenic activities, such as dioxins and polychlorinated biphenyls. Multiresidue methods are necessary in national food regulatory control systems because they allow food inspection and food safety management to prevent or minimize food safety risks. For prohibited compounds or their metabolites, minimum required performance limits are established to show the levels the analytical methods need to achieve. The complexity and variety of matrix composition of food samples, low concentration of analytes and low required maximum residues levels make it difficult to simultaneously analyse several classes of compounds at the same time. The development of generic extraction procedures combined with multiresidue determination can reduce the number of analyses per sample, saving time and costs. Strategies are needed that update the methods to include newly identified compounds in the validated methods. The development of multiresidue methods including analytes of current interest allows us to ascertain the quality of food, indicating whether Good Production Practices have been observed along the food production chain, giving greater consumer confidence on the risks of consuming a particular food. The environment and inputs used in the production of food products can also be important sources of contamination, especially metals and persistent organic compounds, and these must be analyzed at very low concentration levels that require modern analysis techniques. The establishment of fast, efficient and comprehensive methods, including the use of different complementary analysis techniques, requires a unified sample preparation step that allows a wide range of compounds to be determined. Analytical methods have a role in assuring food safety of food products. Food safety is the absence or acceptable levels of chemical, physical or microbiological hazards in food that may harm the health of consumers.
9
Point of View
Prohibited additives and counterfeiting harm consumer health and cause serious commercial damage. Also important are the problems of products formed during processing, such as acrylamide, the presence of allergens, prohibited preservatives and mycotoxins. Another important source of contamination are the materials that are already in contact with food or intended to be brought into contact with food. These materials can transfer their constituents to the food products. Food comes into contact with many materials, such as machinery to process food, packaging materials and kitchenware, during its production, processing, storage, distribution, preparation and serving for consumption. Food contact materials should be sufficiently inert to avoid their constituents influencing food quality, and in the process affecting consumer health. To ensure the safety of these materials, appropriate analytical methods are required to investigate the possible contaminants released into the food products. Even very low concentrations of released compounds can be harmful to human health and can negatively influence food quality, changing food composition, taste and odour in an unacceptable way. The use of new sorbents to obtain cleaner extracts even for very complex food matrices open the possibility of simplifying the sample preparation step for residues and contaminants analyses. In general, highly sensitive and selective gas or liquid chromatography with tandem mass spectrometry using a triple quadrupole analyzer are used for this kind of analyses. To screen non-target and unknown compounds, chromatographic techniques coupled with high-resolution mass spectrometry have been used, generating much information that can be important for food safety. Considering the current needs for establishing efficient analytical methods for determining residues and contaminants to guarantee food safety, the challenge is to improve the available methods in order to achieve the requirements established by legislation, reducing cost and time even in the case of very complex matrices and difficult compounds. The use of suitable methods and training people with experience in developing and applying efficient analytical methods are important and make analyses results much more reliable.
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Letter
Br. J. Anal. Chem., 2019, 6 (23) pp 11-11 DOI: 10.30744/brjac.2179-3425.letter.fvsilva
Analytical Needs in Agribusiness
Fernando Vitorino da Silva Chemistry Laboratory Manager Nestle Quality Assurance Center – Brazil fernando.silva2@br.nestle.com Sustainable agriculture is the efficient production of safe, high quality agricultural products in a way that protects and improves the natural environment, the social and economic conditions of farmers, and their employees and local communities, as well as safeguards the health and welfare of all farmed species. By supporting sustainable development in agriculture, it is possible to contribute to the processes that increase the world’s access to higher food quality and, therefore, contribute to longterm social and economic development, preserving the environment for future generations. This set of practice, at its highest level, is oriented along four natural capital themes: soil, biodiversity, greenhouse gases emissions, and water. Sustainable initiatives build a continuous improvement approach to reach sustainable intensification of agriculture production systems. Managing food safety and quality in agricultural production implies that we inform and train farmers and suppliers on pre- and postharvest practices, including storage and transportation of raw materials, ensuring the application of best practices to maintain high quality of raw materials. To control the efficiency of these preventative actions on the food chain, quality management should apply controls fitted for this purpose. Considering controls implemented on whole food chain production, analytical science has an important role in assessing compliance status with strict quality standards and controls of residue levels or contamination by microbiological pathogens, mycotoxins, heavy metals, and pesticides. Food safety science has experienced intense development over the last 20 years and most of this improvement is associated with advance of analytical science and laboratory instrumentation. Several controls and inspection not feasible at past, nowadays became reality on supply chain of food industry, where agribusiness acts as big player of raw material sourcing. Today, by using rapid methods for multi-screening procedures, veterinary drugs or mycotoxins can be quickly controlled at affordable cost on commodities like milk and cereals. The advance of instrumentation also allows remote monitoring of nutrient soil condition, on frame of precision agriculture, avoiding use of classic and time-consuming methods for test control. The availability of portable devices and rapid methods empowers the farmer to take quick action in quality control, bringing complex tests usually only performed in laboratories to the field. This will be become a trend for the next generations.
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Article
Br. J. Anal. Chem., 2019, 6 (23) pp 12-19 DOI: 10.30744/brjac.2179-3425.AR.137-2018
Development of Spectrophotometric Method for the Determination of 17α-Methyltestosterone Daniela Langaro Savaris1, Matheus Borghezan Alberton1, Roberto de Matos2, Maria Fernanda Zalazar3, Darío J. R. Duarte3, Cleber Antonio Lindino1 Laboratório de Estudos em Química Analítica Limpa. Grupo Interdisciplinar de Pesquisas em Fotoquímica e Eletroquímica Ambiental. Universidade Estadual do Oeste do Paraná, Rua da Faculdade 645, 85903-000, Toledo, PR, Brazil 2 Departamento de Química, Universidade Estadual de Londrina, Rodovia Celso Garcia Cid 380, 86057-970, Londrina, PR, Brazil 3 Laboratorio de Estructura Molecular y Propiedades. Universidad Nacional del Nordeste, Avenida Libertad 5460, Corrientes, Argentina 1
Graphical Abstract Contamination of waters by 17α-methyltestosterone (MT) has increased in recent years due to the increase in the fish farms production. This work describes the determination of the MT hormone by a new spectrophotometric method in the visible region, after the reaction with potassium nitroprusside with the generation of a compound having maximum absorbance at 400 nm. To understand the process of the compound formation, geometry optimizations were performed at MPW1PW91/6-311+G (2d,2p) level of theory and the calculations showing that the NO group of nitroprusside bonds Optimized structure of the formed to the carbon with double bond adjacent to the carbonyl of compound between nitroprusside ion 17α-methyltestosterone. In this method, MT was determined and 17α-methyltestosterone at the concentration between 8.0 x 10-8 and 2.7 x 10-7 mol L-1 with a coefficient of determination of 0.9946. The relative standard deviation for the repeatability in nine replicates was 2.37% for [MT] = 1.50 x 10-7 mol L-1. Limit of detection (LOD) was calculated as 1.75 x 10-8 mol L-1 and the limit of quantification (LOQ) was established as the lowest value of the linear concentration range (8.0 x 10-8 mol L-1). This spectrophotometric method was suitable according to the analysis of analytical figures of merit, it has low cost and can be applied to samples of fish farm waters with portable spectrophotometers for in situ measurements, increasing the environmental control with rapid analysis. Keywords: hormone, fish farm, analytical method. INTRODUCTION The use of the synthetic hormone 17α-methyltestosterone (MT) (Figure 1) in fish farms to induce male sex cultures has been going on for a long time [1]. Male specimens are of greater economic interest since they have higher growth rate given that high-energy losses associated with female gonadal development and reproduction are avoided [2].
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Development of Spectrophotometric Method for the Determination of 17α-Methyltestosterone
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However, the intensive use of MT added to feed (60 mg kg-1) in fish farms, still poses concerns regarding the risks to consumers and environmental health [2]. The routes of contamination of water involve uneaten food and elimination of the non-metabolized hormone [3]. Steroid hormones such as MT are quickly absorbed into the sediments due to their hydrophobic nature and its elimination in water after its Figure 1. Molecular structure of application is due to adsorption in sediment, biotransformation or 17α-methyltestosterone. photodegradation processes and are dependent on several factors Source: ChemSpider (Copyright Royal Society of Chemistry, 2018) such as pH and organic content of the medium, presence of Fe (III) and salinity [2-6]. The hormone 17α-methyltestosterone to induce several developmental and biochemical effects in zebrafish early life stages. The most important developmental effects included tail malformations, edemas, abnormal development of the head and hatching delay [7]. The sexual changes caused by MT in crocodiles in their habitat demonstrated the increase in the proportion of males in relation to the control with the introduction of this hormone coming fish farms [8,9], indicating the need for control and rapid determination of MT in environmental compartments. Therefore, the need to determine the levels of MT in water and its implication in environmental, emphasizes the importance of appropriate analytical methods that can quantify MT with reliability, speed and low cost. The most widely used analytical methods for quantifying MT are based on chromatographic techniques with UV detection or coupled mass spectrometry [10-15] or chemiluminescence [16], as well as electrochemical methods in the determination of 17α-methyltestosterone [17]. These methods have versatility, sensitivity and efficiency, but it has as disadvantages the need for using potentially toxic solvents or reagents or harmful to the environment and instrumentation with high cost, often not portable, leading to the search for more environmentally friendly methods that can be readily accessible. Spectrophotometry in the visible region is a technique that can be used directly in the productive sector of fish farming due to the lower cost, ease of operation and staff training besides the portability, allowing the control of MT levels directly in the property. There are few studies involving the determination of MT by spectrophotometry among which methods in the UV region at 241 nm [18,19], but several organic molecules present in the sample matrix can absorb in the ultraviolet region, with decreasing the resolution of the analytical method. Methods that use the visible region can have less interference and lower cost and are essential for application in several fields of the productive sector. In this work, the development of a spectrophotometric method for the determination of MT was based on the reaction with the NO group of the potassium nitroprusside ([Fe(CN)5NO]2-) in alkaline medium to give the anion compound [20] and the formed compound absorbs in the visible region. To provide a better understanding to the experimental UV-VIS spectrum, a theoretical study was carried on MT-nitroprusside, since there is no mention of this compound in the literature. MATERIALS AND METHODS Chemical reagents were of highest purity (>99%). The water used for preparing stock solutions or dilutions was distilled and purified by reverse osmosis (ADAMO, water resistance: 10 MW cm-1 at 25.0 °C). The stock solution of MT was prepared from pharmaceutical standard (101.5 ± 2.0% purity) by diluting in ethanol (99%, Sigma-Aldrich). Appropriate volumes of stock solution were transferred to volumetric flasks using micropipette (Labmate, ± 0.82%), adding then enough potassium nitroprusside solution (5% w/v) to 1:1 stoichiometry with the MT [20], and diluted with NaOH solution (30% w/v) with final pH of 13.0. Measures have been taken in spectrophotometer Shimadzu UV-1601 PC, double beam spectrophotometer (resolution of ± 1 nm). The spectra of methyltestosterone and nitroprusside solutions were obtained before and after the 13
Article
Savaris, D. L.; Alberton, M. B.; de Matos, R.; Zalazar, M. F.; Duarte, D. J. R.; Lindino, C. A.
formation of the product at wavelength between 200 and 700 nm to characterize and evaluate each possible overlapping band. The stability of the nitroprusside solutions and the formed compound were evaluated by measuring the absorbance of the solutions over time. Geometry optimizations were performed at MPW1PW91/6-311+G (2d,2p) level of theory. The stationary points on the potential energy surface were characterized by calculating the Hessian matrix and analyzing the vibrational normal modes. The excitation transitions of MT-nitroprusside compound in implicit water were calculated using time-dependent density functional theory (TD-DFT) at the MPW1PW91/6-311+G (2d,2p) level of theory [21]. This functional was used as it provides good estimates for the excited states of interest for this work [22]. To obtain the excitation spectra of the studied compound, six excited states were calculated [23]. The solvent (water) was simulated by means of the self-consistent reaction field (SCRF) method based on the polarizable continuum model (PCM) [24,25]. All calculations were performed with the Gaussian 09 software [26]. The selectivity of the method was evaluated from study of interfering of organic functional groups in reaction with the nitroprusside of the proposed method for 17α-methyltestosterone for the substances: acetic acid, acetone, methylethylketone and ethanol. For this, 1.0 mL of pure substance (>99.0% purity) was mixed with 1.0 mL of 5% (w/v) sodium nitroprusside and diluted in 30% (w/v) sodium hydroxide solution in volumetric flask and the absorbance this solution was read in spectrophotometer (between 200 and 700 nm). The 17α-methyltestosterone solution (4.0 x 10-6 mol L-1) was used as a control and the mixture of the reagents was used as blank. The interference of iron, aluminum and copper ions was evaluated during the recovery study with a sample of fishpond water. The linearity was assessed by means of standard 17α-methyltestosterone curves in water with the points of the curve measured in quintuplicate. To determine the limit of detection (LOD), it was used the equation of the calibration curve data of higher sensitivity, obtained from the standard deviation of the arithmetic mean of the absorbance of the blank and the slope of the calibration curve multiplied by 3.3. The limit of quantification (LOQ) was established as the lowest value of the linear concentration range. The precision of the method was evaluated by estimating the repeatability in nine replicates at the concentration of 1.50 x 10-7 mol L-1 of 17α-methyltestosterone and for three levels of 17α-methyltestosterone concentration (1.2 x 10-7 mol L-1, 2.0 x 10-7 mol L-1 and 2.7 x 10-7 mol L-1). Accuracy was assessed by methyltestosterone pattern recovery studies added in samples of fishpond water in which there was no use of the hormone. The samples were collected in fish farm and characterized by the content of total iron, total copper and total aluminum by X-Ray Fluorescence, S2 Picofox, with Mo K radiation, 50 kV voltage and current of 602 mA. These metals could interact with the nitroprusside and could interfere with the analytical signal [27-29]. The fishpond water conductivity was measured by a LUTRON CD-4303 conductivity meter (2% accuracy of the full scale), calibrated with standard KCl solution (± 0.5%); the pH was measured by a LABMETER PHS-3B pHmeter (resolution ± 0.01) and combined glass electrode, calibrated with buffer solutions pH 7.0 (± 0.05) and pH 4.0 (± 0.02); and the turbidity was measured using a Tecnopon TB1000 digital turbidimeter (resolution 0.8) calibrated with standards between 0.1 and 1,000 NTU. The proposed method was compared with the method of determination of 17α-methyltestosterone by UV from the analytical curves obtained from solutions of the standard. RESULTS AND DISCUSSION Figure 2 shows the absorption spectrum of the reaction product between nitroprusside and MT in alkaline medium, in which the maximum absorption is observed at 400 nm.
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Development of Spectrophotometric Method for the Determination of 17α-Methyltestosterone
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Figure 2. Visible spectrum of compound between nitroprusside solution (5% w/v) and MT (2.0 x 10-7 mol L-1). Alkaline medium (pH = 13.0) and temperature of 25.0 °C.
Spectrophotometric measurements for potassium nitroprusside (5.0 x 10-5 mol L-1) showed absorption at 271 nm concerning the nitro group and at 222 nm attributed to the strong absorption of the cyan group. The MT showed absorption at 241 nm and refers to enone, characteristic for the p-p* electron transition of unsaturated ketones, with e = 17000 L mol-1 cm-1 [30]. These results demonstrate that pure MT solution and pure nitroprusside solution do not show absorption in the visible region and do not interfere with the measurements. Stability tests indicated that the absorbance of the MT-nitroprusside compound has a decay constant of 0.0039 min-1 (Figure 3). The absorbance in 271 nm of the 5% (w/v) sodium nitroprusside solution drops to 3.65% day-1, showing that the solution should be used on the same day of preparation.
Figure 3. Decay of the MT-nitroprusside compound absorbance (2.04 x 10-7 mol L-1) in alkaline medium (pH = 13.0) and 25.0 °C. R2 = 0.9967.
In the alkaline medium, the nitroprusside ion is in equilibrium according to Equation 1 [31]. In the same way, the enolization of acetone group occurs (Equation 2) and the reaction between them generates the product [Fe(CN)5ON=CHCOCH3]4-, which is also in agreement as proposed by Feigl [20]. [Fe(CN)5NO]2- + 2 OH- D [Fe(CN)5NO2]4- + H2O (1) CH3COCH3 + OH- D CH3COCH2- + H2O
(2)
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Savaris, D. L.; Alberton, M. B.; de Matos, R.; Zalazar, M. F.; Duarte, D. J. R.; Lindino, C. A.
The proposal is that 17α-methyltestosterone follows the same compound formation mechanism with nitroprusside with the attack of the NO group on carbon with double bond adjacent to the carbonyl. Figure 4 shows the optimized structure of the formed compound between nitroprusside ion and MT. To understand the absorption spectra observed, it was analyzed through theoretical calculations the most important electronic excitation in MT-nitroprusside compound, within the vertical approximation of Franck-Condon. Our calculations showing that the NO group of nitroprusside bonds to the carbon with double bond adjacent to the carbonyl of 17α-methyltestosterone. Figure 5 illustrates the main molecular orbitals involved in the electronic transition studied. The HOMO (-1) orbital is mainly localized on the metallic cation, while the LUMO orbital is more delocalized in NO and C‒C=O. The position of the orbitals in this compound may help to explain the electronic excitation. There is apparently a superposition of the orbitals HOMO (-1) and part of the LUMO located on NO fragment, which is expected to facilitate the excitation. In other words, the HOMO (-1) that has a large percentage of 3d(Fe) atomic orbital character overlaps with π*(NO). Similar vertical excitation has been detected both theoretically and experimentally for ground state of the nitroprusside compound [23,32].
Figure 4. MT-nitroprusside compound in implicit water (l oxygen); (l nitrogen); (l iron); (l carbon)
Figure 5. (a)
HOMO (-1) and (b) LUMO orbitals of the MT-nitroprusside compound in implicit water (l oxygen); (l nitrogen); (l iron); (l carbon)
The interference of organic functional groups in the reaction with nitroprussiate showed the maximum absorbance for the ketone (lmax = 403 nm), methylketone (lmax = 396 nm) and alcohol groups (lmax = 399 nm), but not of carboxylic groups (lmax = 215 nm). This result points to the careful selection of substances 16
Development of Spectrophotometric Method for the Determination of 17α-Methyltestosterone
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used in the extraction process of 17α-methyltestosterone in different matrices to avoid agents that may interfere with the analytical signal. However, considering possible 17α-methyltestosterone degradation processes in the environment, several studies in the literature indicate that there is no formation of ketones that could interfere with the analytical signal obtained by the proposed method [33-36]. Figure 6 shows the analytical curve obtained for different concentrations of MT, after the reaction with nitroprusside at l= 400 nm.
Figure 6. 17α-methyltestosterone analytical curve (quintuplicate). Temperature of 25.0 °C. Linear equation: y = -0.24 + 2.96325x106[MT]. Error bars of 5%.
The linear concentration range is between 8.0 x 10-8 and 2.7 x 10-7 mol L-1 (24.2 µg L-1 and 81.7 µg L-1) with determination coefficient of 0.9946. This concentration range is adequate because the levels of methyltestosterone in the environment may be very low, between 5.1 x 10-8 and 3.3 x 10-7 mol L-1 [37]. The relative standard deviation for the repeatability in nine replicates (1.50 x 10-7 mol L-1) was 2.37%. For three levels of concentration and in triplicate, tests of repeatability of MT showed that the values of relative standard deviation were 7.8% (1.2 x 10-7 mol L-1), 1.9% (2.0 x 10-7 mol L-1) and 1.5% (2.7 x 10-7 mol L-1). Limit of detection (LOD) was calculated as 1.75 x 10-8 mol L-1 (5.29 µg L-1). The limit of quantification (LOQ) was established as the lowest value of the linear concentration range (8.0 x 10-8 mol L-1). Table I presents the comparison of LOD for MT in waters in some articles. Table I. LOD values of MT in water reported in the literature Method
LOD (µg L-1)
Reference
UV-HPLC
10.0
[5]
Luminescence
10.0
[16]
Hg electrode
3.07
[17]
UV-HPLC
3.60
[38]
visible
5.29
This work
The mean recovery was calculated as 101.5 ± 0.5% (triplicate) for the MT concentration of 2.4 x 10-7 mol L-1 added in surface water collected from fishpond (pH = 6.3, conductivity = 40.8 mS cm-1, turbidity = 8.7 NTU and ionic strength of 0.01 mol L-1) and indicated that the concentration of 1.46 x 10-3 mol L-1 of Fe3+, 1.89 x 10-5 mol L-1 of Cu2+ and 4.64 x 10-3 mol L-1 of Al3+ found in this sample do not interfere with the measurements. 17
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Savaris, D. L.; Alberton, M. B.; de Matos, R.; Zalazar, M. F.; Duarte, D. J. R.; Lindino, C. A.
The results of the determination of 17α-methyltestosterone in water samples obtained by the proposed method were compared with a direct spectrophotometric method in the ultraviolet region and are presented in Figure 7. The results were evaluated by Student’s t-test for differences in the confidence level of 95% and showed that there was no statistically significant difference between the results. The slope of the linear curve near 1 indicates a high correlation between the methods.
Figure 7. Comparison between the proposed method (visible) and the direct UV method. R2 = 0.9998 with slope = 1.012.
