Page 1

AIC 2010 Color and Food, Mar del Plata, Argentina, 12-15 October 2010

Color classification of veal carcasses: Past, present and future Marcel Lucassen

AIC 2010

Johan Alferdinck

Ron van Megen

1


The color question

+

AIC 2010 AIC 2010

=?

2 2


From a physical point of view

AIC 2010 AIC 2010

3 3


From a biological point of view

AIC 2010 AIC 2010

4 4


AIC 2010 AIC 2010

5 5


Color classification of veal carcasses: Past, present and future Marcel Lucassen

AIC 2010

Johan Alferdinck

Ron van Megen

6


From the farm to the table

AIC 2010 AIC 2010

7 7


Carcass classification Veal carcasses are classified on • Fatness (amount of fat tissue) • Conformation (size and weight) • Color Color is an important factor for pricing The color classification process is reviewed here

AIC 2010 AIC 2010

8 8


Past: visual classification

• Meat color was visually matched to a 10-point scale • Scale design based on representative variations in meat color AIC 2010 AIC 2010

9 9


Past: visual classification • Visual task: determine the smallest difference

Meat

Color scale

• Disadvantages: - Subjective - Dependent on illumination AIC 2010 AIC 2010

1010


Past: instrumental classification

Certified personnel perform on-line color measurements in the slaughterhouses

AIC 2010 AIC 2010

1111


Past: instrumental classification • Handheld Minolta CR300 (tristimulus meter) • Positioned on the m. Rectus abdominis (vinkelap) • Measurement of CIE X,Y,Z • 45 min post mortem AIC 2010 AIC 2010

1212


Past: instrumental classification Measured CIE X,Y,Z

Conversion algorithm

Color class 1..10

AIC 2010 AIC 2010

• Algorithm derived from database with both visual and instrumental measurements • Discriminant analysis: calculates the most likely color class using functions based on measured L* and a* values 1313


Past: instrumental classification

> 80% within 1 color class difference AIC 2010 AIC 2010

1414


Present: update hardware & software • Tristimulus meters replaced by newer version

Minolta CR300 AIC 2010 AIC 2010

Minolta CR400 1515


Present: update hardware & software • Improved calibration procedure (user calibration), using additional tile with representative target color

AIC 2010 AIC 2010

1616


Present: update hardware & software

AIC 2010 AIC 2010

1717


Present: update hardware & software • New datasets: instrumental only instrumental + visual

n=113,556 n= 11,745

Restricted area in CIELAB color space

AIC 2010 AIC 2010

1818


Present: update hardware & software • New algorithm to convert color measurement to a color class, based on ∆E94 color difference metric • Finds minimum color distance to new, virtual color scale

∆E=12.7 ∆E=5.6

∆E=0.8

∆E=7.6

∆E=14.9

Meat

Virtual color scale

AIC 2010 AIC 2010

1919


Present: update hardware & software • New algorithm is attuned to historical databases 35 30 25

Relative Frequency (%)

20 old algorithm

15

new algorithm 10 5 0 1

2

3

4

5

6

7

8

9

10

Color class

AIC 2010 AIC 2010

2020


Present: update hardware & software • Good agreement with visual data

AIC 2010 AIC 2010

2121


Main advantages ∆E-based method 1. Works similiar to visual classification: it determines the smallest difference to reference colors 2. Easy to explain and comprehend 3. Does not require complex statistical analysis 4. Less sensitive to small variations in color measurements 5. ∆E is an international / industrial standard

AIC 2010 AIC 2010

2222


Future perspective • LED illumination in color measurement equipment: longer life-time, less calibration efforts • Operational research: local factors (temperature, humidity, animal stress, etc) affecting the color measurement? • Camera based, non-contact color measurement

AIC 2010 AIC 2010

2323


Color classification of veal carcasses: Past, present and future Marcel Lucassen

AIC 2010

Johan Alferdinck

Ron van Megen

24

lucassen  

Marcel Lucassen, Johan Alferdinck, Ron van Megen (Netherlands): Color classification of veal carcasses: Past, present and future

Read more
Read more
Similar to
Popular now
Just for you