lucassen

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

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The color question

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From a physical point of view

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From a biological point of view

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

AIC 2010

Johan Alferdinck

Ron van Megen

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From the farm to the table

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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

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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

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

Meat

Color scale

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

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Past: instrumental classification

Certified personnel perform on-line color measurements in the slaughterhouses

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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

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Past: instrumental classification Measured CIE X,Y,Z

Conversion algorithm

Color class 1..10

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• 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

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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

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Present: update hardware & software

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

n=113,556 n= 11,745

Restricted area in CIELAB color space

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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

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

Relative Frequency (%)

20 old algorithm

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new algorithm 10 5 0 1

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Color class

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

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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

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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

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

AIC 2010

Johan Alferdinck

Ron van Megen

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