CONCLUSION The reaction between nitroprusside and 17α-methyltestosterone resulted in a yellow colored product that can be used in situ spectrophotometric determinations in the visible region and considered suitable according to the analytical figures of merit. Theoretical calculations reveal that the absorption spectrum observed for MT-nitroprusside compound is mainly due to 3d(Fe)→π*(NO) electronic transition. This method can be applied in situ for analyses of fish farms water samples, with portable and low cost spectrophotometers, which increases environmental control by rapid analysis. Manuscript submitted: Dec. 12, 2018; revised manuscript submitted: March 13, 2019; manuscript accepted: April 8, 2019; published online: June 11, 2019. REFERENCES 1. Johnstone, R.; Macintosh, D. J.; Wright, R. S. Aquaculture, 1983, 35, pp 249-257. 2. Mlalila, N.; Mahika, C.; Kalombo, L.; Swai, H.; Hilonga, A. Environ. Sci. Pollut., Res. 2015, 22, pp 4922-4931. 3. Green, B. W.; Teichert-Coddington, D. R. J. World Aquacult. Soc., 2000, 31, pp 337-357. 4. Kolok, A. S.; Sellin, M. K. Rev. Environ. Contam. Toxicol., 2008, 195, pp 1-30. 5. Ong, S. K.; Chotisukarn, P.; Limpiyakorn, T. Water, Air, Soil Pollut., 2012, 223, pp 3869-3875. 6. Young, R. B.; Latch, D. E.; Mawhinney, D. B.; Nguyen, T-H; Davis, J. C.; Borch, T. Environ. Sci. Technol., 2013, 47, pp 8416-8424. 7. Rivero-Wendt, C. L. G.; Oliveira, R.; Monteiro, M. S.; Domingues, I.; Soares, A. M. V. M.; Grisolia, C. K. Environ. Toxicol. Pharmacol., 2016, 44, pp 107-113 (DOI: http://dx.doi.org/10.1016/j.etap.2016.04.014). 8. Murray, C. M.; Easter, M.; Merchant, M.; Rheubert, J. L.; Wilson, K. A.; Cooper, A.; Mendonça, M.; Wibbels, T.; Marin, M. S.; Guyer, C. Gen. Comp. Endocrinol., 2016, 236, pp 63-69. 18
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9. Murray, C. M.; Merchant, M.; Easter, M.; Padilla, S.; Garrig, D. B.; Marin, M. S.; Guyer, C. Chemosphere, 2017, 180, pp 125-129. 10. Tölgyesi, A.; Verebey, Z.; Sharma, V. K.; Kovacsics, L.; Fekete, J. Chemosphere, 2010, 78, pp 972-979. 11. Liu, S.; Ying, G.; Zhao, J.; Chen, F.; Yang, B.; Zhou, L-Z.; Lai, H. J. Chromatogr. A, 2011, 1218, pp 1367-1378. 12. Barbosa, I. R.; Lopes, S.; Oliveira, R.; Domingues, I.; Soares, A. M. V. M.; Nogueira, A. J. A. Am. J. Anal. Chem., 2013, 4, pp 207-211. 13. Bianchi; V. N.; Silva, M. R. A. S.; Lamim, M. A.; Silva, C. L.; Lima, E. C. Rev. Ambiente Agua, 2017, 12, pp 380-389. 14. Justino, C. I. L.; Duarte, K. R.; Freitas, A. C.; Panteleitchouk, T. S. L.; Duarte, A. C.; RochaSantos, T. A. P. TrAC, Trends Anal. Chem., 2016, 80, pp 293-310. 15. Zheng, W.; Yoo, K-H.; Choi, J-M.; Park, D-H.; Kim, S.-K.; Kang, Y-S.; El‐Aty, A. M. A.; Hacımüftüoğlu, A.; Wang, J.; Shim, J. H.; Shin, H-C. Biomed. Chromatogr., 2019, 33, p e4396. 16. Xie, Z.; Ouyang, X.; Guo, L.; Lin, X.; Chen, G. Luminescence, 2005, 20, pp 231-235. 17. Miranda, L.; Galli, A.; Quináia, S. P. Rev. Virtual Quim., 2014, 6, pp 416-431. 18. Rosenbaum, E. J. Anal. Chem., 1954, 26, pp 20-26. 19. Klein, S.; James, A. E.; Tuckerman, M. M. J. Am. Pharm. Assoc., 1960, 49, pp 314-316. 20. Feigl, F. Spot tests in organic analysis. Elsevier Pub. Co., Amsterdam, 1966. Chapter 3, p 208. 21. Burke, K.; Werschnik, J.; Gross, E. K. U. J. Chem. Phys., 2005, 123, 062206. 22. Laurent, A. D.; Jacquemin, D. Int. J. Quantum Chem., 2013, 113, pp 2019-2039. 23. Cossi, M.; Barone, V.; Cancès, E.; Mennunci, B.; Tomasi, J. J. Chem. Phys., 1997, 107, pp 3032-3041. 24. Barone, V.; Cossi, M.; Tomasi, J. J. Comp. Chem., 1998, 19, pp 404-417. 25. Tomasi, J.; Mennucci, B.; Cammi, R. Chem. Rev., 2005, 105, pp 2999-3094. 26. Frisch, M. J.; Trucks, G. W.; Schlegel, H. B G.; Scuseria, E.; Robb, M. A.; Cheeseman, J. R.; Montgomery, J. A.; Vreven, T.; Kudin, K. N.; Burant, J. C.; et al. Gaussian 09. Wallingford, CT, Gaussian, Inc, 2009. 27. Devaramani, S.; Thippeswamy, R.; Lawrence, N. S. J. Braz. Chem. Soc., 2015, 26, pp 521-530. 28. Mullaliu, A.; Sougrati, M-T.; Louvain, N.; Aquilanti, G.; Doublet, M-L.; Stievano, L.; Giorgetti, M. Electrochim. Acta, 2017, 257, pp 364-371. 29. Boclair, J. W.; Braterman, P. S.; Brister, B. D.; Yarberry, F. Chem. Mater., 1999, 11, pp 2199-2204. 30. Loach, K. W.; Turney, T. A. J. Inorg. Nucl. Chem., 1961, 18, pp 179-183. 31. Ishikawa, T.; Tanaka, K. Z. Kristallogr., 2008, 223, pp 334-342. 32. Ishikawa, T.; Tanaka, K. J. Chem. Phys., 2005, 122, 074314. 33. Hu, X.; Deng, Y.; Gao, Z.; Liu, B.; Sun, C. Appl. Catal. B, 2012, 127, pp 167-174. 34. Hu, X.; Liu, B.; Deng, Y.; Chen, H.; Luo, S.; Sun, C.; Yang, P.; Yang, S. Appl. Catal. B, 2011, 107, pp 274-283. 35. Savaris, D. L.; de Matos, R.; Lindino, C. A. Rev. Ambient. Água, 2018, 13, pp 1-9. 36. Homklin, S.; Kee-Ong, S.; Limpyakorn, T. Chemosphere, 2011, 82, pp 1401-1407. 37. Falone, S. Z. Development of methods for the determination of 17α-methyltestosterone hormone in water samples and fish culture sediments: ecotoxicological tests with cladocerans. Doctoral Thesis, 2007, School of Engineering of São Carlos, University of São Paulo, São Carlos, SP, Brazil. 38. Adnan, F.; Thanasupsin, S. P. Environm. Eng. Res., 2016, 21, pp 384-392. 19
Article
Br. J. Anal. Chem., 2019, 6 (23) pp 20-28 DOI: 10.30744/brjac.2179-3425.AR.139-2018
Evaluation of Calcium Alginate Microparticles for Copper Preconcentration prior to F AAS Measurements in Fresh Water Marcos Bruno Almeida Colombo, Lucas Eduardo Oliveira Porto, Gabriel Gustinelli Arantes de Carvalho, Denise Freitas Siqueira Petri, Pedro Vitoriano Oliveira Departamento de Química Fundamental, Instituto de Química, Universidade de São Paulo, Av. Prof. Lineu Prestes, 748, 05513-970 São Paulo, SP, Brazil
Graphical Abstract
Cu preconcentration for F AAS - Calcium alginate microparticles were used for the preconcentration of copper for F AAS analysis of fresh waters
A method for separation and preconcentration of Cu2+ from fresh waters prior to flame atomic absorption spectrometry (FAAS) measurements, using calcium alginate (CA) microparticles, is proposed. Off-line analytes preconcentration was achieved using a flow-injection system incorporating a column containing CA (< 180 μm; 300 mg). The preconcentration procedure consisted of 50 mL sample or standard (at pH = 6) loading at 2.0 mL min-1 followed by analyte elution with 3 mL of HCl 1 mol L-1. In these conditions, the enrichment factor was 17 times. The calibration curves were built ranging from 5 to 50 µg L-1 Cu2+. The procedure was applied for the analysis of fresh water sample spiked with 10 µg L-1 Cu2+, resulting in a recovery higher than 94%. Additionally, inter-column reproducibility (RSD < 3,5%; n = 5 columns) and the limit of detection of 0.8 µg L-1 were considered appropriate by taking into the maximum permitted Cu levels by the Brazilian National Environment Council (CONAMA) of 9 µg L-1 (fresh water, Class 1). The proposed approach using CA columns is a reliable and lower cost alternative for single-stage Cu preconcentration prior to F AAS measurements and can be recommended for the analysis of river fresh waters. CA column re-usability was confirmed up to 100 adsorption–desorption cycles. Keywords: Copper, alginate, preconcentration, atomic absorption, water INTRODUCTION Copper is an essential element for the maintenance of biological functions in bacteria, plants, fungi and mammals [1]. Particularly in humans, it is an essential element that is a component of several proteins and metalloenzymes, playing substantial roles on metabolic processes, such as immune system, hemoglobin production, and collagen synthesis [2-4]. The dietary reference intake varies from 1.5 to 4 mg/day and is practically sourced by food and drinking water in most countries [4]. Copper deficiency is characterized by body growth delay, anemia, hair and weight loss, central nervous system and cardiovascular disorders, osteoporosis, among other metabolic dysfunctions [4]. On the other hand, chronic exposure may cause nausea, vomiting, abdominal pain and diarrhea [5]. Acute exposures 20
Evaluation of Calcium Alginate Microparticles for Copper Preconcentration prior to F AAS Measurements in Fresh Water
Article
caused mostly by accidental intakes may cause liver and kidneys failures [5]. Thus, Cu determination in natural and drinking waters is of environmental and public health concern. The Brazilian National Environment Council (CONAMA), establishes the maximum allowed Cu concentration in fresh water (Class 1) as 9 µg L-1 [6]. Copper determination in natural waters is generally performed by instrumental techniques such as inductively coupled plasma mass spectrometry (ICP-MS), inductively coupled plasma optical emission spectrometry (ICP OES), graphite furnace atomic absorption spectrometry (GF AAS) and flame atomic absorption spectrometry (F AAS) [7]. ICP-MS and ICP OES instrumentation provide appropriate limits of detection (LOD) at the ng L-1 and μg L-1 range, respectively. Additionally, both techniques offer attractive features such as simultaneous and multielement capability, and, in the case of ICP-MS, the possibility to obtain useful isotopic information [8]. However, F AAS instrumentation involves a much simpler methodology and can be a reliable alternative for laboratories intended to quality control of fresh water. Besides simplicity, F AAS presents additional features, such as relatively high analytical throughput for single elemental analysis (e.g., about 3-4 samples/min), high tolerance of total dissolved solids (e.g., 5%), and robustness. Notwithstanding, the relatively poor LOD of F AAS, inherent of its characteristic concentration of 0.03 mg L-1 at 324.8 nm absorption line [7], impairs reliable measurements at low Cu concentrations (e.g., < 0.1 mg L-1). Therefore, precise and accurate Cu measurements by F AAS at the aforementioned concentration levels can only be achieved after preconcentration procedures, which can be carried out by using different approaches, such as solid phase extraction (SPE), cloud-point extraction [9], ionic liquids [10,11], dispersive liquid-liquid microextraction [12,13], among others. These strategies not only improve analyte detectability, but also are relevant for removing matrix components that may induce inaccurate measurements [14]. Several biosorbents have been used as a substrate for dissolved Cu separation and preconcentration, such as banana peel [15,16], corn silk [17], olive pomace [18], food waste biomass [19], peat [20], pinion shell [21], soybean hull [22],among others. They have been considered reliable and environmental-friendly alternatives to the aforementioned commercially available synthetic sorbents [23]. These materials are generally framed from biopolymers (e.g., cellulose, alginate, xanthan and chitosan) containing active binding sites, such as amine, amide, carboxylate, hydroxyl, thiol, that can interact with metallic ions and organic molecules. Alginic acid, a biopolymer extracted from brown seaweeds comprising of α-L-guluronic acid (G) and β-D-mannuronic acid (M) groups has been frequently used for the separation of metallic cations (e.g., Ni [24], Zn [24], Pb [24,25], Cu [26,27]) from aqueous media via a cation-exchange reaction. This substrate has been also used aiming towards the adsorption of Cr(VI) ions from aqueous solution after chemical modification [28]. Calcium alginate (CA) beads were used in batch mode for Cu preconcentration from fresh waters prior to F AAS measurements [29]. At the same fashion, CA beads were used for rare earth elements (REEs) preconcentration from fresh waters prior to their determination by ICP OES [30]. Most recently, on-line analyte preconcentration was achieved using a flow-injection system incorporating a column filled with CA microparticles (< 180 µm diameter) prior to 14 REEs determination in fresh waters by ICP-MS [14]. This strategy provided significant advantages in terms of analytical performance when compared to CA beads (in the mm diameter range) in the batch mode, since it provides faster sample loading and less possibilities of losses and contamination. CA microparticles present relatively high adsorption capacity and desorption rate, good stability for repeated use and relatively low cost (i.e., <US$ 0.50 g-1) [14]. Notwithstanding, as well as for commercially-available iminodiacetate- and sulfonate-based resins, CA microparticles present non-selective adsorptive properties, which provides a wider range of potential elements for preconcentration. This work evaluates the potentialities of CA microparticles as a substrate for a single-stage separation and preconcentration of Cu dissolved in fresh water for determination by F AAS. A set of separation columns containing 300 mg of CA microparticles was assembled, and analyte adsorption and desorption were optimized aiming towards the highest enrichment factor. The analytical capabilities and limitations of the proposed procedure were discussed by considering the main analytical figures of merit. 21
Article
Colombo, M. B. A.; Porto, L. E. O.; de Carvalho, G. G. A.; Petri, D. F. S.; Oliveira, P. O.
MATERIALS AND METHODS Reagents and solutions Alginic acid solution was prepared by dissolving alginic acid sodium salt (Sigma 180947, M-to-G ratio = 1.56, Mw from 120000 to 190000 g mol-1) in deionized water (DIW) under constant stirring for 12 h. CaCl2 was sourced by Merck (Darmstadt, Germany). High-purity DIW (resistivity > 18.2 MΩ cm) was obtained from a Milli-Q® water purification system (Millipore, Belford, USA). High-purity HCl and HNO3 were purchased from Merck (Darmstadt, Germany). Tritisol 1000 mg L-1 Cu standard solution was sourced from Merck (Darmstadt, Germany). All the glassware was soaked in HNO3 10% (v/v) solution overnight and rinsed with DIW prior to use. Calcium alginate column Spherical CA hydrated beads (ca. 3 mm diameter) were synthesized by adding a 1.0% (w/v) alginic acid solution drop wise to a 4.0% (w/v) CaCl2 solution under constant stirring, as recommended elsewhere [31]. Thereafter, CA microparticles (from 10 to 180 µm) were obtained after cryogenic grinding for 10 min dried CA beads (ca. 1 mm diameter) [14]. For column separation, 300 mg CA microparticles was dispersed in 2 mL of DIW and transferred into a polypropylene column (0.8 cm internal diameter; Eichrom Technologies Inc., Darien, IL, USA) with a porous polyethylene frit at the bottom, resulting in a ca. 2 cm bed height. A second frit was placed on the top of the CA microparticles bed to avoid particle re-suspension during solution loading. The column was coupled to a flow injection system composed of a peristaltic pump (Ismatec, Switzerland), Tygon® and PTFE tubing, and silicone sleeves (Figure 1).
Figure 1. Column separation manifold.
Cu adsorption Tap water samples were collected at the Institute of Chemistry of the University of São Paulo (São Paulo, Brazil). Laboratory samples were collected with Teflon bottles, filtered through 0.45 mm pore diameter cellulose acetate membrane (Millipore, Belford, USA), acidified with HNO3 to pH 1.60, and stored in Teflon bottles before preconcentration. Parameters affecting Cu preconcentration such as pH (from 0 to 8), sample loading flow rate (from 0.5 to 2 mL min-1), eluting HCl concentration (from 0.01 to 1 mol L-1) and corresponding volume (from 1 to 5 mL) were monoparametrically evaluated aiming towards the highest analyte enrichment factor. Adsorption efficiency (Q), adopted as the percentage of removed analytes from test samples, was calculated through the Equation 1:
22
Equation 1
Evaluation of Calcium Alginate Microparticles for Copper Preconcentration prior to F AAS Measurements in Fresh Water
Article
where C0 is the initial concentration of analytes in the test samples and Ci is the concentration of analytes in remaining sample matrix solution after passing through the column. FAAS instrumentation An AAS Vario 6 Flame Atomic Absorption Spectrometer (Analytik Jena, Jena, Germany) equipped with copper hollow-cathode lamp, as radiation source for absorbance measurements at 324.8 nm, operating at 4 mA, deuterium background correction lamp and integrated autosampler (Analytik Jena AS51) were used. Operating parameters were adjusted according to the manufacturer’s recommendations, and consisted of 0.8 nm slit width, air flow rate 361 L h-1, C2H2 flow rate 50 L h-1. Limit of detection (LOD) of the proposed procedure was calculated through the Equation 2:
Equation 2
where σ is the standard deviation of the blank and α is the slope of the calibration curve. Characterization of calcium alginate substrate The adsorption isotherms of Cu onto CA microparticles were obtained at (24 ± 1) ºC and pH 6.0, in the concentration range of 1.0 to 200 mg L-1 in order to determine the adsorption capacity. The mass of CA microparticles and the Cu solution volume were kept constant as 10 mg and 1 mL, respectively. After reaching equilibrium conditions, the supernatants were separated from the substrate and further analyzed by F AAS. The concentration of adsorbed Cu onto the particles was determined as the difference between the initial concentration (C0) and its concentration in the supernatants, or the equilibrium concentration (Ce). The adsorption capacity of Cu was calculated dividing the concentration of adsorbed Cu by the mass of CA microparticles (m) and multiplying by the solution volume (v) (Equation 3):
Equation 3
where qe(mg g-1) is the adsorption capacity of Cu. The Langmuir, Freundlich and Dubinin-Radushkevitch (D-R) models [32] were applied to evaluate the adsorption process. About 10 mg of CA microparticles and 10 mL of 2 g L-1 Cu2+ solution at pH = 6 were shaken overnight in order to saturate the substrate before scanning electron microscopy (SEM) imaging. The morphology of CA microparticles was analyzed by using a model FEG-SEM JEOL JSM 7401 equipment. Dried CA microparticles were coated with a thin (∼2 nm) gold layer prior to the analyses. RESULTS AND DISCUSSION Analyte adsorption Adsorption pH is a key variable for obtaining quantitative analyte preconcentration, since the cationexchange reactions between carboxylates of the alginate matrix and Cu ions are dependent on the availabilities of H+, Ca2+ (from the substrate), Cu2+, and –COO- groups. The influence of pH (from 0 to 8) on Cu adsorption was evaluated, in batch mode, using a 10 mL of 1.0 mg L-1 Cu2+ standard solution and 100 mg of dried beads (ca., 1 mm diameter). The system was shaken overnight to ensure equilibrium, and the Cu concentration in the supernatant was further determined by F AAS. No Cu adsorption (i.e., Q = 0) was observed at pH = 0. A substantial increase in analyte adsorption efficiency was achieved when the solution pH was increased from 1 to 2, reaching a plateau of ca. 100% Q at pH values ranging from 4 to 8. This relatively wide adsorption pH range 23
Colombo, M. B. A.; Porto, L. E. O.; de Carvalho, G. G. A.; Petri, D. F. S.; Oliveira, P. O.
Article
(i.e., from 4 to 8) indicates there is no need to buffer test samples before sample loading, minimizing the possibilities of contamination. The influence of pH on Cu adsorption onto CA beads was in close agreement with previous findings, where in maximum Cu adsorption was attained at pH = 5 [26,29]. As expected, CA beads and microparticles presented the same adsorption behavior in terms of pH; therefore, further experiments were performed with CA column (containing 300 mg CA microparticles) conditioned with DIW at pH = 6. The influence of sample loading flow rate (from 0.5 to 2 mL min-1) was evaluated with a 1 mg L-1 Cu standard solution at pH = 6. Quantitative analyte adsorption on the CA column was obtained when samples were loaded at flow rates of up to 2 mL min-1, as the concentrations (Ci) of Cu in the remaining sample matrix solution, after passing through the column, were below the F AAS LOD (i.e., 50 Âľg L-1). After defining the best adsorption conditions, isotherm experiments were carried out for determining the adsorption capacity of Cu by the CA microparticles. Parameters predicted from (a) nonlinear fittings for Langmuir and Freundlich models and linear fitting for Dubinin-Radushkevitch are presented in Table I. No plateau was observed in the Cu isotherm, indicating that the CA substrate was no longer saturated. The experimental data fitted the Langmuir and Freundlich models (Figure 2a). The qmax value of 6.49 g g-1 predicted by Langmuir model was 3 orders of magnitude higher than that reported by Singh et al., wherein a qmax value of 5 mg g-1 was obtained for Cu2+ adsorption onto calcium alginate beads [26]. Table I. Equilibrium adsorption of Cu onto CA microparticles. Parameters predicted from (a) nonlinear fittings for Langmuir and Freundlich models, and (b) linear fitting for Dubinin-Radushkevitch corresponding to each adsorption model Langmuir
Freundlich
Dubinin-Radushkevitch
qmax= 6.49 g g-1
n = 1.2359
qmax=1.43 g g-1
KL = 2.56 x 10-4 L mg-1 r2 = 0.9704
KF = 1.0288
Ea= 4.45 kJ mol-1
r2 = 0.9813
r2 = 0.9691
Figure 2. Adsorption isotherm of Cu solution on CA microparticles along with (a) nonlinear fittings for Langmuir and Freundlich models, and (b) linear fitting for Dubinin-Radushkevitch (D-R).
Additionally, the experimental adsorption data fitted well the D-R model (Figure 2b), which is described by the linearized form (Equation 4) in which appropriate correlation coefficient of 0.9691 was obtained: 24
Evaluation of Calcium Alginate Microparticles for Copper Preconcentration prior to F AAS Measurements in Fresh Water
Article
Equation 4
where ɛ = RT ln[1+1/Ce] (J mol-1) is the Polanyi potential, R is the gas constant (8.314 J mol-1 K-1) and T is temperature. The plot of ln qe as a function of ε² yields qmax (intercept) and β (slope). β value is related to the mean free energy of adsorption (Ea, kJ mol-1), Equation 5:
Equation 5
The values of the D-R fitting parameters qmax and Ea amounted to 1.43 g g-1 and 4.45 kJ mol-1, respectively. The magnitude of the adsorption energy indicated a strong affinity, as expected for electrostatic interaction among the negatively charged CA microparticles surface and Cu2+ cations [14]. Although the adsorption capacity predicted by D-R model be smaller than that from Langmuir, at the diluted concentration range (i.e., from 1 to 200 mg L-1), both values are considered appropriate and demonstrate the high affinity of Cu2+ ions to the CA substrate. The adsorption of Cu on CA microparticles led to substantial changes in the morphological characteristics of the particles’ surface. SEM images revealed the smooth surface of CA microparticles (Figure 3a), which turned rough after Cu adsorption (Figure 3b). Additionally, Figure 3b suggests that smaller particles were clustered into larger ones, probably mediated by Cu2+ ions.
Figure 3. Scanning electron microscopic images of CA microparticles (a) before and (b) after Cu adsorption. Experimental conditions: 10 mg CA substrate + 10 mL of 2 g L-1 Cu2+ at pH = 6.
Analyte desorption Preliminary experiments demonstrated that HCl and HNO3 presented similar desorption behavior, and HCl was selected because chlorine ions induce Cu complexation enhancing desorption. The influence of eluting HCl concentration (from 0.01 to 1.0 mol L-1) and corresponding volume (from 1 to 5 mL) were evaluated after loading 10 mL of 1 mg L-1 Cu2+ standard solution at pH = 6 onto CA microparticles column. Quantitative analyte desorption was obtained when 3 mL of HCl 1.0 mol L-1 was used, at 0.5 mL min-1. By loading the column with a 50 mL test portion, the procedure provided an enrichment factor 17-fold. The optimized condition for Cu preconcentration is summarized in Table II.
25
Colombo, M. B. A.; Porto, L. E. O.; de Carvalho, G. G. A.; Petri, D. F. S.; Oliveira, P. O.
Article
Table II. Procedure for CA microparticles column conditioning Step
Procedure
Conditioning
15 mL of DIW pH = 6
Sample loading
50 mL of sample or copper standard solution (pH =6)
Analyte elution Rinsing
3 mL of HCl 1 mol L-1
10 mL of DIW + 2 mL of HCl 1 mol L-1
Analytical features The inter-column reproducibility was evaluated by using 5 independent columns containing 300 mg dried CA microparticles. After loading 50 mL of 10 µg L-1 Cu standard solutions in each column, loading at 2.0 mL min-1, the adsorbed Cu2+ ions were eluted with 3 mL of 1.0 mol L-1 HCl solution, and the obtained enriched solutions were analyzed by F AAS. Under these conditions, coefficient of variation of Cu determinations (i.e., inter-column reproducibility) was 3.4% (n = 5), and was fit for the intended purpose. These findings demonstrate that sample throughput can be significantly increased by assembling a set of analytical CA columns, allowing the processing of several calibrating and test samples simultaneously. Figure 4 presents a calibration curve of Cu obtained by F AAS in the concentration range from 5 to 50 ng g-1. The high correlation coefficient (r² = 0.9999) demonstrates the reliability and robustness of the proposed preconcentration procedure, since each calibration point was obtained with one independent column.
Figure 4. Calibration curve for Cu2+ obtained with the proposed procedure. Calibration points were obtained simultaneously using 5 different columns. Error bars correspond to ± the standard deviation (n = 3 measurements).
Spike and Recovery The proposed preconcentration procedure, which is described in Table II, was applied to the analysis of tap water test sample, which was collected at the Institute of Chemistry of the University of São Paulo (São Paulo, Brazil). The measured Cu concentration in the tap water was 1.04 ± 0.10 µg L-1, which 26
Evaluation of Calcium Alginate Microparticles for Copper Preconcentration prior to F AAS Measurements in Fresh Water
Article
was close to the LOD of the method (i.e., 0.8 µg L-1). That was the reason for such high measurement uncertainty. Recovery from the analysis of a 50 mL tap water spiked with 10 µg L-1 Cu standard solution was 94 ± 4.7% (n = 3), and was considered appropriate for the intended purpose (i.e., acceptable range: between 90 and 110%). In spite of the presence of matrix concomitants (e.g., Na+, K+, Ca2+, Mg2+, NO3-, SO42-, CO32-, Cl-), the high affinity between Cu2+ and CA beads provided reasonable recoveries. Additionally, the LOD of 0.8 µg L-1 was below to the appropriate by taking into account the maximum permitted Cu levels in fresh water by the Brazilian National Environment Council (CONAMA) of 9 µg L-1 [6]. The CA column could be properly regenerated after rinsing with 10 mL of DIW + 2 mL of HCl 1 mol L-1 and conditioning with 15 mL of DIW at pH = 6. This procedure allowed the re-utilization of the CA column for up to 100 times (i.e., adsorption-desorption cycles) procedures. For comparison, Table III presents a comparison of the analytical features of the proposed method and selected preconcentration methods using different biosorbents already published in the literature, that are aimed at the determination of Cu in fresh waters by F AAS. Most analytical features such as enrichment factor, LOD and measurement precision are in the same magnitude as those reported in the literature (especially peat and soybean hull), and they are considered appropriated for the intended purpose. Additionally, the reusability of the CA substrate is a remarkable advantage, as it can be used for more than 100 cycles without any loss of performance. Table III. Analytical features of selected preconcentration methods for the determination of Cu in fresh waters by FAAS. EF = enrichment factors, LOD = limit of detection, RSD = percentage relative standard deviation; Rec = recovery based on the analysis of spiked test samples, n.i. = not informed. Material
EF
LOD (μg L-1)
RSD (%)
Rec (%)
Re-use
Reference
Banana peel
20
n.i.
n.i.
100
11
[15]
Corn silk
39
0.35
1.6
99
50
[17]
Olive pomace
30
0.042
0.75-10
87
n.i.
[18]
Peat
16
3
3.3
100
>100
[20]
Soybean hull
18
0.8
n.i.
99
30
[22]
Calcium alginate
n.i.
n.i.
4.0-8.0
91
n.i.
[29]
Calcium alginate
17
0.8
3.5
94
> 100
Present work
CONCLUSIONS CA microparticles is a reliable substrate for Cu preconcentration from freshwater. The optimized preconcentration procedure could be successfully applied to the analysis of tap water, in which the analytical features such as reproducibility, accuracy and LOD were considered appropriate for the intended purpose. The CA substrate is a reliable and environmentally-friendly alternative to commercially available resins and can be recommended for Cu preconcentration aiming at F AAS measurements. It provides a high adsorption capacity and good stability for repeated use. Acknowledgments The authors gratefully acknowledge financial support from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP; Grants 2015/06161-1 and 2017/22599-2) and from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq; Grants 306848/2017-1 and 308930/2017-7). Manuscript submitted: Dec. 23, 2018; revised manuscript submitted: March 25, 2019; manuscript accepted: April 11, 2019; published online: July 15, 2019. 27
Article
Colombo, M. B. A.; Porto, L. E. O.; de Carvalho, G. G. A.; Petri, D. F. S.; Oliveira, P. O.
REFERENCES 1. Festa, R. A.; Thiele, D. J. Curr. Biol., 2011, 21, pp R877-R883. 2. Djoko, K. Y.; Ong, C. L.; Walker, M. J.; McEwan, A. G. J. Biol. Chem., 2015, 290, pp 1854-1861. 3. Kim, B.-E.; Nevitt, T.; Thiele, D. J. Nat. Chem. Biol., 2008, 4, pp 176-185. 4. Kabata-Pendias, A.; Mukherjee, A. B. Trace Elements from Soil to Human, Springer-Verlag, Berlin, 2007. 5. Council, N. R. Committee on Copper in Drinking Water, The National Academies Press, Washington, DC, 2000. 6. CONAMA, Resolution 430, May 13, 2011, Provisions the conditions and standards of effluents and complements and changes Resolution 357 from March 17, 2005 issued by the National Environment Council (CONAMA). Published in Official Gazette 92 on May 16, 2011. 7. Welz, B.; Sperling, M. Atomic Absorption Spectrometry, Wiley-VCH Verlag GmbH, Weinhcini, 3rd edition, 1999. 8. Montaser, A.; Golightly, D. W. Inductively coupled plasmas in analytical atomic spectrometry, VCH Publisher, New York, 1988. 9. Gouda, A. A.; Amin, A. S. Spectrochim. Acta Part A, 2014, 120, pp 88-96. 10. Zare-Shahabadi, V.; Asaadi, P.; Abbasitabar, F.; Shirmardi, A. J. Braz. Chem. Soc., 2017, 28, pp 887-894. 11. Khayatian, G.; Hassanpour, M. Anal. Bioanal. Chem. Res., 2018, 5, pp 11-21. 12. Shrivas, K.; Jaiswal, N. K. Food Chem., 2013, 141, pp 2263-2268. 13. Kanberoglu, G. S.; Yilmaz, E.; Soylak, M. J. Iranian Chem. Soc., 2018, 15, pp 2307-2314. 14. Arantes de Carvalho, G. G.; Petri, D. F. S.; Oliveira, P. V. Anal. Meth., 2018, 10, pp 4242-4250. 15. Castro, R. S. D.; Caetano, L.; Ferreira, G.; Padilha, P. M.; Saeki, M. J.; Zara, L. F.; Martines, M. A. U.; Castro, G. R. Ind. Eng. Chem. Res., 2011, 50, pp 3446-3451. 16. Ahmad, Z.; Gao, B.; Mosa, A.; Yu, H.; Yin, X.; Bashir, A.; Ghoveisi, H.; Wang, S. J. Clean. Prod., 2018, 180, pp 437-449. 17. Zhu, X.; Yu, H.; Jia, H.; Wu, Q.; Liu, J.; Li, X. Anal. Meth., 2013, 5, pp 4460-4466. 18. El-Sheikh, A. H.; Sweileh, J. A.; Saleh, M. I. J. Hazard. Mater., 2009, 169, pp 58-64. 19. Ince, M.; Ince, O. K.; Asan, E.; Onal, A. At. Spectrosc., 2017, 38, pp 142-148. 20. Gonzáles, A. P. S.; Firmino, M. A.; Nomura, C. S.; Rocha, F. R. P.; Oliveira, P. V.; Gaubeur, I. Anal. Chim. Acta, 2009, 636, pp 198-204. 21. Calero, M.; Blázquez, G.; Dionisio-Ruiz, E.; Ronda, A.; Martín-Lara, M. A. Desalin. Water Treat., 2013, 51, pp 2411-2422. 22. Xiang, G.; Zhang, Y.; Jiang, X.; He, L.; Fan, L.; Zhao, W. J. Hazard. Mater., 2010, 179, pp 521525. 23. Teixeira, L. S. G.; Lemos, V. A.; Coelho, L. M.; Rocha, F. R. P. Appl. Spectrosc. Rev., 2016, 51, pp 36-72. 24. Nastaj, J.; Przewłocka, A.; Rajkowska-Myśliwiec, M. Polish J. Chem. Technol., 2016, 18, pp 81-87. 25. Ren, H.; Gao, Z.; Wu, D.; Jiang, J.; Sun, Y.; Luo, C. Carbohyd. Polym., 2016, 137, pp 402-409. 26. Singh, L.; Pavankumar, A. R.; Lakshmanan, R.; Rajarao, G. K. Ecol. Eng., 2012, 38, pp 119-124. 27. Kumar, R.; Kim, S.-J.; Kim, K.-H.; Kurade, M. B.; Lee, S.-h.; Oh, S.-E.; Roh, H.-S.; Jeon, B.-H. Surf. Interf. Anal., 2018, 50, pp 480-487. 28. Arantes de Carvalho, G. G.; Kelmer, G. A. R.; Fardim, P.; Oliveira, P. V.; Petri, D. F. S. Colloids Surf. A: Physicochem. Eng. Aspects, 2018, 558, pp 144-153. 29. Choi, J. M.; Choi, S. D. J. Korean Chem. Soc., 2004, 48, pp 590-598. 30. Arantes de Carvalho, G. G.; Kondaveeti, S.; Petri, D. F. S.; Fioroto, A. M.; Albuquerque, L. G. R.; Oliveira, P. V. Talanta, 2016, 161, pp 707-712. 31. Kondaveeti, S.; Cornejo, D. R.; Petri, D. F. S. Colloids Surf., B, 2016, 138, pp 94-101. 32. Foo, K. Y.; Hameed, B. H. Chem. Eng. J., 2010, 156, pp 2-10.
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Article
Br. J. Anal. Chem., 2019, 6 (23) pp 29-44 DOI: 10.30744/brjac.2179-3425.AR.140-2018
Determination of Macro and Trace Element Levels in Honey from the Lower Amazonian Region, Brazil Gabriela Sousa Dourado1, Victor Valentin Gomes1, Maila Thais Vieira Maia1, Arthur Abinader Vasconcelos1, Kauê Santana da Costa1, Kelson do Carmo Faial2, Bruno Santana Carneiro2, Newton Trindade Vasconcelos Junior2, Paulo Sérgio Taube1 Instituto de Biodiversidade e Florestas, Universidade Federal do Oeste do Pará, Rua Vera Paz s/n, 68005-100, Santarém, PA, Brazil 2 Instituto Evandro Chagas, Campus de Ananindeua, Rod. BR-316 Km 7 S/N, 67000-030, Ananindeua, PA, Brazil
1
Graphical Abstract
Possible sources of macro and micronutients, as well as potentially toxic elements in honey
The aim of this study was to quantify the macro and trace element concentrations in Apis mellifera and Melipona interrupta honey samples from the Lower Amazonian region in order to evaluate if the samples of different geographical origin and/or species could be distinguished by their mineral content. In addition, it was evaluated the presence of potentially toxic metals in honey samples. The metal contents were determined by inductively coupled plasma optical emission spectrometry (ICP OES) and the most abundant metals found in the samples were K, Ca, Mg, and Na. The total metal (K, Ca, Mg, Na, Cd, Co, Ni, Fe, Mn, Cr, Al and Ba) concentration ranged from 127.7 ± 1.4 to 1844.4 ± 45.2 µg g-1 and from 102.7 ± 2.0 to 639.0 ± 15.3 µg g-1 in Apis mellifera and Melipona interrupta honey, respectively. All mineral content levels found in the honey samples were lower than the maximum established by Brazilian and international law (Cd and Cr 0.1 μg g-1, Pb 0.30 μg g-1, Ni 5 μg g-1, Cu 10 μg g-1, Zn 50 μg g-1). Furthermore, Cu, Pb, and Zn were not detected in any of the samples. However, potentially toxic elements, such as Cd, Co, and Ni, were detected in most of the commercial samples and in Apis mellifera honey from beehives that were close to livestock fields and/or soybean areas. Hierarchical cluster analysis (HCA) was used to study the mineral contents and it was possible to distinguish eight different groups of honey. However, the Melipona interrupta honey could not be separated into different groups. Keywords: Trace elements, mineral content, toxic metals, hierarchical component analysis
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Article
Determination of Macro and Trace Element Levels in Honey from the Lower Amazonian Region, Brazil
INTRODUCTION The mineral composition of honey consists of approximately 27 elements, and several studies have reported that it is possible to separate honey from different floral and geographical origins using the presence of specific mineral groups [1-3]. These metals are classified into macro elements, such as: K, P, and Ca, and trace elements, such as: Pb, Cd, Zn, Fe, Mg, Mn, Al, Ba, Ag, Cr, As, etc., which are not considered to be essential components of honey [4]. The trace element content in honey varies, but it is typically below 1.0 µg g-1 and is normally associated with its color. For example, dark honey has the highest metal contents [5-6]. Brazil is one of the largest producers of honey (FAO 2014) [7] and its production is widely distributed across all regions of the country, which has led to a large variety of honey types [8]. The Lower Amazonian region is a promising area for beekeeping due to its special flora and climate characteristics, although it is considered to be a secondary activity [8]. However, this region has been suffering from agricultural expansion since the 1970s, and has mainly occurred along the BR 163 highway [9]. Several potentially toxic elements bioaccumulate in soils and could affect plant and, consequently, honey quality. Therefore, it is important to evaluate the mineral composition of honey, especially because some minerals are essential for human health. It is also important because some trace elements, such as Cd, Hg, and Pb, are non-essential but toxic in very small concentrations and some, such as Cr, Fe, Mn, Ni, Se, and Zn are essential at certain content levels, but can bioaccumulate to toxic levels (plantanimal-human) under certain circumstances [5]. It has been suggested that honey could be used as bio-indicator of environmental pollution if it is produced from the nectar of different plants, particularly if the nectar bioaccumulates heavy metals [5]. However, honey could also be contaminated with different minerals during processing and storage (e.g., contaminated equipment and tools, and poor hygiene) [6,10-11]. Some authors have found large concentrations of heavy metals in honey produced in areas where industrial and/or agricultural activity is high. These types of activity are considered the most important sources of honey contamination [12-13]. The use of multivariate analysis methods such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) have been used for statistical data processing in order to classify honey samples according to their properties in different groups according to their botanical origin, species or geographical origin [4,6,14-18]. The first aim of this study was to determine the metal contents (macro and trace elements) in Apis mellifera and Melipona interrupta honeys from the Lower Amazonian region. Secondly, to evaluate whether it is possible to differentiate honey by its geographical origin or bee species through the determination of its mineral content. In addition, the degree of contamination of these samples by potentially toxic metals was evaluated. MATERIALS AND METHODS Honey samples A total of 46 honey samples from the Lower Amazonian region were collected between November 2015 and July 2017. Sixteen samples were obtained from commercial sources and 30 samples were obtained through manual extraction where no metal instruments were used and without the use of smoke or chemicals. These samples were collected from two bee species. A total of 33 samples came from Apis mellifera (M01–M33) and 13 samples came from Jandaíra (Melipona interrupta) (J01–J13). Thirty five samples (M01–M20, M30–M33, and J1–J11), four samples (M26–M29), and seven samples (M21– M25 and J12–J13) were obtained from Santarém (02°26′34″S and 54°42′28″W), Belterra (02°38′09″S and 54°56′13″W) and Itaituba (04º16′33″S and 55°59′02″W), respectively, located in the state of Pará, Brazil (Figure 1 and Table I). All honey samples were stored in Falcon tubes, protected from light, and kept at room temperature until needed for further analysis.
30
Dourado, G. S.; Gomes, V. V.; Maia, M. T. V.; Vasconcelos, A. A.; da Costa, K. S.; Faial, K. C.; Carneiro, B. S.; Vasconcelos Junior, N. T.; Taube, P. S.
Article
Figure 1. Geographic location of the beehives where the honey samples were collected.
31
Determination of Macro and Trace Element Levels in Honey from the Lower Amazonian Region, Brazil
Article
Table I. Main characteristics of the different beehive areas
32
ID sample
Description of the beehive areas
M01
Soybean field ± 300 m, highway ± 500 m, obtained from the beestock
M02
Located in a secondary forest, Livestock area ± 500, highway ± 300 m, obtained from the beestock
M03
Soybean field and livestock area ± 500 m, highway ± 2000 m, obtained from the beestock
M04
Located in secondary forest, passion fruit plantation ± 300 m, highway ± 1500 m, obtained from the beestock
M05
Located in secondary forest, highway ± 2000 m, obtained from the beestock
M06
Soybean field ± 500 m, highway ± 1000 m, obtained from the beestock
M07
Livestock area ± 500 m, highway ± 3000 m, obtained from the beestock
M08
Vegetable garden ±1000 m, livestock area ± 1500 m, highway ± 3000 m, obtained from the beestock
M09
Soybean field ± 500 m, highway ± 1000 m, obtained from the beestock
M10
Located in secondary forest, livestock area ± 500 m, highway ± 300 m, obtained from the beestock
M11
Soybean field and livestock area ± 500 m, highway ± 2000 m, obtained from the beestock
M12
Located in secondary forest, passion fruit plantation ± 500 m, highway ± 1500 m, obtained from the beestock
M13
Located in secondary forest, highway ± 2000 m, obtained from the beestock
M14
Soybean field ± 1000 m, highway ± 1500 m, obtained from the beestock
M15
Livestock area ± 500 m, highway ± 300 m, obtained from the beestock
M16
Vegetable garden ±1000 m, livestock area ± 1500 m, highway ± 3000 m, obtained from the beestock
M17
Commercial sample
M18
Commercial sample
M19
Commercial sample
M20
Commercial sample
M21
Located in secondary forest, freshly harvested honey
M22
Located in secondary forest, freshly harvested honey
M23
Acerola (Malpighia emarginata), cupuaçu (Theobroma grandiflorum), papay (Carica papaya), orange (Citrus aurantium), and lemon (Citrus limonum) trees.
M24
Acerola (Malpighia emarginata), cupuaçu (Theobroma grandiflorum), papay (Carica papaya), orange (Citrus aurantium), and lemon (Citrus limonum) trees.
M25
Acerola (Malpighia emarginata), cupuaçu (Theobroma grandiflorum), papay (Carica papaya), orange (Citrus aurantium), and lemon (Citrus limonum) trees.
M26
Mango (Mangifera indica L.), orange (Citrus aurantium), Brazil nut (Bertholletia excelsa), cashew (Anacardium occidentale) and açai (Euterpe oleracea) trees.
M27
Mango (Mangifera indica L.), orange (Citrus aurantium), Brazil nut (Bertholletia excelsa), cashew (Anacardium occidentale), and açai (Euterpe oleracea) trees.
M28
Mango (Mangifera indica L.), orange (Citrus aurantium), Brazil nut (Bertholletia excelsa), cashew (Anacardium occidentale), and açai (Euterpe oleracea) trees.
M29
Mango (Mangifera indica L.), orange (Citrus aurantium), Brazil nut (Bertholletia excelsa), cashew (Anacardium occidentale), and açai (Euterpe oleracea) trees.
M30
Commercial sample
M31
Commercial sample
M32
Commercial sample
M33
Commercial sample
Dourado, G. S.; Gomes, V. V.; Maia, M. T. V.; Vasconcelos, A. A.; da Costa, K. S.; Faial, K. C.; Carneiro, B. S.; Vasconcelos Junior, N. T.; Taube, P. S.
Article
Table I. Main characteristics of the different beehive areas (Cont.) ID sample
Description of the beehive areas
J01
Located in secondary forest, soybean field ± 1000 m, highway ± 1000 m
J02
Located in secondary forest, soybean field ± 1000 m, highway ± 1000 m
J03
Located in secondary forest, soybean field ± 1000 m, passion fruit plantation ± 300 m, highway ± 1500 m
J04
Soybean field ± 1000 m, highway ± 1000 m
J05
Located in a secondary forest, soybean field ± 1000 m, highway ± 1000 m
J06
Commercial sample
J07
Commercial sample
J08
Commercial sample
J09
Commercial sample
J10
Commercial sample
J11
Commercial sample
J12
Commercial sample
J13
Commercial sample
M: Apis mellifera honey; J: Jandaíra (Melipona interrupta) honey.
Sample preparation Microwave assisted digestion Approximately 1.0 g of the sample was weighed in polytetrafluoroethylene (PTFE) tubes and then mixed with 2.0 mL of HNO3, 14.0 mol L-1 (Vetec, Rio de Janeiro, RJ, Brazil) that had been previously distillated in a quartz distillatory (Kurner, Analysentechnik, Rosenheim, Germany); 2.0 mL of H2O2, 30% (v/v) (Vetec, Rio de Janeiro, RJ, Brazil); and 0.5 mL of yttrium solution (100.0 mg L-1) (Sigma Aldrich, St. Louis, MO, USA), which was the internal standard. The mixture was divided into three fractions and then was subjected to a heating program in a closed microwave oven (CEM/Mars Xpress, Matthews, NC, USA), which consisted of the following steps: 1 min at 320 W, 2 min at 120 W, 5 min at 320 W, 5 min at 520 W, and 5 min at 720 W. Then, the resulting mixture was left to cool and was quantitatively transferred to polypropylene vials. The volume of the final solution was made up to 25.0 mL with ultrapure water with a resistivity of 18 MΩ cm. The ultrapure water had been obtained from a Milli-Q system (Millipore, Bedford, MA, USA) before analysis. Reagent blank containing ultrapure water, HNO3, and H2O2 was subjected to the same treatment as the honey samples. The metal contents in the honey were determined by inductively coupled plasma optical emission spectroscopy (ICP OES) model Vista-MPX CCD (Varian, Mulgrave, VIC, Australia) with an automatic sampling system (ASX 520, Cetac Technologies, Omaha, NE, USA). Each sample was analyzed in triplicate. A cyclonic spray chamber and concentric nebulizer were used. The metal determinations were carried out using the manufacturer recommended conditions for power (1.4 kW), generator frequency (40 MHz), plasma gas flow (15 L min-1), auxiliary gas flow (1.5 L min-1), nebulizer gas flow (0.75 L min-1) and sample uptake rate (1.0 mL min-1). The emission intensity scan duration was 60 s. The following analytical wavelengths (nm) were selected: Al (396.152), Ba (455.403), Ca (318.127), Cd (226.502), Co (231.160) Cr (276.716), Cu (327.395), Fe (238.204), K (766.491), Mg (280.270), Mn (257.610), Na (589.592), Ni (231.604), Pb (220.353) and Zn (213.857). The absence of certified honey samples meant that recovery tests were performed to validate the method. The recovery percentage was calculated by processing five honey samples spiked with known amounts of element analytical standards. Calibration of the samples against matrix matches was undertaken so that the macro and trace element concentrations could be calculated and to correct for matrix interferences. One sample of base 33
Determination of Macro and Trace Element Levels in Honey from the Lower Amazonian Region, Brazil
Article
honey from Apis mellifera (M10) honey was selected. Then, standard stock solutions containing 1000 mg L-1 of Zn, Pb, Ni, Cd, Co, Fe, Mn, Cr, Mg, Cu, Al, Ca, Ba, Na, or K were purchased from Merck (Darmstadt, Germany). Analytical calibration standards were prepared daily and the concentrations ranged from 40 to 400 µg L-1 for all analyzed elements. The base honey was prepared in the same manner as the samples. Yttrium was added as the internal standard to a final concentration 10 µg L-1. This sample was digested in a microwave oven and after dilution in the same way as the other samples. The limits of detection (LOD) were calculated using the equation LOD = (3 × RSDBlank × BEC)/100 and LOQ = (10 × RSDBlank × BEC)/100. The limits of quantification (LOQ) were determined as three times the LOD value (3 x LOD). Where BEC (background equivalent concentration) is the blank intensity divided by the angular coefficient of the analytical curve and RSDBlank is the relative standard deviation of the blank (n = 10). The equation for the BEC can also be written as BEC = CElement / SBR, where CElement is the element concentration and SBR (signal-bottom ratio) is the ratio between the intensity of the pure analytical signal and the background signal, which can be calculated SBR = (IAnalytical - IBlank) / IBlank. The LOD and LOQ values are given in Table II. All analyses were run in triplicate and the results were expressed as means ± the standard deviation (SD). A Pearson’s correlation analysis was performed on the mean values for each metal content using Minitab Statistical Software 14 (Minitab, State College, PA, USA). Statistical multivariate analysis by Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were undertaken using the complete linkage with the square Euclidian distance to evaluate if Apis mellifera and Melipona interrupta honey could be distinguished by their mineral contents. The results of the analysis are presented in a dendrogram plot that shows the similarity level between sample results provided by the Minitab Statistical Software 14 (Minitab Inc., 2010) and free software R version 3.1.1 (R Core Team, 2017). RESULTS AND DISCUSSION Macro and trace element concentrations in honey from different areas of Brazil Table II shows the limits of detection and quantification as well as the percentage of recovery of the analyzed elements. The recovery obtained for all elements ranged from 67.0% (Al) to 113.1% (K). A total of 15 metals were simultaneously determined using ICP OES and the average macro and trace element concentrations in the honey samples are given along with their standard deviations in Tables IV and V, respectively. Total metal concentration was obtained from the sum of the mean concentrations of each element, which ranged from 127.7 (M26) to 1844.4 µg g-1 (M23) and from 102.7 (J05) to 639.0 µg g-1 (J07) for Apis mellifera and Melipona interrupta, respectively. The most abundant elements found in the honey samples were K, Na, Ca, and Mg with maximum concentrations of 1618.0 µg g-1 (M23), 200.3 µg g-1 (M32), 167.5 µg g-1 (M32), and 134.2 µg g-1 (M32), respectively (Table III). Table II. Major (Mg, Ca, Na, K) and trace element (Zn, Pb, Cd, Co, Ni, Fe, Mn, Cr, Cu, Al, Ba) levels, and recoveries from spiked honey samples
34
Spike values (µg g-1)
Measured value (µg g-1)
Recovery (%)
0.66
500
391.67 ± 12.89
76.6 ± 2.2
0.36
1.17
500
425.35 ± 15.39
81.6 ± 3.3
Na
0.29
0.96
500
486.54 ± 39.92
95.5 ± 6.6
K
1.59
5.30
500
565.50 ± 34.90
113.1 ± 7.0
Zn
0.23
0.77
500
410.66 ± 16.32
82.1 ± 3.3
Pb
0.21
0.69
500
448.44 ± 17.20
89.7 ± 3.4
Element
LOD (ng g-1)
Mg
0.19
Ca
LOQ (ng g-1)
Dourado, G. S.; Gomes, V. V.; Maia, M. T. V.; Vasconcelos, A. A.; da Costa, K. S.; Faial, K. C.; Carneiro, B. S.; Vasconcelos Junior, N. T.; Taube, P. S.
Article
Table II. Major (Mg, Ca, Na, K) and trace element (Zn, Pb, Cd, Co, Ni, Fe, Mn, Cr, Cu, Al, Ba) levels, and recoveries from spiked honey samples (Cont.) Element
LOD (ng g-1)
LOQ (ng g-1)
Spike values (µg g-1)
Measured value (µg g-1)
Recovery (%)
Cd
1.30
4.34
500
374.67 ± 16.39
74.9 ± 3.3
Co
0.87
2.90
500
372.05 ± 17.50
74.4 ± 3.5
Ni
2.71
9.04
500
372.79 ± 13.98
74.6 ± 2.8
Fe
0.28
0.93
500
378.70 ± 17.24
75.7 ± 3.4
Mn
0.40
1.34
500
374.67 ± 16.39
74.9 ± 3.3
Cr
1.66
5.53
500
391.40 ± 16.34
78.3 ± 3.3
Cu
1.19
3.99
500
385.11 ± 15.70
77.0 ± 3.1
Al
0.28
0.94
500
335.23 ± 13.84
67.0 ± 2.8
Ba
1.13
3.79
500
337.29 ± 12.11
67.5 ± 2.4
LOD: Limits of detection LOQ: Limits of quantification
Table III. Average Mg, Ca, Na, and K concentrations in the honey samples (µg g-1)a ID sample
Mg
Ca
Na
K
M01
21.98 ± 0.44
43.39 ± 0.35
42.18 ± 0.49
364.87 ± 7.13
M02
8.82 ± 0.20
22.32 ± 0.17
28.04 ± 0.17
201.71 ± 5.25
M03
19.55 ± 0.23
41.91 ± 0.56
51.50 ± 0.50
448.17 ± 10.79
M04
27.18 ± 0.30
47.67 ± 0.72
73.40 ± 0.58
592.40 ± 15.36
M05
60.06 ± 0.19
117.63 ± 0.52
82.91 ± 0.86
1418.06 ± 30.12
M06
51.39 ± 0.20
89.22 ± 0.91
90.12 ± 1.00
802.92 ± 6.69
M07
71.14 ± 0.20
131.02 ± 0.49
103.91 ± 1.10
1404.06 ± 42.84
M08
88.68 ± 0.43
113.76 ± 0.52
98.40 ± 0.44
1465.84 ± 27.13
M09
27.85 ± 0.46
51.72 ± 0.41
69.31 ± 0.57
483.35 ± 4.86
M10
12.05 ± 0.27
26.78 ± 0.32
68.21 ± 1.82
234.47 ± 6.41
M11
20.24 ± 0.36
42.91 ± 0.49
81.62 ± 1.34
441.82 ± 8.76
M12
13.74 ± 0.28
23.07 ± 1.07
55.59 ± 0.78
233.42 ± 13.32
M13
36.37 ± 0.19
70.25 ± 0.66
83.02 ± 1.07
761.18 ± 3.04
M14
36.73 ± 0.26
59.46 ± 1.24
89.99 ± 0.96
568.89 ± 5.92
M15
43.66 ± 0.50
81.96 ± 0.35
87.80 ± 0.64
695.98 ± 20.12
M16
45.46 ± 0.41
63.25 ± 0.89
82.94 ± 0.28
737.39 ± 12.80
M17
13.86 ± 0.55
98.41 ± 1.90
137.33 ± 3.43
667.00 ± 2.17
M18
7.99 ± 0.36
17.11 ± 0.44
67.19 ± 2.57
161.83 ± 2.13
M19
15.28 ± 0.75
29.92 ± 1.23
68.26 ± 1.64
333.25 ± 6.18
M20
23.83 ± 0.38
29.21 ± 2.79
62.34 ± 1.12
198.45 ± 2.28
M21
9.26 ± 0.21
36.74 ± 0.46
59.98 ± 0.66
118.86 ± 6.75
35
Determination of Macro and Trace Element Levels in Honey from the Lower Amazonian Region, Brazil
Article
Table III. Average Mg, Ca, Na, and K concentrations in the honey samples (µg g-1)a (Cont.) ID sample
Mg
Ca
Na
K
M22
9.32 ± 0.11
42.63 ± 0.44
72.02 ± 0.53
68.69 ± 4.59
M23
34.91 ± 0.13
103.17 ± 0.62
81.28 ± 0.52
1618.01 ± 43.76
M24
35.62 ± 0.10
74.44 ± 0.54
60.97 ± 0.41
1111.71 ± 28.37
M25
47.44 ± 0.62
84.99 ± 0.22
81.83 ± 0.34
1159.15 ± 23.20
M26
6.36 ± 0.35
32.77 ± 0.10
48.53 ± 0.21
36.41 ± 0.70
M27
15.38 ± 0.79
78.45 ± 0.97
66.59 ± 0.54
113.49 ± 1.12
M28
8.98 ± 0.50
24.10 ± 0.34
60.03 ± 0.20
196.00 ± 0.98
M29
17.62 ± 0.36
29.73 ± 0.76
77.35 ± 0.45
265.23 ± 1.33
M30
20.14 ± 0.91
78.48 ± 1.38
81.25 ± 1.03
145.68 ± 4.49
M31
47.95 ± 0.62
100.76 ± 0.94
110.55 ± 2.21
1264.94 ± 16.74
M32
134.17 ± 0.44
167.50 ± 1.94
200.29 ± 4.34
867.12 ± 20.05
M33
11.89 ± 0.28
19.40 ± 0.23
59.92 ± 0.81
172.56 ± 3.87
J01
2.80 ± 0.11
14.82 ± 0.30
75.60 ± 1.67
40.82 ± 3.56
J02
10.70 ± 0.27
34.44 ± 0.32
103.39 ± 1.90
130.67 ± 7.22
J03
4.72 ± 0.11
15.42 ± 0.58
67.33 ± 0.89
90.02 ± 2.58
J04
3.30 ± 0.07
13.43 ± 0.20
52.40 ± 2.47
52.23 ± 1.35
J05
2.79 ± 0.10
13.78 ± 0.21
53.72 ± 0.96
30.75 ± 0.72
J06
18.41 ± 0.32
79.75 ± 1.62
79.59 ± 5.22
233.35 ± 8.57
J07
34.50 ± 0.28
84.60 ± 4.51
104.13 ± 2.22
412.22 ± 8.22
J08
30.34 ± 0.54
81.59 ± 5.03
78.20 ± 2.69
366.76 ± 6.00
J09
11.93 ± 0.15
64.74 ± 2.11
67.56 ± 2.84
121.43 ± 6.42
J10
16.55 ± 0.79
20.17 ± 0.72
65.93 ± 4.12
109.34 ± 6.00
J11
19.94 ± 0.57
30.32 ± 0.46
60.58 ± 3.50
136.72 ± 9.02
J12
24.10 ± 0.46
56.29 ± 1.26
64.93 ± 1.93
202.14 ± 4.04
J13
12.28 ± 0.28
49.24 ± 0.13
67.56 ± 1.32
266.34 ± 3.39
Average of three determinations ± the standard deviation. M: Apis mellifera honey; J: Jandaíra (Melipona interrupta) honey. a
36
Dourado, G. S.; Gomes, V. V.; Maia, M. T. V.; Vasconcelos, A. A.; da Costa, K. S.; Faial, K. C.; Carneiro, B. S.; Vasconcelos Junior, N. T.; Taube, P. S.
Article
Table IV. Average Cd, Co, Ni, Fe, Mn, Cr, Al, and Ba concentrations in the honey samples (µg g-1) and their standard deviationsa ID
Cd
Co
Ni
Fe
Mn
Cr
Al
Ba
M01
< LOD
0.13 ± 0.04
0.07 ± 0.01
< LOD
1.65 ± 0.07
< LOD
< LOD
< LOD
M02
0.016 ± 0.003
0.26 ± 0.03
< LOD
< LOD
1.52 ± 0.02
< LOD
< LOD
< LOD
M03
< LOD
0.20 ± 0.02
0.20 ± 0.01
< LOD
0.65 ± 0.01
< LOD
< LOD
< LOD
M04
0.006 ± 0.001
0.28 ± 0.02
< LOD
< LOD
1.50 ± 0.04
< LOD
< LOD
< LOD
M05
0.031 ± 0.002
0.17 ± 0.02
0.02 ± 0.00
< LOD
1.87 ± 0.04
< LOD
< LOD
< LOD
M06
< LOD
0.06 ± 0.02
0.03 ± 0.01
0.42 ± 0.02
0.99 ± 0.05
< LOD
0.61 ± 0.01
< LOD
M07
0.018 ± 0.004
0.37 ± 0.02
0.26 ± 0.02
1.18 ± 0.09
2.35 ± 0.09
< LOD
1.84 ± 0.02
0.14 ± 0.01
M08
0.011 ± 0.002
0.20 ± 0.03
0.05 ± 0.01
0.40 ± 0.03
1.33 ± 0.05
< LOD
0.43 ± 0.02
< LOD
M09
< LOD
0.25 ± 0.02
0.02 ± 0.00
< LOD
1.71 ± 0.07
0.03 ± 0.00
< LOD
< LOD
M10
0.031 ± 0.005
0.37 ± 0.02
0.10 ± 0.01
< LOD
1.36 ± 0.03
0.01 ± 0.00
< LOD
< LOD
M11
0.063 ± 0.002
0.21 ± 0.03
0.20 ± 0.01
0.13 ± 0.09
0.50 ± 0.02
< LOD
0.37 ± 0.01
< LOD
M12
0.008 ± 0.002
0.14 ± 0.02
< LOD
< LOD
0.61 ± 0.04
< LOD
< LOD
< LOD
M13
< LOD
0.34 ± 0.08
0.23 ± 0.03
0.12 ± 0.02
1.08 ± 0.04
< LOD
< LOD
< LOD
M14
0.012 ± 0.003
< LOD
< LOD
0.20 ± 0.02
0.66 ± 0.05
< LOD
0.44 ± 0.01
< LOD
M15
0.045 ± 0.005
0.06 ± 0.01
< LOD
0.74 ± 0.07
1.39 ± 0.07
< LOD
0.54 ± 0.01
< LOD
M16
0.057 ± 0.008
0.07 ± 0.02
0.05 ± 0.01
< LOD
0.75 ± 0.02
0.05 ± 0.01
0.13 ± 0.01
< LOD
M17
0.005 ± 0.001
0.13 ± 0.02
< LOD
< LOD
3.48 ± 0.26
< LOD
0.14 ± 0.01
0.41 ± 0.03
M18
0.020 ± 0.003
0.4 ± 0.04
0.11 ± 0.01
< LOD
0.43 ± 0.03
< LOD
1.49 ± 0.05
< LOD
M19
< LOD
0.31 ± 0.03
0.20 ± 0.01
< LOD
0.47 ± 0.01
0.01 ± 0.00
0.14 ± 0.01
0.15 ± 0.008
M20
< LOD
0.10 ± 0.02
< LOD
< LOD
0.21 ± 0.02
< LOD
0.14 ± 0.02
< LOD
M21
< LOD
0.03 ± 0.01
0.22 ± 0.02
3.48 ± 0.10
0.38 ± 0.02
< LOD
1.06 ± 0.05
< LOD
M22
0.002 ± 0.001
0.36 ± 0.02
< LOD
3.28 ± 0.09
0.25 ± 0.01
< LOD
0.97 ± 0.01
< LOD
M23
0.003 ± 0.001
0.02 ± 0.01
0.16 ± 0.02
2.66 ± 0.06
0.61 ± 0.01
< LOD
3.57 ± 0.03
< LOD
M24
< LOD
0.05 ± 0.01
< LOD
2.52 ± 0.05
0.92 ± 0.02
< LOD
4.14 ± 0.02
< LOD
M25
< LOD
< LOD
< LOD
2.04 ± 0.17
1.73 ± 0.04
< LOD
4.52 ± 0.02
0.02 ± 0.01
M26
< LOD
< LOD
< LOD
< LOD
< LOD
< LOD
3.60 ± 0.04
< LOD
M27
< LOD
< LOD
< LOD
< LOD
0.14 ± 0.01
< LOD
2.52 ± 0.02
< LOD
M28
< LOD
< LOD
< LOD
0.46 ± 0.04
0.17 ± 0.01
< LOD
2.10 ± 0.03
< LOD
M29
< LOD
< LOD
< LOD
< LOD
0.32 ± 0.01
< LOD
1.64 ± 0.03
< LOD
M30
0.027 ± 0.003
0.07 ± 0.01
0.10 ± 0.01
56.35 ± 0.13
0.38 ± 0.03
0.01 ± 0.00
< LOD
0.003 ± 0.001
M31
0.021 ± 0.004
0.03 ± 0.01
0.32 ± 0.03
1.00 ± 0.07
3.43 ± 0.14
< LOD
1.26 ± 0.01
< LOD
M32
0.010 ± 0.001
0.08 ± 0.01
0.05 ± 0.00
1.53 ± 0.09
1.77 ± 0.03
< LOD
4.49 ± 0.05
1.26 ± 0.11
M33
0.007 ± 0.002
0.24 ± 0.01
0.04 ± 0.01
< LOD
0.37 ± 0.04
< LOD
0.01 ± 0.00
0.15 ± 0.02
J01
< LOD
< LOD
< LOD
< LOD
< LOD
< LOD
2.19 ± 0.04
< LOD
J02
< LOD
< LOD
< LOD
< LOD
0.23 ± 0.02
< LOD
3.11 ± 0.05
0.09 ± 0.02
J03
< LOD
< LOD
< LOD
< LOD
< LOD
< LOD
1.66 ± 0.03
< LOD
37
Determination of Macro and Trace Element Levels in Honey from the Lower Amazonian Region, Brazil
Article
Table IV. Average Cd, Co, Ni, Fe, Mn, Cr, Al, and Ba concentrations in the honey samples (µg g-1) and their standard deviationsa (Cont.) ID
Cd
Co
Ni
Fe
Mn
Cr
Al
Ba
J04
< LOD
< LOD
< LOD
< LOD
< LOD
< LOD
1.40 ± 0.04
< LOD
J05
< LOD
< LOD
< LOD
< LOD
< LOD
< LOD
1.62 ± 0.02
< LOD
J06
< LOD
< LOD
< LOD
< LOD
0.68 ± 0.05
< LOD
9.13 ± 0.07
< LOD
J07
< LOD
0.10 ± 0.01
< LOD
< LOD
0.59 ± 0.03
< LOD
2.88 ± 0.04
< LOD
J08
< LOD
< LOD
< LOD
< LOD
0.49 ± 0.03
< LOD
2.98 ± 0.03
0.05 ± 0.01
J09
< LOD
< LOD
< LOD
< LOD
0.14 ± 0.03
< LOD
2.63 ± 0.02
< LOD
J10
0.044 ± 0.005
0.02 ± 0.01
0.31 ± 0.02
< LOD
0.13 ± 0.01
0.02 ± 0.00
0.33 ± 0.01
< LOD
J11
0.017 ± 0.002
0.27 ± 0.02
0.21 ± 0.01
0.52 ± 0.06
0.34 ± 0.03
< LOD
0.36 ± 0.01
< LOD
J12
0.015 ± 0.004
0.01 ± 0.00
< LOD
7.59 ± 0.12
0.78 ± 0.02
< LOD
3.00 ± 0.03
< LOD
J13
< LOD
0.27 ± 0.02
< LOD
0.72 ± 0.03
0.57 ± 0.02
< LOD
0.76 ± 0.01
1.38 ± 0.23
Average of three determinations ± standard deviation. ID Sample; M: Apis mellifera honey; J: Jandaíra honey. LOD: limit of detection. a
There were also a few potentially toxic trace elements, such as Cd, Co, Ni, Fe, Mn, and Al, detected in most of the Apis honey samples and some of the Melipona interrupta samples (Table IV). However, there was generally less than 1.00 µg g-1 of these elements present, except for Fe, Mn, and Al in some samples (Table IV). The correlations between these metals ranged from –0.114 to 0.848 for Ca, which had a negative correlation with Cr content (r2 = 1.3%) and a strong positive correlation with Mg content (r2 = 72.0%) (Table V). The correlations between Cr and Na, and K contents whereas 0.749 (r2= 56.1%) and with K was 0.772 (r2= 59.6%), this perhaps shows that the composition of these elements at the sampling honeys, increase when the each one increases, therefore in all samples the calcium content was directly influenced by Na, Mg and K contents representing the major total variation of data. The Mn contents show the major value of correlation between a trace metal (0.605) (r2= 36.6%) (Table V). The Na correlation coefficients ranged from -0.057 to 0.749, being -0.057 with Co contents and 0.749 with Ca contents. The Na had a moderate correlation with Mn (0.488) among the trace metals, as the Ca contents. The others macro elements had the ranges of their correlation coefficients: -0.083 with Fe and 0.772 with Ca (for K contents); and -0.019 with Fe and 0.848 with Ca (for Mg contents). The Co contents showed a weak positive correlation with Ni contents (0.300) (r2= 9.0%), however, it presented moderate negative correlation with Al content (-0.507) (r2= 25.0%) (Table V). All others elements showed low correlations with others elements with the remark for the coefficients between Cr and Cd (0.634) that could represent a risk for the human health. If the continuous monitoring under the same sampling and analysis conditions shows that Cr or Cd continue to be present at high levels, then it may indicate that an increase in Cr leads to an increase in Cd and vice versa. The levels of Cu, Pb and Zn in the honey samples were below the limit of detection. Cadmium was detected in 57.60% and 23.07% of Apis and Melipona honey samples, respectively, with concentrations ranging from 0.02 (M22) to 0.063 (M11) µg g-1. In addition, Ni and Co were detected in more than half of the Apis mellifera honey, with concentrations ranging from 0.025 µg g-1 (M09) to 0.325 µg g-1 (M31) µg g-1, and from 0.016 µg g-1 (M23) to 0.373 µg g-1 (M10), respectively (Table IV).
38
Dourado, G. S.; Gomes, V. V.; Maia, M. T. V.; Vasconcelos, A. A.; da Costa, K. S.; Faial, K. C.; Carneiro, B. S.; Vasconcelos Junior, N. T.; Taube, P. S.
Article
Table V. Pearson’s coefficients for macro and trace elements Al
Ba
Ca
Cd
Co
Cr
Fe
K
Mg
Mn
Ba
0.098
Ca
0.288
0.327
Cd
-0.322
-0.095
0.089
Co
-0.507
0.147
-0.007
0.146
Cr
-0,229
-0.073
-0.114
0.634
0.120
Fe
-0.081
-0.034
0.120
0.149
-0.067
0.010
K
0.056
0.064
0.772
0.162
0.119
-0.020
-0.083
Mg
0.128
0.363
0.848
0.187
0.080
0.007
-0.019
0.738
Mn
-0.124
0.184
0.605
0.164
0.281
-0.041
-0.076
0.645
0.504
Na
0.227
0.513
0.749
0.112
-0.057
-0.014
0.034
0.458
0.734
0.488
Ni
-0.253
-0.081
0.092
0.357
0.300
0.212
0.070
0.244
0.132
0.209
Na
0.072
Multivariate analysis Figure 2 shows the scores and loadings plots of macro and trace elements determined in honey samples. The explained variance of PC1 and PC2 was 51.8%. The contents of Fe, Co and K were similar and had the same weight to separation as well as Na, Cd and Mg and Ca, Ba, Al and Ni. The contents of Mn and Cr were isolated. The content of Cr has a diametrical opposition in relationship to Al, Ba and Ca contents, this may explain the low and negative correlation between these variables (-0,229 (Cr-Al); -0.073 (Cr-Ba); -0.114 (Cr-Ca)). The Apis melifera samples had the major of metal contents responsible to distinguish different geographical location while the Melipona interrupta had the majority of samples with contents of Fe, Co, K, Cd, Mg and Na very different in relationship to Apis mellifera samples. Only two samples from Melipona interrupta had similar contents of Mn in comparison to samples from other bee species. Some samples of Apis mellifera had similar characteristics to the others samples (M26, M27, M28 and M29). The majority of Apis samples had contents of Ni, Al, Ba and Ca similar. Two samples had the same contents of Cd and Mg. Some samples of Apis and Melipona had similar Cr contents (M23, M25, M06 and M14; M24, J07 and J13, respectively). One sample of Apis (M32) was an outlier.
Figure 2. Principal component analysis of the averages of metal concentration in Apis mellifera (■) and Melipona interrupta (●) honey. Left: Scores. Right: Loadings. 39
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Determination of Macro and Trace Element Levels in Honey from the Lower Amazonian Region, Brazil
The final partition of dendrogram was at 98.1% similarity (Figure 1). This meant that eight clusters were formed based on the similarity between the samples. The M01, M19, J07, and J08 grouping contained relatively high metal contents, which were similar to the contents in samples M03, M11, and M09 (Figure 3). The major grouping was at 91.8% similarity (groups II and III, b and c) and this grouping contained 12 sample types. A large number of the “M” samples were around 99.5% similar; and 14 samples among “J” and “M” sample types had 99.1% similarity. These sample types were differentiated from the first group where the similarity between the sample types was only 69.9%. The groups included many samples from the different types of bees and therefore formed a heterogeneous cluster.
Figure 3. Dendrogram of the macro and trace element concentrations in the honey samples. M: Apis mellifera honey and J: Melipona interrupta honey.
Others sample types grouped into three clusters with 95.2% similarity, Cluster IV (d) included M04 and M14, which had similar K contents, and M15 and M17, which had high K content similarities with M04 and M14. Cluster V (e) contained sample types M13, M06, and M16 because they had similar Na, K, and Mn contents (Table III). Cluster VI (f) contained just one sample type (M32). This sample was separated from the others because it had high macro-element contents (Table III). Clusters V and VI had 98.1% similarity. The similarity between these two clusters was lower than between the previous groups and there was no similarity between Clusters V and VI and the other groups. Group VII (g) contained M05, M07, M08, and M23, which all had similar K contents (Table III). The Fe content was higher than in the other clusters, which may explain the reduced similarity between this cluster and the others (Table III). This cluster had 89.8% similarity with group VIII (h), which contained M31, M24, and M25. The sample types had similar trace element compositions, but M31 had different Ca, Na, and K contents. Therefore, these elements were the factor that distinguished M31 from the others samples (Table III). In addition, the “J” samples were not grouped into their own clusters by the analysis. Instead, they were distributed among the “M” samples (Figure 3).
40
Dourado, G. S.; Gomes, V. V.; Maia, M. T. V.; Vasconcelos, A. A.; da Costa, K. S.; Faial, K. C.; Carneiro, B. S.; Vasconcelos Junior, N. T.; Taube, P. S.
Article
Discussion Since there is no certified and appropriate commercial reference material for honey, the recovery rate was estimated by the use of spiked concentrations of all elements investigated. The percentage of recovery of the analyzed elements was high (ranging from 67% to 113% at a spiking concentration of 500 µg g-1), indicating that the method is suitable. It is clear that all the honey samples analyzed in this study had total mineral contents that were similar to those found by Silva et al. (2013) in northwestern Pará, Brazil. Silva et al. (2013) also found lower amounts of these minerals in Melipona fasciculata samples compared to Apis mellifera and other species of meliponae in the state of Pará [4]. In general, the macro element levels in the analyzed samples were similar to those found in Brazilian honey [4,19-20], as well as in Spanish multifloral/monofloral honey [1], Portuguese multifloral and monofloral honey [21] and Malaysian honey [22]. The trace elements levels were in agreement with those found in other studies [4,10]. The strong correlation between Ca and Mg content could be related to plant growth and the close relationship between Ca and Mg uptake. Interesting, the relationship between Ca and Mn content is reported to be a potential fingerprint for honeys from different regions [23]. The presence of heavy metal in honeys is related to industrial pollution levels near to the apiaries, which means that honey could potentially be used as a biological pollution indicator [24]. The negative correlation between Co and Al could be a good potential indicator because when Co increases in the honey Al decreases and vice versa. The presence of these heavy metals may be due to intense agricultural activity close to the nectar collecting regions or to contamination of the groundwater absorbed by trees in the nectar collection area [3]. Cadmium levels were not thought to be high in honey [25]. However, in this study, the positive correlation between these elements was significant and represented 40% of the determination coefficient. Furthermore, Cd levels were similar to those found in Polish honey from an industrialized region [26] and were higher than those found in French [27] and Brazilian honey [2]. Nevertheless, no honey sample analyzed in this study had Cd concentrations that were higher than the maximum recommended (0.1 µg g-1) by international law [28]. Presence of this trace element may indicate anthropogenic activities or the impact of environmental pollution [29-30]. The presence of higher Fe and Al contents in the samples analyzed in this study could be associated with the predominance of latosols and argisols in the study region, which are rich in these metals [31]. Although the Fe, Mn, and Al concentrations were higher than the other trace metals [32]. The absence of Pb, Cu, and Zn in the analyzed samples corroborated the results obtained by Silva et al. (2013) when they analyzed 27 samples from the North region of Brazil [4]. The presence of Cd, Co, Fe, K, Mg, Mn, Pb and Na in higher concentrations may also be associated with substances provided for the nutrition of bees (e.g. industrial syrups) [6,33]. Most of the metals present in honey come from plants that produce soil and nectar. However, the presence of some metals, mainly Cd, Cr, Cu, Fe, Ni, Pb and Zn, may indicate environmental pollution or anthropogenic alteration [6]. All mineral content levels found in the honey samples were lower than the maximum established by Brazilian and international law (Cd and Cr 0.1 μg g-1, Pb 0.30 μg g-1, Ni 5 μg g-1, Cu 10 μg g-1, Zn 50 μg g-1) [28]. The M01, M19, J07, and e J08 grouping may be explained by their similar macro element contents (Na, Ca, Ba, Mg, and K) (Table III). The differences between M03, M11, and M09 may be due their similar macro and trace element contents (Tables III and IV). These sample types may lead to similar contamination results because their trace element contents were almost the same. The high similarity between the 32 samples in Groups I and II may be due to the high homogeneity between the macro and trace metal contents in each sample type. The absence of clusters among the Melipona interrupta samples (Figure 2) may indicate that the individual Melipona interrupta sample types were highly heterogeneous, which suggests that floral nectar composition was highly variable and that there was a relationship between the “J” samples and 41
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Determination of Macro and Trace Element Levels in Honey from the Lower Amazonian Region, Brazil
the composition of the Apis mellifera samples. The trace-elements in a soil affect honey composition and the macro elements are influenced by the floral origins of the nectar [18]. The high similarity between some “J” and “M” samples may indicate that the soil in the area and the floral nectar collected by the different bee species were similar (Figure 2). CONCLUSIONS The results produced by this study showed that the total mineral contents in the analysed honey were similar to those reported by previous studies in the same region and from other Brazilian regions. The most abundant elements in the honey samples were K, Ca, Mg, and Na. The K contents in the Melipona interrupta honey samples were generally lower than in the Apis mellifera honey. In addition, Cu, Pb, and Zn were not detected in any honey sample analysed. Potentially toxic trace elements, such as, Cd, Co and Ni were detected in most of the Apis mellifera honey samples, and they could be associated with the use of agrochemicals in areas near to the beehives and in the Apis mellifera flightpaths. The Pearson’s coefficients showed that Mg, Na, and Mn were highly positively correlated with Ca. In addition, there was a moderate positive correlation between Ni and Cd and a moderate negative correlation between Co and Al. The PCA of the macro and trace elements showed that the mineral content of the samples changes with respect to geographical origin. However, there is no specific separation between honeys of different species. The HCA of the macro and trace elements showed that the honey sample types could be aligned into eight groups. However, the Melipona interrupta honey samples did not form an isolated group, which may indicate a potentially high variation in floral nectar composition or that the honeys produced by these species may have been altered by the addition of Apis mellifera honey. Agricultural activities are increasing in the Lower Amazonian region, which means that more research should be conducted periodically to determine the concentrations of potentially toxic heavy metals and to monitor environmental contamination in this region. Conflict of Interest Statement The authors declare no conflict of interest. Acknowledgements This work was supported by Project 88881.159143/2017-01 funded by CAPES (Coordination of Improvement of Higher Education Personnel) and FAPESPA (Amazonia Foundation for Studies and Research in Pará). The authors also thank the two anonymous reviewers for their constructive comments and additions. Manuscript submitted: Dec. 13, 2018; revised manuscript submitted: June 7, 2019; revised for the 2nd time submitted: July 16, 2019; manuscript accepted: July 19, 2019; published online: Aug. 6, 2019. REFERENCES 1. Hernández, O. M.; Fraga, J. M. G.; Jiménez, A. I.; Jiménez, F.; Arias, J. J. Food Chem., 2005, 93, pp 449–458 (DOI: http://dx.doi.org/10.1016/j.foodchem.2004.10.036). 2. Batista, B. L.; Silva, L. R. S.; Rocha, B. A.; Rodrigues, J. L.; Berretta-Silva, A. A.; Bonates, T. O.; Barbosa, F. Food Res. Int., 2012, 49, pp 209–215 (DOI: http://dx.doi.org/10.1016/j. foodres.2012.07.015). 3. Solayman, M.; Islam, M. A.; Paul, S.; Ali, Y.; Khalil, M. I.; Alam, N.; Gan, S. H. Compr. Rev. Food Sci. Food Saf., 2015, 15, pp 219–233 (DOI: http://dx.doi.org/10.1111/1541-4337.12182). 42
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Technical Note
Br. J. Anal. Chem., 2019, 6 (23) pp 45-50 DOI: 10.30744/brjac.2179-3425.TN.132-2018
Pseudo-Univariate Calibration as an Analytical Tool to Determine Antioxidant Activity: An Alternative to DPPH Method Applied to the Evaluation from Extracts of Turmeric Powder Mirian S. Laczkowski1, Thays R. Gonçalves1, Sandra T. M. Gomes¹, Paulo Henrique Março², Patrícia Valderrama² , Makoto Matsushita1 Universidade Estadual de Maringá — UEM. 87020-900, Maringá, PR, Brazil Universidade Tecnológica Federal do Paraná — UTFPR. P.O. Box 271, 87301-899, Campo Mourão, PR, Brazil 1
2
A pseudo-univariate calibration model was proposed as an alternative method to determine the antioxidants activity (%RSA) in extract from turmeric powder by using the relative intensity from multivariate curve resolution with alternating least squares (MCR-ALS) from near-infrared (NIR) spectra. An extraction was performed by using an ultrasound with probe, ethanol 70% and time 15 minutes. For pseudo-univariate calibration, eight independent samples were prepared from the curcuminoid extracts. NIR spectra were collect from 900 to 1700 nm, and to these samples the percentage of inhibition of DPPH was determined with reference method. The signal for antioxidants was isolated from the signal to the other analytes in the sample by applying MCR-ALS to the NIR spectra. The relative intensity profile related to the antioxidants was employed in a pseudo-univariate calibration against the %RSA obtained by the reference conventional method based on DPPH. The results of the %RSA determination in four independent samples with mean %RSA (obtained by the reference method) of 90.26% show a mean %RSA of 87.5%, showing the possibility of %RSA determination directly from NIR spectra of turmeric powder extracts by using the multivariate resolution method MCR-ALS. Keywords: MCR-ALS; pseudo-univariate calibration; NIR; DPPH; antioxidant activity. INTRODUCTION Analytical chemistry begins to reach a higher degree of maturation after the introduction of chemometrics. At the time it was hoped that chemometrics could change the way in which analytical methods were developed [1]. It is now well known that chemometrics is increasingly present in analytical methodologies, especially those involving spectroscopies. However, many methodologies, especially in food analysis, still deserve an update. An example is an evaluation of antioxidants through the 2,2-Diphenyl-1-picrylhydrazyl (DPPH) method. There is no consensus on the scientific literature about the DPPH method. In each case the method is optimized in some different senses. Recently, in a previous study, it was proposed to evaluate the antioxidant activity by using near-infrared (NIR) spectroscopy [2]. In this sense, the main objective of this work was to perform a pseudo univariate calibration based on NIR spectroscopy coupled with multivariate curve resolution with alternating least squares (MCR-ALS) for determination of the antioxidant activity from extracts of turmeric powder. Some phenolic compounds can form complexes with metals, inhibit lipoxygenase and capture free radicals, such as DPPH [3]. DPPH is known to be characterized as a stable organic free radical, being used in more than 90% in the studies of evaluation of antioxidants in matrices of pure substances, mixtures or complexes mixtures [4-6]. However, this conventional method presents several drawbacks, as the use of significant amounts of reagents, standards, samples, and time. In this way, when coupled 45
Technical Note
Pseudo-univariate calibration as an analytical tool to determine antioxidant activity: An alternative to DPPH method applied to the evaluation from extracts of turmeric powder
with instrumental techniques, chemometric tools of multivariate analysis can be an update to the results interpretation, allowing a more complete and efficient understanding of the analytical results [7,8]. Curve resolution is a chemometric tool that has been employed for pseudo univariate calibration [2,9,10]. This tool promotes the separation of the analyte signals without the need for physical separations [11]. MCR-ALS is a curve resolution chemometric tool that aims to resolve signal mixtures by recovering pure spectra and its relative intensity for the analytes in a sample [11-13]. Then, this chemometric tool can be applied for quantification when that relative intensity profile for the analyte of interest is employed in the construction of a pseudo univariate calibration curve [11]. MATERIAL AND METHODS Fresh turmeric rhizomes were obtained in Maringá, Paraná, Brazil and harvested between August and November 2016/2017. The turmeric rhizomes were washed, sliced (0.50 to 0.70 mm) and submitted to drying in an oven with air circulation at 40 ± 0.5 ºC for 36 hours, this method was adapted from Vieira & Jorge [14]. After drying the rhizomes were ground in a knife mill, being standardized granulometry (120 mesh), obtaining the rhizomes in powder. The extraction from turmeric powder was based on preliminaries studies [15-17], by using 0.1 g of powder rhizomes and 10 mL of ethanol 70% in an Ultrasound (Fisher Scientific – FB120 – 2000, Park Lana – Pittsburg, PA – 15275) with probe (Fisher Scientific – Model E18), 120 W of power and frequency 100% (20 kHz) in room temperature (25 °C), pH 5.5, and time of 15 minutes. The obtained extract was centrifuged for 20 minutes at 4000 rpm. In order to verify the extraction, the curcuminoids concentration (g mL-1) was evaluated. A standard calibration curve was obtained (with ethanol 70%) based on the curcumin standard (65% of curcumin, 35% other curcuminoids, Sigma Aldrich) in the range from 8.10-6 to 8.10-5 g mL-1, by using the absorption at 427 nm (Ocean Optics Spectrophotometer Red Tide, USB650 UV model). The antioxidant activity from the extract was determined by the DPPH method employing the reagent 2,2-Diphenyl-1-picrylhydrazyl (Sigma Aldrich). DPPH solution in ethanol was prepared in the concentration of 28 mg mL-1. This concentration was used in previous studies in order to show absorbance at 515 nm between 0.6 and 0.7 u.a [2,18,19]. The determinations were performed (in triplicates) with 100 µL of the extract and 3900 µL of the DPPH solution. A control sample was prepared with 100 µL of ethanol and 3900 µL of DPPH solution. Each mixture was placed in the dark for 30 minutes. The absorption was measured in a spectrophotometer (Ocean Optics Spectrophotometer Red Tide, USB650 UV model) at 515 nm. The antioxidant activity (%RSA) was calculated as the percentage of inhibition of the DPPH radical through equation 1 [20]. % RSA = [(Acontrol - Asample) / Acontrol] x 100
(1)
where: Acontrol is the absorbance at 515 nm for the control sample and Asample is the absorbance at 515 nm for the samples. For pseudo-univariate calibration, eight independent samples were prepared from the curcuminoid extracts by adding 25, 50, 75, 100 and 150 µL of extract in 2 mL of distilled water. NIR spectra were collect from 900 to 1700 nm (MicroNIR - JDSU) with a glass cuvette. To these samples, the percentage of inhibition of DPPH was determined at the same time, by using the previously cited conventional method. The signal for antioxidants was isolated from the signal to the other analytes in the sample by applying MCR-ALS to the NIR spectra. A graphical user-friendly interface for MCR-ALS [21], for Matlab software (R2007b) was employed. The MCR-ALS provided a spectral profile for each constituent in the sample and its relative intensity profile. The relative intensity profile related to the antioxidants was employed in a pseudo-univariate 46
Laczkowski, M. S.; Gonรงalves, T. R.; Gomes, S. T. M.; Marรงo, P. H.; Valderrama, P.; Matsushita, M.
Technical Note
calibration against the %RSA obtained by the reference conventional method based on DPPH. RESULTS AND DISCUSSION The curcuminoids concentration for the extracts was 0.718 mg L-1. The NIR spectra of the antioxidant extracts from turmeric rhizomes are shown in Figure 1. The spectral baseline was corrected by savgol algorithm [22], through 7 points, first order polynomial and first derivative.
Figure 1. NIR spectra of antioxidant extracts from turmeric powder. (A) Raw spectra. (B) Spectra after baseline correction.
For the spectra after baseline correction, a mathematical rank was estimated from the percentage of variance explained by the Principal Component Analysis (PCA) [9]. The rank equal to four was considered since the first four principal components retain 99.40% variance from this data set. MCR-ALS was applied to resolve the pure spectra and concentration profiles for four different components in antioxidant extracts from turmeric powder. During alternating least squares optimization, no constraints were employed. The initial estimates for ST were achieved by the PCA loadings [23]. Figure 2 shows the concentration profiles and recovered spectra resolved for antioxidant extracts. The relative intensity profile related to the antioxidants (bold blue solid line) shows a %RSA decreasing with the antioxidant concentration increasing. The absorptions for the recovered spectra related to the antioxidants are attributed for the second and third overtones of CH3, CH2, and CH, besides the third overtone absorption of R-OH, Ar-OH and Ar-CH [24].
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Pseudo-univariate calibration as an analytical tool to determine antioxidant activity: An alternative to DPPH method applied to the evaluation from extracts of turmeric powder
Figure 2: MCR-ALS results. (A) Recovered spectra. (B) Relative intensity: (—) antioxidants, (—) interferent 1, (—) interferent 2, (—) interferent 3.
Kasemsumran et al. [25,26], in works from turmeric powder, observed absorptions in the NIR range from 1100 to 1620 nm, mainly due to the R-OH bonds and CH absorptions, similar to that identified in this work. Jovanovic et al. [27], reported that the mechanism of antioxidant activity from curcumin (present in higher concentrations among curcuminoids) is through the donation of hydrogen from the methylene CH subunit at acid pH, which makes these bonds weakened by facilitating the mechanism of donation of H, in addition to the predicted antioxidant activity related to phenolic hydroxyls [28], suggesting that the pH used in this work to extract the curcuminoids helped to obtain the antioxidant activity from them. The relative intensity related to antioxidants were plotted against %RSA result in a pseudo-univariate calibration model, as shown in Figure 3, with correlation coefficient 0.9532. By using this model, the %RSA determination in four independent samples (Table I) with mean %RSA (obtained by the reference method) of 90.26% shows a mean %RSA of 87.5%. Table I. Results obtained from reference method and by pseudounivariate calibration curve for four independent samples
48
%RSA Reference method
%RSA Pseudo-univariate calibration
90.26
87.5
90.25
87.5
91.95
90.0
88.57
85.0
Mean = 90.26
Mean = 87.5
Laczkowski, M. S.; Gonçalves, T. R.; Gomes, S. T. M.; Março, P. H.; Valderrama, P.; Matsushita, M.
Technical Note
Figure 3. Pseudo-univariate calibration curve.
The results obtained in this proposal show the possibility of %RSA determination directly from NIR spectra of turmeric powder extracts by using the multivariate resolution method MCR-ALS. In summary, the steps to perform this method are: 1) extraction of curcuminoids (with the ethanol concentration 70%, extraction time 15 minutes, and with drying oven process); 2) prepare the samples; 3) obtaining the NIR spectra; 4) performer MCR-ALS; 5) prepare the pseudo-univariate calibration curve; 6) test the samples with unknown concentration. CONCLUSION Pseudo-univariate calibration model based on MCR-ALS shows the possibility of antioxidant activity determination in extracts from turmeric powder directly from NIR spectra. The main advantage in this alternative method is that the DPPH reagent is not necessary (when the pseudo-univariate curve is ready), which is important in terms of reagents consuming, and waste generation. Manuscript submitted: Nov. 26, 2018; revised manuscript submitted: Feb. 26, 2019; manuscript accepted: March 25, 2019; published online: June 11, 2019. REFERENCES 1. Senise, P. Quim. Nova, 1983, 6, pp 112-116. 2. Laczkowski, M. S.; Gonçalves, T. R.; Gomes, S. T. M.; Março, P. H.; Valderrama, P.; Matsushita, M. LWT – Food Sci. Technol., 2018, 95, pp 303-307. 3. Decker, E. A. Nutr. Rev., 1997, 55, pp 396-398. 4. Deng, J.; Cheng, W.; Yang, G. Food Chem., 2011, 125, pp 1430-1435. 5. Moon, J. -K.; Shibamoto, T. J. Agric. Food Chem., 2009, 57, pp 1655-1666. 6. Scherer, R.; Godoy, H. T. Food Chem., 2009, 112, pp 654-658. 7. Duarte-Almeida, J. M.; Santos, R. J.; Genovese, M. I.; Lajolo, F. M. Ciênc. Tecnol. Aliment., 2006, 26, pp 446-452. 8. Ferreira, S. L. C.; dos Santos, W. N. L.; Bezerra, M. A.; Lemos, V. A.; Bosque-Sendra, J. M. Anal. 49
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Pseudo-univariate calibration as an analytical tool to determine antioxidant activity: An alternative to DPPH method applied to the evaluation from extracts of turmeric powder
Bioanal. Chem., 2003, 375, pp 443-449. 9. Ribeiro, G. M.; Madivadua, D. A.; Curti, S. M. M.; Pantean, L. P.; Março, P. H.; Valderrama, P. Microchem. J., 2017, 134, pp 114-118. 10. Mamián-López, M.; Poppi, R. J. Anal. Chim. Acta, 2013, 760, pp 53-59. 11. Março, P. H.; Valderrama, P.; Alexandrino, G. L.; Poppi, R. J.; Tauler, R. Quim. Nova, 2014, 37, pp 1525-1532. 12. de Juan, A.; Tauler, R. Crit. Rev. Anal. Chem., 2006, 36, pp 163-176. 13. Parastar, H.; Tauler, R. Anal. Chem., 2014, 86, pp 286-297. 14. Vieira, J. A. G.; Jorge, N. Aliment. Nutr., 1997, 8, pp 39-47. 15. Gopal, J.; Muthu, M.; Chun, S. -C. RSC Adv., 2015, 5, pp 48391-48398. 16. Paulucci, V. P.; Couto, R. O.; Teixeira, C. C. C.; Freitas, L. A. P. Braz. J. Pharmacogn., 2013, 23, pp 94-100. 17. Rouhani, S.; Alizadeh, N.; Salimi, S.; Haji-Ghasemi, T. Prog. Color Colorants Coat., 2009, 2, pp 103-113. 18. Brand-Williams, W.; Cuvelier, M. E.; Berset, C. LWT – Food Sci. Technol., 1995, 28, pp 25-30. 19. Rodrigues-Brandão, I.; Kleinowski, A. M.; Einhardt, A. M.; Lima, M. C.; Amarante, L.; Peters, J. A.; Braga, E. J. B. Cienc. Rural, 2014, 44, pp 1893-1898. 20. Cheng, Z.; Moore, J.; Yu, L. J. Agric. Food Chem., 2006, 54, pp 7429-7436. 21. Jaumot, J.; de Juan, A.; Tauler, R. Chemom. Intell. Lab. Syst., 2015, 140, pp 1-12. 22. Savitzky, A.; Golay, M. J. E. Anal. Chem., 1964, 36, pp 1627-1639. 23. Sabin, G. P.; Lozano, V. A.; Rocha, W. F. C.; Romão, W.; Ortiz, R. S.; Poppi, R. J. J. Pharm. Biom. Anal., 2013, 85, pp 207-212. 24. Burns, D. A.; Ciurczak, E. W. Handbook of near-infrared analysis, 3rd ed. CRC Press, Boca Raton, 2008. 25. Kasemsumran, S.; Apiwatanapiwat, W.; Suttiwijitpukdee, N.; Vaithanomsat, P.; Thanapase, W. J. Near Infrared Spectrosc., 2014, 22, pp 113-120. 26. Kasemsumran, S.; Suttiwijitpukdee, N.; Keeratinijakal, V. Anal. Sci., 2017, 33, pp 111-115. 27. Jovanovic, S. V.; Steenken, S.; Boone, C. W.; Simic, M. G. J. Am. Chem. Soc., 1999, 121, pp 9677-9681. 28. Ak, T.; Gülçin, I. Chem.-Biol. Interac., 2008, 174, pp 27-37.
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Br. J. Anal. Chem., 2019, 6 (23) pp 51-54
Pittcon Conference & Expo 2019 Celebrated 70 Years of Laboratory Science and Instrumentation Innovation
Pittcon 2019 celebrated 70 years â&#x20AC;&#x201C; Photo: Facebook Pittcon
Pittconâ&#x20AC;&#x2122;s 70th annual meeting took place on March 17-21, 2019, at the Pennsylvania Convention Center, Philadelphia, USA. The number of participants in this edition of Pittcon was almost 10% higher than in 2018, reaching more than 12,500 delegates from 94 countries, representing government institutions, the academic sector and industry. In addition to the United States, the major five participating countries were China, Canada, Japan, the United Kingdom and Mexico. The interests of the participants were wide and included various scientific disciplines, from agriculture and food science to drug discovery and nanotechnology. The Pittcon exhibition featured 713 companies exhibiting newly developed laboratory instruments, products, and analytical services. Among these companies, 101 participated in Pittcon for the first time. Many of the company booths have provided visitors with the practical experience of new analytical technologies. Technical Program Traditionally, the main attraction of the Pittcon Conference is its dynamic Technical Program. In 2019, the Pittcon Conference Technical Program offered more than 2,000 technical sessions and 75 short courses. It featured high-level speakers such as Nobel Prize winners in chemistry who presented a wide variety of topics, including forensics, environmental, food science, nanotechnology, and materials science. In addition, 14 participants were honored with awards for their outstanding accomplishments in analytical chemistry.
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The Nobel Laureate, Dr. Fraser Stoddart started the Technical Program with a lecture titled “Serendipity Stokes Discovery: Disrupting Established Industries”, which focused on how members of his research group have made two important discoveries in the area of carbohydrate materials, each with the potential to become a ‘disruptive technology’ for environmentally friendly products and sustainable processes. Another important researcher who was present was Dr. Fenella France, from the Library of Congress, who took the main stage for the plenary lecture. As the Chief of the Preservation Research and Testing Division, Dr. France focused on her research on non-destructive imaging techniques and the Dr. Fraser Stoddart, a Nobel Prize in Chemistry prevention of environmental degradation to collections. She 2016 recipient, started the Pittcon Conference discussed the development of spectral imaging and image Technical Program - Photo: Supplied to Pittcon by F. Stoddart. processing techniques, and increasing links and access between scientific and scholarly data. The technical program also featured sessions organized by co-programming partners the International Association of Environmental Analytical Chemistry, Japan Analytical Instruments Manufacturers Association, National Institute of Justice, Society for Applied Spectroscopy, Society for Electroanalytical Chemistry, and the American Chemical Society. Exposition: Pittcon Park The three-day global exhibition is a great opportunity to know the latest innovation in laboratory instrumentation. In the center of the exhibition, was Pittcon Park, an interactive area where the participants were able to test their lab skills, see live product demonstrations and travel away from the expo floor for a few minutes. The Park housed DemoZones, the LEGO Gravity Car Racing, Virtual Reality Experiences, and the Lab Gauntlet, a series of laboratory challenges where attendees could win a free t-shirt or other prizes daily, and much more. The Pittcon Park also provided places to rest or have lunch.
Dr. Fenella France, Chief of the Preservation Research and Testing Division from the Library of Congress, gave the plenary lecture - Photo: Supplied to Pittcon by F. France.
Aerial image of Pittcon Expo 2019 - Photo: Event Disclosure 52
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Pittcon Exhibition Area - Photo: Lilian Freitas
The NEXUS Theaters offered an engaging “soft science” presentation experience that promoted networking. These theaters held 30-minute interactive and informative presentations with question-andanswer sessions, panels, and much more. The Pittcon 2019 Expo featured hundreds of vendors from around the globe showcasing their laboratory instrumentation, services and ancillary products. Pittcon Social Action Pittcon’s charitable action focus in 2019 was the Parent Education & Advocacy Leadership (PEAL) Center. Pittcon 2019 offered an extra space for the PEAL Center to increase awareness for individuals with autism and other disabilities. A selection of events to generate awareness and donations to reinforce the objectives of the PEAL Center was provided. This repeats a similar productive partnership that occurred in Pittcon 2011.
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About Pittcon Pittcon is the world’s largest annual conference and exhibition on laboratory science. Pittcon attracts more than 16,000 participants from industry, academia and government from over 90 countries worldwide.
Pittcon 2019: The Visible Difference in Laboratory Science Expositions – Photo: Lilian Freitas
Pittcon’s mission is to sponsor and sustain educational and charitable activities for the advancement and benefit of scientific endeavor. Pittcon’s target audience is not just “analytical chemists,” but all laboratory scientists — anyone who identifies, quantifies, analyzes or tests the chemical or biological properties of compounds or molecules, or who manages laboratory scientists. Having grown beyond its roots in analytical chemistry and spectroscopy, Pittcon has evolved into an event that now also serves a diverse constituency encompassing life sciences, pharmaceutical discovery and quality assurance (QA), food safety, environmental, bioterrorism and other emerging markets.
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Br. J. Anal. Chem., 2019, 6 (23) pp 55-62 PDF
This section is dedicated for sponsor responsibility articles.
Multi-Residue Pesticide Screening in Cereals using GC-Orbitrap Mass Spectrometry Dominic Roberts,1 Mette Erecius Poulsen,2 and Paul Silcock1 1
Thermo Fisher Scientific, Runcorn, United Kingdom. 2European Union Reference Laboratory for Pesticide Residues in Cereals and Feedstuffs, Technical University of Denmark, Denmark
Keywords: Pesticides, QuEChERS, Cereals, GC Orbitrap Mass Spectrometry, Screening, Quantitation, Accurate Mass, High Resolution, TraceFinder INTRODUCTION Pesticides are used to improve cereal crop yields and to minimize degradation during storage and processing. However, the widespread use of pesticides and the potential for residues to remain on the final product is of concern to consumers and to governments whose responsibility it is to ensure a safe food supply. Consequently, legislation has been introduced to protect consumers from exposure to contaminated foods [1]. Pesticide application to cereal crops is regulated by international organizations, and maximum residue levels (MRLs) are set for each pesticide/commodity combination. In the EU, if no substantive MRL has been set, a default MRL value of 0.01 mg/kg is usually applied. For complete coverage of the hundreds of pesticides in use, routine residue testing requires both liquid and gas chromatographic (GC) techniques coupled with mass spectrometers. Triple quadrupole mass spectrometers can provide the required sensitivity and selectivity to ensure that residue limits are not exceeded and the regulations are enforced. However, such targeted MS methods are limited to only detecting pesticides that are measured at the time of data acquisition and require careful method optimization and management to ensure selected reaction monitoring (SRM) windows remain viable. The alternative technique of high-resolution Orbitrap mass spectrometry provides distinct advantages over low-resolution MS/MS techniques and can substantially increase the scope of the analysis. With high-resolution mass spectrometry (HRMS), the default acquisition mode is untargeted (full-scan), making it simple to manage methods and allowing for a potentially unlimited number of pesticides to be monitored in a single injection. Unlike SRM acquisition on a triple quadrupole MS, high-resolution, fullscan data acquisition provides increased selectivity and enables retrospective interrogation of samples to search for emerging pesticides or other contaminants that were not screened for at the time of acquisition [2,3]. In this study, the performance of the Thermo Scientific Exactive GC Orbitrap mass spectrometer was evaluated for the routine analysis of GC-amenable pesticides in cereals (wheat, barley, oat, rye, and rice). The Exactive GC-MS system is routinely operated at a resolving power of 60,000 (measured at m/z 200 as full width at half maximum) for the detection of trace compounds against a complex chemical background as encountered in cereal sample extracts. MATERIALS AND METHODS Sample preparation Cereal samples (barley, oat, rice, rye, and wheat) were ground (or milled) to flour and then extracted using a citrate buffered QuEChERS procedure. The final acetonitrile extracts were acidified with 5% formic acid and diluted 1:1 with acetonitrile so that the standards and samples had the same level of matrix. Each cereal type was spiked with 105 pesticides prior to extraction at a concentration of 100 μg/kg with five replicate extractions performed. Further dilutions of this extract were made to 10 and 20 μg/kg. These concentrations were equivalent to 5, 10, and 50 μg/L in the vial after the 1:1 dilution. For the assessment of compound linearity, a calibration series in rye matrix was prepared over the range from 10 to 300 μg/kg. The 105 pesticides included in the study cover a wide 55
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range of chemical classes and, with the five matrices, a total of 525 pesticide/matrix combinations were generated. The pesticides chosen in this study are not usually found as part of routine screening, therefore, their performance on the system was tested. The performance of more routine pesticides has been studied previously [2,3]. Instrument and method setup In all experiments, an Exactive GC Orbitrap mass spectrometer was used. Automatic sample injection was performed using a Thermo Scientific™ TriPlus™ RSH™ autosampler, and chromatographic separation was obtained with a Thermo Scientific™ TRACE™ 1310 GC and a Thermo Scientific™ TraceGOLD™ TG-5SilMS 30 m × 0.25 mm I.D. × 0.25 μm film capillary column with a 5 m integrated guard (P/N 26096-1425). Additional details of instrument parameters are displayed in Tables I and II.
Table I. GC and Injector Conditions
Table II. Mass Spectrometer Conditions
TRACE 1310 GC System Parameters
Exactive GC MS Parameters
Injection Volume (μL):
1 splitless
Transfer Line (°C):
280
Liner*:
Siltek 1
Ionization type:
EI
Inlet (°C):
70
Ion Source (°C):
250
Split Flow (mL/min):
50
Electron Energy (eV):
70
Transfer Rate (°C):
2.5
Acquisition Mode:
Full-scan
Final Temperature (°C):
300
Mass Range (Da):
50-600
Carrier Gas, (mL/min):
He, 1.2
Resolving Power (FWHM at m/z 200):
60,000
Lockmass, Column Bleed (m/z):
207.03235
Oven Temperature Program Temperature 1 (°C):
40
Hold Time (min):
1.5
Temperature 2 (°C):
90
Rate (°C/min):
25
Hold Time (min):
1.5
Temperature 3 (°C):
280
Rate (°C/min):
5
Hold Time (min):
0
Temperature 4 (°C):
300
Rate (°C/min):
10
Hold Time (min):
5
*Liner Siltek 1, splitless six baffle PTV liner (P/N: 453T2120) Data processing Data were acquired using the Thermo Scientific™ TraceFinder™ software. This single platform software package integrates instrument control, method development functionality, and qualitative and quantitation-focused workflows. For targeted analysis, a customised compound database contained the 105 compound names, accurate masses for quantification and identification ions, retention times, and elemental compositions of fragment masses. For the generation of extracted ion chromatograms, an extraction mass window of ±5 ppm was used. 56
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RESULTS AND DISCUSSION The objective of this study was to screen for 105 pesticides in five replicate extractions of different cereal matrices with a high degree of confidence. The lowest concentration at which each pesticide could be detected was to be determined. Further assessments of mass accuracy, linearity in matrix, and repeatability are also reported. The five sample types chosen provided both typical and difficult matrices that are encountered in routine cereals testing. The full-scan total ion chromatograms shown in Figure 1 illustrate the high complexity and diversity of the different cereal samples. This is one reason why high-resolution, accurate-mass mass spectrometry is required to selectively extract target analytes from background chemical noise. In comparison to most fruit and vegetable samples, cereals have a high fat content that results in heterogeneous extracts when generic extraction techniques are used. The low selectivity of the QuEChERS sample extraction approach needs to be compensated for by selective instrumental analysis. On the Exactive GC, this is achieved using high mass resolving power. This capability, in combination with a full-scan acquisition, increases the scope of the analysis without the need for optimization of acquisition parameters, as is the case with targeted analyses.
Figure 1. Full-scan total ion chromatogram (TIC) with zoomed Y axis of cereal extracts showing the complexity of the sample matrices used in this study.
The primary aim of the analysis was to determine how many of the fortified pesticides could be detected at each of the concentration levels (10, 20, and 100 μg/kg). For a positive detection, the following criteria based on SANTE guidelines [4] had to be satisfied: 1. Two ions detected for each pesticide with mass accuracy < 5 ppm and peak S/N > 3. 2. Retention time tolerance of ± 0.1 minutes compared with standards in the same sequence. 3. Ion ratio within ±30% of the average of calibration standards from the same sequence. Intelligent data processing TraceFinder software provides automated data acquisition and processing that quickly extracts and displays the identification information for all 105 spiked pesticides in approximately 20 seconds per sample file (0.75 GB). The software enables the analyst to rapidly review the data and to confidently confirm the presence of a pesticide. As Figure 2 shows, the analyst is presented with a traffic light 57
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system alongside raw data to show which identification criteria have been satisfied. More importantly, it will also flag when a parameter is outside of expected tolerance and alert the analyst to carefully review all of the available information before making the final decision to confirm a positive identification. In the example in Figure 2, the ion ratio of one of the fragment ions of isocarbophos in oat sample A (46.7%) is just outside the allowable ratio window of 48-89% due to peak integration. This is flagged to the analyst by a red square in the ion ratio (IR) column. By hovering over this square, further details are displayed. In this case, isocarbophos can be confirmed despite this flag as the other criteria are met and alternative fragment ion ratios are within the 30% tolerance. The multiple identification points provided by full-scan analysis along with user friendly software enables a faster time to result, which is vital in routine pesticide analysis.
Figure 2. TraceFinder software browser enables fast data review and confirmation. The software quickly points the analyst to the data that supports a positive identification using a traffic light system along with real data values. More importantly, it will flag when a parameter is outside of tolerance, and by what value, and allow the analyst to make the final decision to confirm an identification. Hovering above the red square (below) brings up further details.
Following the criteria listed previously, the lowest concentration level at which each pesticide was detected and confirmed in each of the five matrices is summarized in Figure 3. Of the 525 pesticide/matrix combinations, 90% were confirmed at ≤ 10 μg/kg and 96% at ≤ 20 μg/kg. Having multiple identification points and limits of detection below the MRL increases the confidence in positive detections. This also minimizes the risk of false negative results and ensures that the limits of false positive detects are at a manageable level within a routine environment. All 105 pesticides were detected at concentrations lower than 10 μg/kg (5 μg/L in vial) if screened based on retention time and the main quantifier ion. The limiting factor for confirmed identification in the case of a few analytes was the sensitivity of additional ions that were much lower in intensity compared to the main ion. As the criteria applied here has shown, using electron ionization (EI) in combination with full-scan acquisition provides the opportunity to use multiple diagnostic ions for the identification of pesticides. In addition to individual ions, compound spectra can be used to confirm identifications. The Exactive GC generates standard EI spectra that are highly reproducible and library searchable (using nominal- or high-resolution MS libraries commercially available or custom made). An example of spectral matching with NIST 2014 for the pesticide mexacarbate (SI 905) is shown in Figure 4.
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Figure 3. The lowest concentration confirmed (two ions within 5 ppm, ion ratios within ±30%) for each pesticide in each of the five sample matrices. The total number of pesticides is 105.
Figure 4. TraceFinder software deconvoluted peaks (left). Acquired spectrum and library spectrum (right) for mexacarbate with search index score of 905.
True mass accuracy Acquiring reliable accurate mass measurements is critical when detecting pesticide residues at low concentrations in complex sample matrices. Low mass errors ensure that compound selectivity is high and that detection and identification are robust. The low mass errors (ppm) observed with the Exactive GC are achieved through the high mass resolving power that can discriminate between matrix interferences and target analyte ions. Internal mass correction enables mass accuracies of ≤ 1 ppm to be consistently achieved regardless of analyte concentration or matrix complexity. As an example, the mass accuracy of all detected pesticides in wheat at 10 μg/kg is shown in Figure 5. All pesticides are detected with sub-1 ppm mass accuracy, well below the guideline limit of 5 ppm (< 1 mDa for m/z < 200), delivering the highest confidence in accurate and selective detection. The low mass accuracy also allows for tighter tolerances to be applied for extracted ion chromatograms, which will result in fewer false positive detects thus increasing efficiency by reducing the need for manual review. When the mass resolution is insufficient, it can result in target ions that have a mass accuracy outside of the required identification criteria. This is demonstrated in Figure 6 where the oat 20 μg/kg matrix sample was analyzed at resolving powers of 15K, 30K, and 60K. The zoomed mass spectra show the 59
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quantifier ion for tribufos. At 15K and 30K, the m/z 201.97042 ion demonstrates poor mass resolution resulting in mass accuracies of 6.4 and 3.7 ppm, respectively. However, the ion is well resolved at 60K resulting in the expected sub-1 ppm mass accuracy. At 15K this pesticide would have failed the identification criteria of < 5 ppm and would have been reported as not detected.
Figure 5. Mass difference measurements at 10 Îźg/kg for each pesticide in wheat.
Figure 6. Effect of resolving power on mass accuracy of diagnostic ion (m/z 201.97042) tribufos at 20 Îźg/kg in oat acquired at different resolutions of 15K, 30K, and 60K.
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Robust quantitative performance Having reliably identified a pesticide in a sample, the final stage is to determine its concentration. The Exactive GC quantitative linearity was assessed using matrix matched standards in rye across a concentration of 10-300 μg/kg. In all cases, the coefficient of determination (R2) was > 0.99 for each pesticide from its LOD value to 300 μg/kg. An example of the TraceFinder software quantification results browser showing dichlorprop methyl ester is given in Figure 7. A final assessment was made of the peak area repeatability at low analyte level by running n = 20 replicate injections at 10 μg/kg in wheat. All detected pesticides had RSD% of less than 13%, (Figure 8). This shows that the Exactive GC operated in full-scan at 60k resolution has the selectivity and sensitivity required for robust and reliable routine anlysis of pesticides residues at or below the MRLs in a range of different types of cereal samples.
Figure 7. TraceFinder software browser showing positively identified pesticides, extracted ion chromatogram, and calibration graph (dichlorprop methyl ester as an example). Sub-ppm mass accuracy for dichlorprop across the calibration range and in replicates of 20 mg/kg. Identification criteria information is available and flagged when out of tolerance for quick data review.
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Figure 8. Repeatability (%RSD) for 10 μg/kg (n=20) for each pesticide in wheat.
CONCLUSIONS The results of this study demonstrate that the Exactive GC Orbitrap high-resolution mass spectrometer, in combination with TraceFinder software, delivers robust and sensitive performance for routine pesticide analysis in cereals to regulatory standards. • All 105 pesticides were detected at 10 μg/kg (5 μg/L in vial). 96% of the 525 pesticide/matrix combinations were confirmed at < 20 μg/kg (< 10 μg/L in vial) with excellent linearity, and in full compliance with the EU SANTE method performance criteria. • The full scan acquisition permits efficient targeted data processing by use of a compound database and has the capability to easily add further analytes into the method scope. • Intelligent software allows for results to be reviewed and detections confirmed in an efficient manner. • Consistent sub-ppm mass accuracy was achieved for all compounds over a wide concentration range, ensuring that compounds are detected with high confidence at low and high concentration levels. • Repeated injections of a wheat matrix at 10 μg/kg showed that the system is able to maintain a consistent level of performance over an extended period of time as is demanded by a routine testing laboratory. REFERENCES 1. http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=URISERV:l13002a 2. Mol, H. G.; Tienstra, M.; Zomer, P. Anal. Chim. Acta, 2016, doi: 10.1016/j.aca.2016.06.017. 3. Thermo Fisher Scientific Application Note: 10509. Routine quantitative method of analysis for pesticides using GC-Orbitrap MS in accordance with SANTE/11945/2015 guidance. 4. SANTE/11945/2015. Guidance document on analytical quality control and method validation procedures for pesticides residues analysis in food and feed. Supersedes SANCO/12571/2013. Implemented by 01/01/2016. This sponsor report is the responsibility of Thermo Fisher Scientifc.
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Br. J. Anal. Chem., 2019, 6 (23) pp 63-69 PDF
This section is dedicated for sponsor responsibility articles.
Total Elemental Analysis of Food Samples for Routine and Research Laboratories using the Thermo Scientific iCAP RQ ICP-MS Tomoko Vincent1, Simon Lofthouse2, Daniel Kutscher1 and Shona McSheehy Ducos1 1
Thermo Fisher Scientific, Bremen, Germany. 2Thermo Fisher Scientific UK
Keywords: Arsenic, Automation, Food safety, He KED, High matrix, High-throughput, iCAP RQ ICPMS, Multielement, Quality control, Rice, Speciation INTRODUCTION The measurement of toxic, essential and nutritional elements in food has become a major topic of public interest in recent years. Intergovernmental bodies sponsored by the Food and Agricultural Organization and the World Health Organization are responsible for developing standard test methods for the analysis of food samples. Alongside this regulatory compliance, it is important to monitor toxic contaminants that could potentially enter the food chain via a series of pathways such as industrial pollution or environmental contamination. Once toxic elements are in the food chain, they can pose significant health risks. For these reasons, it is essential to have a simple, robust, multielemental analysis method for major and minor concentrations of elements in food. The elemental and dynamic range of single quadrupole (SQ) ICP-MS makes it particularly suited to the analysis of food, simultaneously determining trace level contaminants and macro level nutrients. In some cases, a sample may contain matrix that leads to specific interferences that can only be effectively resolved using triple quadrupole (TQ) ICP-MS. The goal of this work is to demonstrate how simultaneous determination of all elements of interest in a wide range of food samples can be efficiently and rapidly performed using the Thermo Scientific™ iCAP™ RQ ICP-MS. MATERIALS AND METHODS Sample preparation Certified Reference Materials (Rice Flour IRMM-804 and Chicken NCS ZC73016) were prepared to evaluate the proposed SQ-ICP-MS method. Approximately 0.5 g of each sample were acid digested using a mixture of HNO3 and HCl in a closed vessel microwave digestion system. After digestion, the samples were made up to volume (50 mL) using ultrapure water. The standard calibration solutions, blank and rinse solution were all prepared in 1% (v/v) HNO3. The major elements (Na, Mg, P, S, K and Ca) were prepared at calibration concentration levels of 25, 50 and 100 mg L-1, while the minor elements (balance of analytes) were prepared at concentrations of 25, 50 and 100 μg∙L-1. Internal standard correction was applied with Ga, Rh, and Ir at 20, 10 and 10 μg L-1 respectively. Instrument configuration A Thermo Scientific™ iCAP™ RQ ICP-MS was used for all measurements. The sample introduction system used consisted of a Peltier cooled (3 ºC), baffled cyclonic spraychamber, PFA nebulizer and quartz torch with a 2.5 mm i.d. removable quartz injector. The instrument was operated using kinetic energy discrimination (KED) using pure He as the collision gas in the collision/reaction cell (CRC). To automate the sampling process, an Elemental Scientific SC-4 DX Autosampler (Omaha, NE, USA) was used.
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General analytical conditions The iCAP RQ ICP-MS was operated in a single He KED mode using the parameters presented in Table I. Table I. Instrument Operating Parameters Parameter
Value
Forward Power
1500 W
Nebulizer Gas
0.9 L min-1
Auxiliary Gas
0.8 L min-1
Cool Gas Flow
14.0 L min-1
CRC Conditions
4.5 mL min-1 at He, 3V KED
Sample Uptake/Wash Time
45 s each
Dwell Times
Optimized per analyte
Total Acquisition Time
3 min
RESULTS The use of a single, comprehensive He KED mode is made possible through the use of unique Thermo Scientific QCell™ flatapole technology. Sample throughput is significantly improved with the single analysis mode – a key advantage for the analysis of food, since large numbers of samples may have to be screened rapidly. High transmission of the iCAP RQ ICP-MS QCell provides sufficient low mass sensitivity for accurate analysis of low mass analytes such as Li, so that all analytes can be reliably measured in one single measurement mode. Table II shows the typical detection limits achievable for a range of analytes measured by this method. Taking into account the 1:100 dilution factor required for this analysis, the data shows that μg kg-1 range method detection limits are achieved with ease for all analytes. Detection limits for all the major constituent elements are well below the target levels required for food analysis. Figures 1 and 2 show typical external calibration curves for the low concentration (Li, 0-100 μg L-1) and high concentration (Na, 0-100 mg L-1) analytes determined in the same analytical run with the iCAP RQ ICP-MS in single He KED mode. The results of the rice flour and chicken reference material measurements are presented in Table II. Excellent agreement was observed between the measured and reference values for all target analytes in the two reference materials analyzed. As part of this study, the reference materials were repeatedly analyzed during the analysis. Five independent measurements were made of separate aliquots of each reference material to assess the repeatability of the method. The results in Table II demonstrate that excellent reproducibility was achieved for the five repeat analyses of rice flour and chicken reference materials over 8 hours, with RSD’s of <2 % obtained for all of the elements determined.
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Table II. Dilution corrected MDLs and results for two certified reference materials: relative standard deviation is calculated for 5 independent analyses. All concentrations reported in μg L-1 except where stated. Isotope 7
MDL*
IRMM-804 Rice
NCS ZC73016 Chicken
Measured
Certified
%RSD
Measured
Certified
%RSD
Li
3
-
-
-
28 ± 1
34 ± 7
1.9
B
10
-
-
-
730 ± 23
760 ± 130
1.9
11
23
Na
0.3 (mg L-1)
-
-
-
1310 ± 25
1440 ± 90
1.3
25
Mg
0.01 (mg L-1)
-
-
-
1200 ± 22
1280 ± 100
1.1
31
P
0.6 (mg L-1)
-
-
-
8950 ± 220
9600 ± 800
1.7
34
S
9 (mg L-1)
-
-
-
8310 ± 220
8600 ± 500
1.9
39
K
0.5 (mg L-1)
-
-
-
14000 ± 480
14600 ± 700
1.8
44
Ca
0.2 (mg L-1)
-
-
-
200 ± 4
220 ± 20
1.7
52
Cr
0.2
-
-
-
450 ± 10
590 ± 110
0.9
55
Mn
1
35800 ± 470
34200 ± 2300
0.5
1640 ± 20
1650 ± 70
0.8
56
Fe
4
-
-
-
32700 ± 260
31300 ± 3000
0.7
60
Ni
2
-
-
-
153 ± 2
150 ± 30
0.8
65
Cu
0.8
2650 ± 30
2740 ± 240
0.4
1350 ± 11
1460 ± 120
0.7
66
Zn
2
23100 ± 270
23100 ± 1900
0.7
25300 ± 220
26000 ± 1000
0.6
75
As
0.2
52.3 ± 0.8
49 ± 4
1.4
115 ± 1
109 ± 13
0.9
78
Se
1
35.1 ± 1.0
1.3
549 ± 11
490 ± 60
1.6
88
Sr
0.1
-
-
-
611 ± 11
640 ± 80
1.6
98
Mo
1
-
-
-
112 ± 1
110 ± 10
1.9
Cd
0.3
1620 ± 9
1610 ± 70
0.7
-
-
-
138
Ba
0.3
-
-
-
1610 ± 16
1500 ± 400
1.4
141
Pr
0.02
-
-
-
2.6 ± 0.1
2.8 ± 0.6
1.6
208
Pb
0.1
460 ± 8
420 ± 70
0.8
90.7 ± 2.0
110 ± 20
1.0
111
38
(Reference value)
*Method Detection Limit
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Figure 1. Calibration curve for 7Li in He KED mode.
Figure 2. Calibration curve for 23Na in He KED mode.
Quality control with Qtegraâ&#x201E;˘ Intelligent Scientific Data Solutionâ&#x201E;˘ (ISDS) Software Quality control is critical in routine analysis. To ensure quality control with the high matrix samples described in this method, the internal standards were monitored and continuing calibration checks (CCVs) were performed periodically throughout the analytical run. The absolute suppression and relative drift of the internal standards was evaluated throughout the analysis, further demonstrating the stability and robustness of the iCAP RQ ICP-MS for prolonged measurement of high matrix samples. The variation in the internal standard signals during the run is shown in Figure 3.
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Figure 3. Variation of the internal standard intensities throughout the 8 hours analysis.
The analysis was evaluated for 8 hours, allowing the analysis of more than 150 samples. The minimal variation in the internal standard signals highlights the excellent robustness of the iCAP RQ ICP-MS in terms of both matrix resistance and interference removal for food samples analysis. Powerful, comprehensive He KED mode effectively removed complicated interferences and delivered accurate measurement results. Continuing calibration checks (CCVs) and the reference materials were periodically analyzed throughout the analytical run with good agreement to expected levels illustrating the robustness of the method. Six CCV checks were analyzed at intervals during the 8 hours analysis. Figure 4 shows the average concentration of the CCV standard and the in-run relative error for a range of high and low level analytes. The results from the CCV checks throughout the analysis show that there was minimal drift between the batches of food samples, eliminating the need for any sensitivity re-calibration within the 8 hours analysis period.
Figure 4. Calibration checks verification standards measured. 67
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Dynamic range control with user defined mass resolution Normal resolution or high resolution modes can be selected easily within the Qtegra ISDS Software (Figure 5). This function is particularly useful to extend dynamic range, in food, environmental and clinical research samples.
Figure 5. Screen shot measurement mode from Qtegra ISDS Software.
The normal resolution mode has 0.75 u peak width at 10% of the peak height and high resolution mode has a narrow 0.25 u peak width. Using this user selectable, high resolution mode, sensitivity is reduced in order to generate a linear calibration curve with a wide concentration range. This feature can be used for analytes such as sodium, where due to low ionization potential energy (5.1 eV) and high sensitivity in hot plasma, a calibration up to 1000 mg L-1 can be outside the performance capabilities of the SQ-ICP-MS detectorâ&#x20AC;&#x2122;s dynamic range. Figure 6 shows a full calibration of 23Na at 0, 5, 250, 500 and 1000 mg L-1 with R2=1.000 linearity and background equivalent concentration (BEC) of only 6 Îźg L-1 using high resolution and He KED mode.
Figure 6. Calibration curve for 23Na in He KED mode at 5, 250, 500 and 1000 mg L-1.
IC-ICP-MS speciation analysis in organic brown rice syrup with the iCAP RQ ICP-MS For some types of food, the concentration of a given element may not be sufficient to judge potential hazards. For example, As may be present in both inorganic forms, such as As (III) and As (V), as well as organic forms (e.g. arsenobetaine and methylated forms), which exhibit different toxicological properties. Elevated concentrations of As in foodstuffs such as rice or rice derived products are occasionally reported in the media, and speciation analysis is required to determine whether the As found is either toxic inorganic As or rather harmless organic As. Currently, regulatory authorities strive for maximum concentration levels for As in a variety of foodstuffs. Speciation analysis comprises the separation of different compounds containing a given element 68
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using, for example, Ion Chromatography (IC) followed by selective and sensitive detection of the element using ICP-MS. Based on previous work undertaken by Thermo Fisher Scientific Application Specialists, speciation analysis of As was accomplished using the Thermo Scientific Dionexâ&#x201E;˘ ICS5000+ ion chromatography system coupled to the iCAP RQ ICP-MS. The hyphenated system can be integrated into the Qtegra ISDS Software used for operation of the iCAP RQ ICP-MS using the Thermo Scientific ChromControl Plug-in. As for conventional As analysis, He KED mode was used to efficiently reduce polyatomic interferences affecting the detection of monoisotopic As at m/z 75. The method allows the determination of six As species often encountered in food analysis: The two toxic inorganic As species, and four organic species which are considered harmless. Whereas some samples, for example water or beverages may be simply diluted, for rice and rice derived products such as organic brown rice syrup (OBRS, often used as an organic sweetener for example in cereals and cereal bars), a mild extraction is required. Preparation of OBRS samples for As speciation was achieved by taking 1.5 g of sample, adding 15 mL of 0.28 M HNO3 and refluxing for 90 minutes. This procedure is also suitable for As species extraction from rice grains. Chromatographic separation of the As species under investigation is shown in Figure 7. As can be seen, OBRS contains mostly As (III) and As (V), so one of the toxic forms of As, but also methylated DMA can be observed. Each species of As was identified using comparative retention times of a standard, and automatic peak area integration for quantification was accomplished using the tQuant data evaluation plugin included in the Qtegra ISDS Software.
Figure 7. IC-ICP-MS chromatogram of (top) arsenic standards and (bottom) Arsenic species found in a OBRS sample. As(III) was the most abundant species detected.
This sponsor report is the responsibility of Thermo Fisher Scientifc.
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Br. J. Anal. Chem., 2019, 6 (23) pp 70-74 PDF
This section is dedicated for sponsor responsibility articles.
Simplifying Mixed-Food Microwave Sample Preparation for ICP-MS Analysis Utilizing Single Reaction Chamber (SRC) Technology for Trace Metals Analysis for Food Samples INTRODUCTION Low-level analysis of food matrices has placed a demand on manufacturers, testing laboratories, and instrumentation vendors worldwide. Stricter regulations, better analytical instrumentation, and greatly improved sample preparation (pre-analytical) techniques have focused eff orts to simplify and standardize these analyses. Often overlooked, the preanalytical step determines the quality of the resulting data and requires careful attention to a number of details, including sample size, digestion parameters, and the level of detection needed. This article describes the selection of critical factors used in the optimization and simplification of food and mixed-batch food microwave digestion. Regulatory bodies such as the Food and Drug Administration (FDA), United States Department of Agriculture (USDA), and European Commission (EC) have established element concentration limits on food and agricultural products. A recent addition to the methods menu from the FDA provides a guideline for the pre-analytical step for microwave sample digestion followed by inductively coupled plasma mass spectrometry (ICP-MS) analysis of trace metals in food [1]. Many publications and the Association of Analytical Communities (AOAC) performance methods have validated microwave digestion as a standard approach to food analysis. In fact, Method 4.7 from the FDA is an example of an internal validation exceeding standards set forth by the AOAC. The USDA has also recently updated their chemistry laboratory guidebook for the analysis of food utilizing inductively coupled plasma optical emission spectrometry (ICP OES) and ICP-MS following sample microwave digestion [2]. The important feature in many of these methods relates to the choice of analytical tools and sample preparation technique, which leads to the questions: What is the best way to perform sample preparation and which instrument is best for multi-element analysis within a lab setting? Sample preparation is given little attention in the overall process for the analysis of trace metals, and for many sample types it turns out to be routine. Normally, scientists simply look at the detection limits for an ICP OES or ICP-MS system and calculate back from a detection level or interferences to determine the instrument of choice. Unfortunately, with highly organic sample types and the breadth of complexity increasing, the need for a good quality digestion process is more critical. A list of preanalytical contributing factors that can affect the backend analysis includes sample size, acid digestion volume, sample fat and organic content, contamination, throughput, and post-digestion residual carbon content. Each of these will play a role in the methods chosen for processing a food sample. Larger samples sizes have been used because of the limits of analytical detection and sample inhomogeneity. The consideration on what to use for the sample preparation then becomes a choice between ashing and microwave digestion. Ashing is complicated by the loss of critical elements such as Cd, Pb, and Hg along with contamination issues [3]. Microwave digestion also historically has been a concern with the lack of vessel temperature and pressure capability for complete digestion of reasonably sized sample amounts. This picture becomes even more opaque with incomplete digestions causing problems for the ICP OES or ICP-MS analysis with a resulting high residual carbon content [4â&#x20AC;&#x201C;6]. While ICP OES dominates the testing laboratory environment, there is consistent movement toward ICP-MS with all of the advantages and trace metal regulations it offers [7]. Conventional closed-vessel microwave digestion has been recognized for the past two decades as the most effective technique for the digestion of the widest range of sample types in metals analysis, largely because of its ability to operate at high temperatures and pressures. Because the digestions are in a closed vessel, cross-contamination and the loss of volatile elements are eliminated [8]. Additionally, 70
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microwave sample preparation techniques have overcome challenges of poor digestion quality by providing better digests with low residual acidity, lower carbon content, and lower concentrations of dissolved solids, a critical feature necessary for back-end ICP-MS and ICP-OES analysis [9]. It was recently suggested that an ideal sample preparation procedure would be capable of digesting large sample amounts, provide high throughput, be compatible with multi-element analysis, and be safe, green, and easy to use [9]. A recent publication illustrates the importance of sample size, lower acid consumption, complete digestion with low residual carbon, and the effects of temperature and pressure with a variety of food samples in a conventional microwave and a new single reaction chamber technology [10]. This research goes a long way into the basic understanding of how to think about sample preparation as a process. To further illustrate the completeness of sample preparation, we used a single reaction chamber (SRC) microwave digestion approach for processing mixed-batch and large food samples. METHODS AND MATERIALS SRC microwave digestion has been utilized extensively in the last several years. The operation, specification, sequence of operation, and application have been published for further reading [11,12]. The SRC uses a large, pre-pressurized chamber where multiple independent sample types are digested simultaneously up to 300 °C and 199 bar (including multiple acid combinations for determining the right digestion chemistry). By choosing different size racks to fit inside the chamber, different sample sizes and throughput can be performed, depending on internal needs. Pre-pressurizing the chamber allows a single microwave method to be chosen for all types without the need for extensive method development. All digestions were developed using an UltraWAVE based on the SRC design. The microwave delivers 1500 W of microwave power to a 1L stainless-steel reinforced PTFE chamber that can hold sample racks of 5, 15, or 22 vessels depending on sample and acid volume. Direct temperature and pressure control provide direct control of each sample (all samples reach the same temperature and pressure). The experiments described in this article were performed using a 15-position rack with 15 mL disposable glass vials and a 5-position rack with quartz vials for large sample sizes. An Agilent 7700x ICP-MS system was used for the analysis of all elements. The operating conditions for the ICP-MS system were as follows: RF applied power to the torch, 1.55 kW; carrier gas flow, 0.95 L/min; dilution gas flow rate, 0.15 L/min; plasma mode normal, He cell gas flow, 4 mL/min. Operating in He collision mode for samples allowed for routine robust analysis. Microwave Sample Preparation Samples were selected to include a variety of food types along with standardized reference materials. The primary “big four” toxic metal profiles were used to establish a microwave method and instrument baseline numbers. Following this approach, an expanded set of metals was determined using the same microwave method. The final experiment examined a set of samples with sizes greater than 2 g to illustrate the feasibility of processing larger food samples with an identical microwave method established in the previous experiments (expands the capability to use ICP OES in the well-established food industry). Typical food samples (0.5 g) were placed in disposable glass test tubes and placed in a 15-position rack for microwave operation. The samples were treated with a 4 mL nitric acid and 0.5 mL hydrochloric acid combination (for Cd, Pb, As, and Hg stabilization) or 5 mL of nitric acid for the expanded list of elements and placed in the microwave chamber (note: the PTFE liner contains 100 mL of water, 10 mL of hydrogen peroxide, and 3 mL of sulfuric acid as the microwave load for parameter control) Following pre-pressurization of the chamber with nitrogen to 40 bar, the samples were digested using the following time–temperature microwave method: Ramp to 240 °C over 30 min, hold at 240 °C for 15 min, cool to 60 °C, and depressurize the chamber for analysis. The total time was 55 min. It should be noted that pressures can reach 100 bar or greater during the course of a digestion and sample decomposition, 71
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illustrating one of the critical needs to have a system capable of higher temperatures and pressures for quality digestions. Systems or vessels venting during the course of these conditions would preclude development with the loss of volatile elements at a partial digestion point during a method. Table I. ICP-MS Analysis of Mixed-Food Samples following SRC Digestion (0.5 g, 4 mL HNO3, 0.5 mL HCl; 30 min to 240 °C with 15 min at 250 °C) Quality Control Summary Concentration (ppb) Arsenic
Cadmium
Mercury
Lead
0.001
0
<0.005
0.089
Spike Concentration
100
100
100
100
Spike Result
103
103
105
106
Spike Recovery %
97
97
5
94
Spike Dup Result
101
100
103
106
Duplicate recovery %
99
100
97
94
Detection Limit (ppt)
11.9
2.9
1.2
1.3
Blank
Sample Summary Concentration (ppm) Apple Certified Values
0.038
0.013
0.044
0.47
Apple SRM 1515
0.041
0.011
0.04
0.45
Apple SRM 1515 dup.
0.044
0.01
0.038
0.44
Olive Leaves
0.01
0.009
0.02
0.097
Apple Leaves
0.021
0.088
0.023
0.09
Cheese
0.052
0.062
0.054
0.132
Candy Bar
0.008
0.008
0.028
0.084
Yogurt
0.055
0.089
1.46
0.12
RESULTS Three separate microwave digestion runs were performed with a variety of food sample types and sizes. Each microwave method formed clear digestates with lower acid volumes, that were diluted with water to 25 mL, followed by a 2 mL aliquot diluted to 40 mL, and analyzed using ICP-MS. The first sample set was analyzed for As, Cd, Hg, and Pb. The second set was expanded to include Mg, Mn, Cu, Fe, and Zn. Last, larger sample sizes were digested and analyzed for Se and Hg. For quality control (QC) an acid blank, spikes, and duplicates were analyzed as part of ongoing instrument calibration verification. Quantitative data, detection limits, and QC data are shown in Tables I, II, and III.
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Table II. ICP-MS Analysis of Mixed-Batch Samples Following SRC Digestion (2-25 g 10 mL HNO3, 4 mL H20, 1 mL HCL; 30 min to 240 ยบC w/ 15 min at 240 ยบC) Quality Control Summary Concentration (ppb) Magnesium
Iron
Manganese
Copper
Zinc
0
0
0
0
0
Spike Concentration
100
100
100
100
100
Spike Result
98
100
96
106
95
Spike Recovery %
98
100
96
100
95
Spike Dup Result
96
101
94
98
100
Duplicate recovery %
96
100
94
98
100
Detection Limit (ppt)
2.8
14.8
8.5
2.7
14
Blank
Sample Summary Concentration (ppm) Tomato 1573 Certified
-
368
246
4.7
30.9
Tomato 1573a
-
365.1
241.6
4.35
29.5
Tomato 1573a dup
-
367.8
243.6
4.4
30.1
Apple Leaves
2.051
92.9
79.3
9.2
43.1
Olive Leaves
1.917
88.2
28.2
31.3
18.5
Cheese
6.5
124.2
38.1
45.08
65.35
Peanut Butter
6.27
67.5
12.4
16.7
31.5
Milk Powder
ND
1.75
0.251
0.68
45.2
Table III. ICP-MS Analysis of Large Sample Sizes Following SRC Digestion (2-25 g 10 mL HNO3, 4 mL H20, 1 mL HCL; 30 min to 240 ยบC 15 min at 240 ยบC) Sample Summary Concentration (ppb) Selenium
Mercury
54
34
Tomato 1573a
52.48
35.609
Rice
294.53
27.56
Candy Bar
33.106
26.897
Cheese
31.865
50.695
Yogurt
134.65
1.463.775
Instrument QC
52.011
0.9939
Tomato 1573 Certified
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CONCLUSION Given the high-throughput nature of testing labs, the standardization of the SRC microwave method aligns well with the use of ICP OES or ICP-MS analysis for all sample types. SRC microwave digestion in food, testing, and manufacturing labs for the analysis of trace metals provided a single optimized sample prep method. To achieve complete digestions for multiple sample types simultaneously, an optimized temperature of 240 °C was used; disposable glass vials were used to eliminate the need for vessel cleaning in subsequent digestion runs. With a 15-position rack and a 55 min digestion time start to finish, a series of multiple samples can be processed for multi-element trace metals. Standardizing the SRC method provided a way to scale the process to larger sample sizes without the need for extensive method development, batching, vessel cleaning, and assembly. REFERENCES 1. Gray, P.; Mindak, W.; Cheung, J. FDA Elemental Analysis Manual for Food and Related Products, Section 4.7, 2013. 2. USDA, Determination of Metals by ICP-MS and ICP-OES (Optical Emission Spectrometry) USDA, Food Safety and Inspection Service, 2013. 3. Barin, J. S.; Tischer, B.; Picoloto, R. S.; Antes, F. G.; Silva, F. E. B.; Paula, F. R.; Flores, E. M. M. J. Anal. At. Spectrom, 2013, 29, pp 352–358. 4. Wurfels, M.; Jackwerth, E.; Stoeppler, M. Anal. Chim. Acta, 1989, 26, pp 1-16. 5. Fecher, P.; Ruhnke, G. At. Spectrosc., 1998, 19, pp 204–206. 6. Grindlay, G.; Gras, L.; Mora, J.; de Loos-Vollebregt, M.T.C. Spectrochim. Acta Part B, 2008, 63, pp 234–243. 7. McSheehy, S.; Hamester, M.; Godula, M. Food Qual. Saf., 2010. 8. Gunn, D. Spectroscopy supplement: Applications of ICP & ICP-MS Techniques for Today’s Spectroscopists, 2013, 28 (s11), pp 8–16. 9. Nobrega, J. A.; Pirola, C.; Fialho, L. L.; Rota, G.; de Campos Jordão, C. E.; Pollo, F. Talanta, 2012, 98, pp 272–276. 10. Mueller, C. C.; Muller, A. L. H.; Pirola, C.; Duarte, F. A.; Flores, E. M. M.; Muller, E. I. Microchem. J., 2014, 116, pp 255–260. 11. Michel, T. Amer. Lab., 2010, 42, pp 32–35. 12. Michel T.; Hussain, S. Spectroscopy supplement: Applications of ICP & ICP-MS Techniques for Today’s Spectroscopists, 2012, 27 (s11), pp 30–35.
This sponsor report is the responsibility of Milestone.
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Br. J. Anal. Chem., 2019, 6 (23)
1st Edition of the “Young Talent in Analytical Chemistry” award takes place at the 6th Analitica Latin America Congress The Brazilian Journal of Analytical Chemistry created the “Young Talent in Analytical Chemistry” award to recognize outstanding researchers in Analytical Chemistry who are younger than 35 years old at the award date. With great pleasure, BrJAC announces the researcher selected to receive the first edition of the “Young Talent in Analytical Chemistry” award, Prof. Dr. Leandro Wang Hantao. Leandro Wang Hantao has been an assistant professor in the Department of Analytical Chemistry of the Institute of Chemistry, University of Campinas (IQ-UNICAMP), since 2017. He has a degree in technological chemistry and earned a master’s in analytical chemistry and a doctorate in science at UNICAMP, under the guidance of Prof. Dr. Fabio Augusto. During his doctoral period, Dr. Hantao took up internship at the University of Toledo (OH, USA) under Prof. Dr. Jared Anderson. Dr. Hantao worked as a collaborative researcher under Prof. Dr. Ronei Poppi of IQ-UNICAMP. Between 2015 and 2017, he was a researcher at the Brazilian Center for Research in Energy and Materials (CNPEM) at the Brazilian Nanotechnology National Laboratory (LNNano). His research at IQ-UNICAMP is devoted to the development of advanced analytical techniques (chromatographic and mass spectrometric) for evaluating complex matrices, especially petrochemical samples. Dr. Hantao has previously received the following awards and recognitions: the John Phillips Award for his outstanding contributions to the field of GC×GC (2019); Brazilian Meeting of Analytical Chemistry (ENQA) — Best Work in Chemometrics (2018); Powerlist Top 40 under 40, The Analytical Scientist (2018); Cover Page, Analytica Chimica Acta Volume 1040 Issue 1021, Elsevier (2018); Top 10 Advances in Chromatography, Mass Spectrometry, and Lab Automation, Chemical and Engineering News, American Chemical Society (2015); EXTECH Best Poster Award, Royal Society of Chemistry, Separation Science Group (2013); COLACRO Best Poster Award, COLACRO XIV — Latin American Congress of Chromatography and Related Techniques (2012). The ceremony to award Dr. Hantao will take place on September 26 at 4:00 pm, during the 6th Analitica Latin America Congress that will be held from September 24 to 26, 2019, in São Paulo, SP, Brazil. Know more BrJAC will offer the "Young Talent in Analytical Chemistry" award annually at events related to analytical chemistry, such as the ENQA and the Analitica Latin America Congress, which take place in alternate years. The award will consist of a merit recognition diploma and a registration voucher for the national event on analytical chemistry (ENQA or Analitica Latin America Congress) subsequent to the award. The choice of the researcher to be awarded will be made by a committee designated by the Editor-in-Chief of BrJAC.
75
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Br. J. Anal. Chem., 2019, 6 (23)
Don’t miss the largest meeting of Analytical Chemistry in Latin America
6th Analitica Latin America Congress September 24-26, 2019 São Paulo Expo, São Paulo, SP, Brazil The goal of the Analitica Latin America Congress (ALAC) is the integration of professionals from both the academic and industrial sectors. To assist in achieving this idealistic purpose of integration, BrJAC was launched soon after the 1st edition of ALAC, in 2010, with an Editorial Board of professionals from the analytical chemistry field in academia, private companies and public institutions. Since then, the BrJAC has maintained a close partnership with ALAC and provides a scientific platform to researchers involved in science, technology and innovation projects on analytical chemistry to disseminate their studies developed at universities, research centers and in industry. The 6th ALAC will include Innovation in Analytical Chemistry, Artificial Intelligence, Blockchain, Big Data, Nanotechnology, Forensics, and Startup Labs as the main themes. In addition to lectures and round-tables, the ALAC has specific sessions for the discussion of posters, and technical talks on new technologies in analytical instrumentation and new procedures in the lab. A highlight of this event is the Live Lab, a laboratory fully equipped with instruments to carry out realtime analysis demonstrating the usability of state-of-the-art equipment. In the Live Lab of the 6th ALAC, analysis using GC-MS, LC-MS, HPLC, IC, ICP, UV and other analytical techniques will be performed.
Log in to the BrJAC website and have 10% discount on congress registration! After login, register at the Analytical Congress here
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Br. J. Anal. Chem., 2019, 6 (23)
Thermo Scientific Exactive GC Orbitrap GC-MS System The Frontier of Routine GC-MS The power of multi-award winning Orbitrap GC-MS technology has so far allowed research scientists to break new ground in gaining a broader and deeper understanding of their samples through the use of high-resolution, accurate-mass (HR/AM) analysis. The introduction of the Thermo Scientific™ Exactive™ GC Orbitrap™ GC-MS system brings that power into the routine environment for the first time. The Exactive GC system, in combination with the Thermo Scientific™ TraceFinder™ software, delivers robust and sensitive performance for routine pesticide analysis in food samples to regulatory standards. The full scan acquisition permits efficient targeted data processing by use of a compound database and has the capability to easily add further analytes into the method scope. Intelligent software allows for results to be reviewed and detections confirmed in an efficient manner. Consistent sub-ppm mass accuracy is achieved for all compounds over a wide concentration range, ensuring that compounds are detected with high confidence at low and high concentration levels. The system is able to maintain a consistent level of performance over an extended period of time as is demanded by the routine testing labs in agribusiness. Performance benefits • Resolving power of up to 50,000 (FWHM) at m/z 272 • Routine sub ppm mass accuracy • < 6 fg OFN Instrument Detection Limit • EI/CI Thermo Scientific ExtractaBrite ion source removable under vacuum through vacuum interlock • Vent-free column exchange with source plug Some Hardware Specifications Ion Source • Thermo Scientific ExtractaBrite Electron Ionization (EI) source • Ion source includes ion volume, repeller, source lenses, RF lens and dual filaments in all ionization modes, programmable from 50 °C to 350 °C • Chemical Ionization (CI) source for acquisition with Positive Ion Chemical Ionization (PCI) and Negative Ion Chemical Ionization (NCI) • Remove entire ion source or change to CI source in under 2 minutes without venting • Vent-free column exchange with new, patented source plug • Combination EI/PCI/NCI ion volume can be used without need for source interchange MS Ion Optics • Advanced pre-filtering and axial field bent flatapole ion guide reduces noise by eliminating neutrals. Orbitrap Mass Analyzer • Nitrogen-filled C-Trap • Highly efficient ion transfer to Orbitrap mass analyzer • Low-noise image current preamplifier • 16-bit signal digitalization 78
Redefine your GC-MS Analysis A comprehensive understanding of samples has been out of reach for GC-MS users for too long. The Thermo Scientific™ Q Exactive™ GC Orbitrap™ GC-MS/MS system and the new Thermo Scientific™ Exactive™ GC Orbitrap™ GC-MS system have changed all of that. The Q Exactive GC Orbitrap GC-MS/MS system is here with the superior resolving power, mass accuracy and ™ Orbritrap™ technology can deliver. And the Exactive GC Orbitrap GC-MS sensitivity that only Thermo Scientific system brings the power of high-resolution, accurate-mass (HR/AM) analysis into the routine environment for the first time. Both systems allow scientists working in the fields of food safety, environmental, industrial, forensics and anti-doping to revolutionize their workflows by taking their analytical capability to the next level.
VIDEO
Find out more at thermofisher.com/OrbitrapGCMS ©2016 Thermo Fisher Scientific Inc. All rights reserved. All trademarks are the property of Thermo Fisher Scientific and its subsidiaries unless otherwise specified. AD10525-EN 0616S
WEBSITE
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Thermo Scientific iCAP RQ ICP-MS
Simplicity, productivity and robustness for routine labs in agribusiness
This innovative single quadrupole (SQ) ICP-MS is the ideal trace elemental analyzer for a wide range of several agricultural samples. The Thermo Scientific™ iCAP™ RQ ICP-MS is particularly suited to the analysis of food, simultaneously determining trace level contaminants and macro level nutrients. This system provides a simple, robust, multielemental analysis method for major and minor concentrations of elements in food. The simultaneously determination of all elements of interest in a wide range of food samples can be done efficiently and quickly by the Thermo Scientific™ iCAP™ RQ ICP-MS.
DESCRIPTION Simplicity guarantees user-friendly operation • Quick connect sample introduction system • Easy access cones via bench-height drop-down door • Open geometry architecture for easy peripheral connection • Intuitive platform software for seamless workflows Productivity delivers more analysis in less time • Less training for software and hardware • Compatibility with automation and sample handling systems • Comprehensive interference removal in single measurement mode • Reduced drift and operator intervention Robustness ensures high up-time and low maintenance • Improved matrix tolerance interface • All new state-of-the-art electronics • New sturdy design RF generator • Reliable hot and cold plasma operation Comprehensive interference removal assures data accuracy, while our innovative helium Kinetic Energy Discrimination (He KED) technology enables measurement of all analytes in a single mode. Our highly effective QCell collision/reaction cell, combined with unique flatapole design reduces BECs even further than He KED alone, through the clever, dynamic application of low mass cut off (LMCO). Intuitive Thermo Scientific™ Qtegra™ Intelligent Scientific Data Solution™ (ISDS) software delivers all the support features essential to any lab, while containing all the flexibility needed to achieve the most challenging applications.
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Milestone ultraWAVE is the Benchmark in Microwave Digestion The Milestone ultraWAVE isn’t just an evolution; it’s a revolution - changing how industrial and research laboratories around the world prep samples for analysis. Milestone patented ultraWAVE Single Reaction Chamber (SRC) technology transcends traditional closed and open vessel digestion, offering significantly greater digestion capabilities for even the most difficult sample types. High-performance stainless-steel construction allows for higher pressures and temperatures, while disposable vessels eliminate the need to assemble, disassemble or clean between processing. Just as important, dissimilar samples can be processed simultaneously, saving time and money. Enhanced Efficiency in Sample Preparation for Metals Analysis The SRC technology achieves extraordinary performance capabilities combining microwave heating with a high-pressure reactor which acts simultaneously as microwave cavity and vessel. The ultraWAVE is easy to use, cost-effective, quick to adopt, and fast to implement. The ultraWAVE has already transformed and enhanced the way analytical chemists prepare their samples for trace metal analysis in hundreds of laboratories all over the world. The ultraWAVE represents the state-of- theart in microwave sample preparation, overcoming the limitations of the conventional digestion systems. One Method for All Samples Any combination of sample types (food, environmental, polymer, cosmetic, pharmaceutical, geological, chemical, and petrochemical) can be digested simultaneously. No method development is needed, as the same method can be used for nearly any sample. For the first time, blanks and reference standards of any matrix can be digested alongside samples. The ultraWAVE dramatically improves the lab workflow, as it allows to run any matrices simultaneously in a single digestion cycle. Unrivalled Performance for Superior Digestion Quality Operating up to 199 bar and 300 °C, the ultraWAVE enables the complete digestion of extremely difficult samples and large amounts of organics. Unlike conventional microwave digestion systems, every sample is under direct temperature and pressure control, so there is no need to rely on a reference vessel or indirect control such as infrared temperature sensors. The ultraWAVE reaches high temperatures faster, cools faster (10 min from 200°C to room temperature), and is capable of higher pressure and temperature than any other system, expanding the digestion efficiency. The ultraWAVE does not suffer any cross contamination between samples. Blanks are significantly lower than with conventional microwaves since less acid is used, and vials have a much less surface in contact with the analytical solution. Green Digestion The ultraWAVE allows for the complete digestion of organic samples with diluted nitric acid only with clear benefits for the subsequent analytical step and for the environment. High Productivity High sample throughput and quick turnaround time are top priorities in most analytical laboratories, along with high quality of the analysis and low running costs. The Milestone ultraWAVE fully matches these requirements. 82
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Pittcon Conference & Expo Be Amongst the best in PITTCON 2020... The Future of Laboratory Sciences
PITTCON is the Worldâ&#x20AC;&#x2122;s Leading Annual Conference and Exposition on Laboratory Science Pittcon attracts 16,000 attendees from industry, academia and government from over 90 countries worldwide. From laboratory scientists, academicians to researchers in molecular and biological sciences, the PITTCON, a non-profit organization has been a pioneer in providing educational and scientific assistance to individuals who wish to carve a niche for themselves in this world of constant change to excel and provide best services. PITTCON not only covers analytical chemistry and spectroscopy, but also showcases developments made in the field of food safety, environmental sciences, bioterrorism and pharmaceutical industry. Established since 1950, PITTCON works in collaboration with Spectroscopy Society of Pittsburgh (SSP) and the Society of Analytical Chemists of Pittsburgh (SACP) to help in the development, research and future excellence of science education and its implementation for providing best medical assistance. Pittcon, a vital resource for knowledge, happens yearly to help keep you informed of, connected to and up-to-date on these significant ongoing findings and new instrumentation.
For more information, please visit https://pittcon.org
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SelectScience® Pioneers online Communication and Promotes Scientific Success since 1998
Working with Scientists to Make the Future Healthier SelectScience® promotes scientists and their work, accelerating the communication of successful science. SelectScience® informs scientists about the best products and applications through online peer-to-peer information and product reviews. Scientists can make better decisions using independent, expert information and gain easy access to manufacturers. SelectScience® informs the global community through Editorial, Q&A and Application Articles, Featured Topics, Event Coverage, Video and Webinar programs.
Some recent contributions from SelecScience® to the scientific community Editorial Articles • Neonicotinoid Pesticides Found to Affect Bee Genes Researchers use new approach to investigate impact of neonicotinoid pesticides, clothianidin and imidacloprid, on bee gene expression and brain function. Read this full text here • Win Big with SelectScience by Sharing Your Opinion on Laboratory Products Most scientists write reviews on SelectScience to make a difference and help accelerate science, by sharing their experiences and knowledge with peers around the world. This ever-growing community of science communicators is what makes SelectScience the world’s leading source of opinion on laboratory products, technologies and services. Review laboratory equipment for your chance to win great prizes here The future is closer than you think For two decades, SelectScience has been publishing news and content from the front line of scientific advancement, improving communication between leading scientists and the biggest and best manufacturers across the globe, as we work towards one common goal - making the future healthier. In 2040 - where will we be – a disease-free humanity, producing super-foods, or even super-humans? We welcome you to explore what the future of science could like, meet the people making that happen, and discover how they intend to do it. Access “The Future of Science - How science could change your life by 2040” here
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SelectScienceÂŽ is the leading independent online publisher connecting scientists to the best laboratory products and applications. Access 2 Million+ Decision Makers
Working with Scientists to Make the FutWorking with Scientists to Make the Future Healthier. Informing scientists about the best products and applications. Connecting manufacturers with their customers to develop, promote and sell technologies.ure Healthier.
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CHROMacademy helps Increase your Knowledge, Efficiency and Productivity in the Lab
CHROMacademy is the world’s largest eLearning website for analytical scientists. With a vast library of high-quality animated and interactive eLearning topics, webcasts, tutorials, practical information and troubleshooting tools CHROMacademy helps you refresh your chromatography skills or learn something completely new. A subscription to CHROMacademy provides you with complete access to all content including: • Thousands of eLearning topics covering HPLC / GC / Sample Prep / Mass Spec / Infrared / Basic Lab Skills / Biochromatography Each channel contains e-Learning modules, webcasts, tutorials, tech tips, quick guides and interactive tools and certified assessments. With over 3,000 pages of content, CHROMacademy has something for everyone. • Video Training Courses Each course contains 4 x 1.5-hour video training sessions, released over 4 weeks, with full tutor support and certification. • Ask the Expert – 24-hour Chromatography Support A team of analytical experts are on hand to help fix your instrument and chromatographic problems, offer advice on method development & validation, column choice, data analysis and much more. • Assessments Test your knowledge, certificates awarded upon completion. • Full archive of Essential Guide Webcasts and Tutorials Over 70 training topics covered by industry experts. • Application Notes and LCGC Articles The latest application notes & LCGC articles. • Troubleshooting and Virtual Lab Tools Become the lab expert with our HPLC and GC Troubleshooters. • User Forum Communicate with others interested in analytical science. Lite members have access to less than 5% of CHROMacademy content. Premier members get so much more! For more information, please visit www.chromacademy.com/subscription.html 88
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Women in Science: The Federal University of Pelotas Discusses the Challenges and Perspectives of Including New Talent
Discussion is growing worldwide on the role of women in science and on the discrepancies in gender equity. Currently, women make up only 30 percent of the world’s scientific community. The Federal University of Pelotas will on 25 and 26 September 2019 hold an event to discuss the general panorama of women’s activities in science and encourage the inclusion of new talent. The event will consist of roundtables where prominent national and international professionals will address the main challenges and perspectives on the role of women in the labor market. In addition, there will be four lectures featuring representatives of governmental and scientific bodies, during which inclusive, interdisciplinary and dynamic themes will be addressed. Approximately 150 participants will attend including teachers and researchers, undergraduate and postgraduate students from various institutions, as well as other members of the community who are interested in the subject. Organizing committee: Prof. Dr. Márcia Foster Mesko
Associate Professor at the Center for Chemical, Pharmaceutical and Food Sciences (CCQFA) at the Federal University of Pelotas (UFPel) Coordinator of the Biochemistry and Bioprospecting Program (PPGBBio) at UFPel Award winner “For Women in Science 2012,” promoted by L’Oréal, the Brazilian Academy of Sciences, and United Nations Educational, Scientific and Cultural Organization.
Prof. Dr. Ethel Antunes Wilhelm
Associate Professor at CCQFA, UFPel Vice-Coordinator of the PPGBBio at UFPel Award winner “For Women in Science 2018,” promoted by L’Oréal, the Brazilian Academy of Sciences, and United Nations Educational, Scientific and Cultural Organization.
Dr. Priscila Tessmer Scaglioni
Postdoctoral researcher in the PPGBBio at UFPel Has experience in food analysis, food biochemistry, antioxidant and antifungal compounds, mycotoxins, and methods for food decontamination.
Register online at: https://wp.ufpel.edu.br/elasnaciencia/ 90
Notices of Books
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Whole Grains and their Bioactives - Composition and Health Jodee Johnson, Taylor C. Wallace - Editors March 2019. Publisher: John Wiley & Sons In this book the editors assemble a panel of leading experts to address a comprehensive examination of the cereal and pseudo-cereal grains and their most important bioactive compounds. Not only does this volume offer summaries of existing research, it also places these findings within the larger context of health promotion and disease prevention. Topics addressed include: methodical analyses of domesticated grain species, their horticultural history, nutritional composition, and known effects on health. Read more Analysis, Fate, and Toxicity of Engineered Nanomaterials in Plants - Volume 84 in the Comprehensive Analytical Chemistry Series Sandeep Kumar Verma, Ashok Kumar Das - Serial Volume Editors May 2019. Publisher: Elsevier This new volume highlights new advances in the field of engineered nanomaterials in plants presenting interesting chapters on the current status of environmental monitoring, application in soil and sediments, applications in ecology of animals and plants, applications in contamination, and much more. Read more Vibrational Spectroscopy for Plant Varieties and Cultivars Characterization Volume 80 in the Comprehensive Analytical Chemistry Series JoĂŁo Lopes, Clara Sousa - Serial Volume Editors May 2018. Publisher: Elsevier This volume provides an overview on the application of vibrational spectroscopy to characterize plant cultivars and varieties. It covers a variety of aspects, including the potential of this technique for taxonomic purposes (species and cultivars/varieties identification), how to discriminate plants according to their ages and geographic regions, how to depict soil properties through plant characteristics, etc. Currently, most of these studies are performed through somewhat laborious techniques. This book presents reliable alternatives to such techniques, while also systematizing information concerning the application of vibration spectroscopy in this context. Read more Analytical Methods for Food Safety by Mass Spectrometry - Volume II: Veterinary Drugs Guo-Fang Pang, Author June 2018. Publisher: Academic Press, Elsevier This book systematically introduces pesticide and veterinary drug multiresidues analytical methods that are capable of detecting over 200 veterinary drugs, chemical residues of 20 categories, and toxins in animal and poultry tissues, aquatic products, milk, milk powders and bee products. Read more
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Notices of Books
Alteration of Ovoproducts - From Metabolomics to Online Control Olivier Goncalves, Jack Legrand - Editors July 2018. Publisher: Elsevier This book focuses on the capabilities (potential or proven) of the latest metabolomics based analytical approaches for the (early) diagnostic of the alteration of ovoproducts during their production/preservation processes. The ovoproduct matrix, their known sources of biotic and abiotic alteration, and their associated biomarkers are detailed. In addition, the book covers the capabilities (exploratory and characterization) of the latest metabolomics technics, both invasive and noninvasive, including chromatography, nuclear magnetic resonance, mass spectrometry â&#x20AC;&#x201C; including FTICR-MS, and vibrational spectroscopy, such as Infrared, MIR, NIR or Raman. In final sections, the next generation of online sensors derived from the latest technics is discussed for their applicative potential in industry (NIR, Raman, chromatography, benchmark NMR, and more). Read more Analysis of Pesticide in Tea - Chromatography-Mass Spectrometry Methodology Guo-Fang Pang, Author August 2018. Imprint: Elsevier This is a comprehensive book, providing serial, rapid, high-throughput analytical methods for determining more than 600 pesticides in tea. There are increasing numbers of strict limit standards for pesticide residues in edible agricultural products in countries all over the world. The threshold for pesticide residues in tea is high for international trade. At present, 17 countries and international organizations have stipulated MRL levels for over 800 pesticide residues in tea. All methods described in this book are validated by an independent, U.S.-based organization (AOAC International), and all indexes have satisfied AOAC Internationalâ&#x20AC;&#x2122;s criteria. Read more Cellulose Science and Technology - Chemistry, Analysis, and Applications Thomas Rosenau, Antje Potthast, Johannes Hell - Editors November 2018. Publisher: Wiley This book addresses both classic concepts and state-of-the-art technologies surrounding cellulose science and technology. Integrating nanoscience and applications in materials, energy, biotechnology, and more, the book appeals broadly to students and researchers. Includes contributions from leading cellulose scientists worldwide, with five Anselm Payen Cellulose Award winners and two Hayashi Jisuke Cellulose Award winners. Deals with a highly applicable and timely topic, considering the current activities in the fields of bioeconomies, biorefineries, and biomass utilization. Maximizes readership by combining fundamental science and application development. Read more
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Periodicals & Websites
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American Laboratory The American Laboratory® publication is a platform that provides comprehensive technology coverage for lab professionals at all stages of their careers. Unlike singlechannel publications, American Laboratory® is a multidisciplinary resource that engages scientists through print, digital, mobile, multimedia, and social channels to provide practical information and solutions for cutting-edge results. Addressing basic research, clinical diagnostics, drug discovery, environmental, food and beverage, forensics, and other markets, American Laboratory combines in-depth articles, news, and video to deliver the latest advances in their fields. Read more LCGC Chromatographyonline.com is the premier global resource for unbiased, peerreviewed technical information on the field of chromatography and the separation sciences. Combining all of the resources from the regional editions (LCGC North America, LCGC Europe, and LCGC Asia-Pacific) of award winning magazines, Chromatographyonline delivers practical, nuts-and-bolts information to help scientists and lab managers become more proficient in the use of chromatographic techniques and instrumentation, thereby making laboratories more productive and businesses around the world more successful. Read more Scientia Chromatographica Scientia Chromatographica is the first and to date the only Latin American scientific journal dedicated exclusively to Chromatographic and Related Techniques (Mass Spectrometry, Sample Preparation, Electrophoresis, etc.). With a highly qualified and internationally recognized Editorial Board, it covers all chromatography topics (HPLC, GC, SFC) in all their formats, in addition to discussing related topics such as “The Pillars of Chromatography”, Quality Management, Troubleshooting, Hyphenation (GC-MS, LC-MS, SPE-LC-MS/MS) and others. It also provides columns containing general information, such as: calendar, meeting report, bookstore, etc. Read more Select Science SelectScience® promotes scientists and their work, accelerating the communication of successful science. SelectScience® informs scientists about the best products and applications through online peer-to-peer information and product reviews. Scientists can make better decisions using independent, expert information and gain easy access to manufacturers. SelectScience® informs the global community through Editorial, Features, Video and Webinar programs. Read more Spectroscopy Spectroscopy’s mission is to enhance productivity, efficiency, and the overall value of spectroscopic instruments and methods as a practical analytical technology across a variety of fields. Scientists, technicians, and laboratory managers gain proficiency and competitive advantage for the real-world issues they face through unbiased, peer-reviewed technical articles, trusted troubleshooting advice, and best-practice application solutions. Read more
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Events
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2019 May 12 – 18 43rd International Symposium on Capillary Chromatography & 16th GCxGC Symposium Hilton Fort Worth, Fort Worth, TX, USA https://www.isccgcxgc.com/ May 27 - 30 42nd Annual Meeting of the Brazilian Chemical Society (42nd RASBQ) Centro de Convenções Expoville, Joinville, SC, Brazil http://www.sbq.org.br/reunioes-anuais June 16 - 20 48th International Symposium on High-Performance Liquid Phase Separations and Related Techniques (HPLC 2019) University of Milano-Bicocca, Milan, Italy https://www.hplc2019-milan.org/ July 7 - 12 IUPAC 47th World Chemistry Congress Palais des Congrès, Paris, FR www.iupac2019.org July 14 - 19 Latin American Congress on Chromatography and Related Techniques (COLACRO XVII) Unit – Universidade Tiradentes, Aracaju, SE, Brazil https://www.colacro2019.com September 1 - 5 Brazilian Symposium of Electrochemistry and Electroanalysis - XXII SIBEE Convention Center Ribeirão Preto, Ribeirão Preto, SP, Brazil www.xxiisibee.com.br September 1 - 5 Euroanalysis XX - Europe’s Analytical Chemistry Meeting Istanbul, Turkey http://euroanalysis2019.com/ September 3 - 5 Brazilian Meeting on Chemical Speciation - EspeQBrasil-2019 Universidade Federal da Bahia, Campus de Ondina, Salvador, BA, Brazil http://www.espeqbrasil2019.ufba.br/ September 8 - 11 133rd AOAC Annual Meeting & Exposition Sheraton Denver Downtown Hotel, Denver, CO, USA http://www.aoac.org
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Events
2019 September 24 - 26 15th Analitica Latin America Expo & 6th Analitica Congress Centro de Exposições São Paulo Expo, São Paulo, SP, Brazil https://www.analiticanet.com.br/en September 25 - 26 Women in Science: The Federal University of Pelotas discussing the challenges and perspectives for the inclusion of new talents Pelotense Public Library (opening ceremony) and the Auditorium of the UFPel Arts Center, Pelotas, RS, Brazil https://wp.ufpel.edu.br/elasnaciencia/ October 28 - 31 XXI Brazilian Congress of Toxicology & XV The International Association of Forensic Toxicologists (TIAFT) Latin-American Regional Meeting Águas de Lindóia, SP, Brazil http://www.cbtox-tiaft.org/ November 5 - 8 9th International Symposium on Recent Advances in Food Analysis - RAFA 2019 Prague, Czech Republic http://www.rafa2019.eu November 6 - 8 XIV Latin American Symposium on Environmental Analytical Chemistry (LASEAC) & IX National Meeting on Environmental Chemistry (ENQAmb) Bento Gonçalves, RS, Brazil http://www.laseac2019.furg.br
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Guidelines for Authors
Scope The Brazilian Journal of Analytical Chemistry (BrJAC) is dedicated to the diffusion of significant and original knowledge in all branches of Analytical Chemistry. BrJAC is addressed to professionals involved in science, technology and innovation projects in Analytical Chemistry, at universities, research centers and in industry. Professional Ethics Manuscripts submitted for publication in BrJAC cannot have been previously published or be currently submitted for publication in another journal. BrJAC publishes original, unpublished scientific articles and technical notes that are peer reviewed in the double-blind way. Review process BrJAC’s review process begins with an initial screening of the manuscripts by the editor-in-chief, who evaluates the adequacy of the study to the journal scope. Manuscripts accepted in this screening are then forwarded to at least two reviewers who are experts in the related field. As evaluation criteria, the reviewers employ originality, scientific quality, contribution to knowledge in the field of Analytical Chemistry, the theoretical foundation and bibliography, the presentation of relevant and consistent results, compliance to the BrJAC’s guidelines, and the clarity of writing and presentation. BrJAC is a quarterly journal that, in addition to scientific articles and technical notes, also publishes reviews, interviews, points of view, letters, sponsor reports, and features related to analytical chemistry. Brief description of the documents that can be submitted by the authors • Articles: Full descriptions of an original research finding in Analytical Chemistry. Articles undergo double-blind full peer review. • Reviews: Articles on well-established subjects, including a critical analysis of the bibliographic references and conclusions. Manuscripts submitted for publication as reviews must be original and unpublished. Reviews undergo double-blind full peer review. • Technical Notes: Concise descriptions of a development in analytical method, new technique, procedure or equipment falling within the scope of BrJAC. Technical notes also undergo doubleblind full peer review. The title of the manuscript submitted for technical note must be preceded by the words “Technical note”. • Letters: Discussions, comments, suggestions on issues related to Analytical Chemistry, and consultations to authors. Letters are welcome and will be published at the discretion of the BrJAC editor-in-chief. • Points of view: The expression of a personal opinion on some relevant subject in Analytical Chemistry. Points of View are welcome and will be published at the discretion of the BrJAC editor-in-chief. • Releases: Articles providing new and relevant information for the community involved in Analytical Chemistry. Download a template here Path: Log In / Manuscript Submission / Online Submission
Manuscript (MS) preparation • Language: English is the language adopted by BrJAC. • Required items: the MS must include a title, a graphical abstract, an abstract, keywords, and the following sections: Introduction, Materials and Methods, Results and Discussion, Conclusion, and References. • Identification of authors: the MS must NOT contain the authors’ names nor affiliations. This information must be in the cover letter to the editor-in-chief. This rule is necessary because the MS is subjected to double-blind review. • Layout: the lines in the MS must be numbered consecutively and double-spaced throughout the text. 96
Guidelines
• Graphics and Tables: must appear close to the discussion about them in the text. For figures use Arabic numbers, and for tables use Roman numbers. • Permission to use content already published: for figures, graphs, diagrams, tables, etc. identical to others previously published in the literature, the author must ask for publication permission from the company or scientific society holding the copyrights, and send this permission to the BrJAC editor-in-chief with the final version of the manuscript. • Chemical nomenclature: should conform to the rules of the International Union of Pure and Applied Chemistry (IUPAC) and Chemical Abstracts Service. It is recommended that, whenever possible, authors follow the International System of Units, the International Vocabulary of Metrology (VIM) and the NIST General Table of Units of Measurement. Abbreviations are not recommended except those recognized by the International Bureau of Weights and Measures or those recorded and established in scientific publications. • References: must be cited by numbers in square brackets. It is recommended that references older than 5 (five) years be avoided, except in relevant cases. Include references that are accessible to readers. References should be thoroughly checked for errors by the authors before submission. See how to format the references in the following item. Examples of reference formatting Journals 1. Orlando, R. M.; Nascentes, C. C.; Botelho, B. G.; Moreira, J. S.; Costa, K. A.; Boratto, V. H. M. Anal. Chem. 2019, 91 (10), pp 6471-6478 (https://doi.org/10.1021/acs.analchem.8b04943).
• Publications with more than 10 authors, list the first 10 authors followed by a semicolon and et al. • Titles of journals must be abbreviated as defined by the Chemical Abstracts Service Source Index (http:// cassi.cas.org/search.jsp).
Electronic journals 2. Sapozhnikova, Y.; Hoh, E. LCGC North Am. 2019, 37 (1), pp 52-65. Available from: http:// www.chromatographyonline.com/suspect-screening-chemicals-food-packaging-plastic-filmcomprehensive-two-dimensional-gas-chromatogr [Accessed 20 January 2019]. Books 3. Burgot, J.-L. Ionic Equilibria in Analytical Chemistry. Springer Science & Business Media, New York, 2012, Chapter 11, p 181. 4. Griffiths, W. J.; Ogundare, M.; Meljon, A.; Wang, Y. Mass Spectrometry for Steroid Analysis. In: Mike, S. L. (Ed.). Mass Spectrometry Handbook, v. 7 of Wiley Series on Pharmaceutical Science and Biotechnology: Practices, Applications and Methods. John Wiley & Sons, Hoboken, N.J., 2012, pp 297-338. Standard methods 5. International Organization for Standardization. ISO 26603. Plastics — Aromatic isocyanates for use in the production of polyurethanes — Determination of total chlorine. Geneva, CH: ISO, 2017. Master’s and doctoral theses or other academic literature 6. Dantas, W. F. C. Application of multivariate curve resolution methods and optical spectroscopy in forensic and photochemical analysis. Doctoral thesis, 2019, Institute of Chemistry, University of Campinas, Campinas, SP, Brazil. Patents 7. Trygve, R.; Perelman, G. US 9053915 B2, June 9, 2015, Agilent Technologies Inc., Santa Clara, CA, US. 97
Guidelines
Web pages 8. http://www.chromedia.org/chromedia [Accessed 10 January 2019]. Unpublished source 9. Viner, R.; Horn, D. M.; Damoc, E.; Konijnenberg, A. Integrative Structural Proteomics Analysis of the 20S Proteasome Complex (WP-25). Poster presented at the XXII International Mass Spectrometry Conference (IMSC 2018) / August 26-31, 2018, Florence, IT. 10. Author, A. A. J. Braz. Chem. Soc., in press. 11. Author, B. B., 2017, submitted for publication. 12. Author, C. C., 2018, unpublished manuscript. Note: Unpublished results may be mentioned only with express authorization of the author(s). Personal communications can be accepted exceptionally.
Download templates here Path: Log In / Manuscript Submission / Online Submission Manuscript submission Three different files, as described below, must be sent online through the website www.brjac.com.br 1. A Cover Letter (PDF file): addressed to the editor-in-chief, with the manuscript title, the full names of the authors and their affiliations, and the complete contact information of the corresponding author, including the ORCID iD. This letter should also inform to which section of the BrJAC the manuscript is being submitted (e.g. Article, Review, Technical Note, Point of View or Letter). The cover letter should also contain a statement that the article has not been previously published and is not under consideration for publication elsewhere. 2. The manuscript PDF file that must NOT mention the names of the authors nor their affiliations. 3. A similarities analysis report on the manuscript obtained through an anti-plagiarism software. (BrJAC indicates CopySpiderŠ freeware to support similarities checking analyzes. Download the CopySpider freeware at: www.copyspider.com.br
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