The use of milk replacers to feed suckling kids could affect the shelf life and appearance of themeat. Leg chops were evaluated by consumers and the instrumental colorwas measured. A machine learning algorithmwas used to relate them. The aim of this experimentwas to study the shelf life of the meat of kids reared with dam’smilk or milk replacers and to ascertain which illuminant and instrumental color variables are used by consumers as criteria to evaluate that visual appraisal.
RESULTS: Meat from kids reared with milk replacers was more valuable and had a longer shelf life than meat from kids reared with natural milk. Consumers used the color of the whole surface of the leg chop to assess the appearance of meat. Lightness and hue angle were the prime cues used to evaluate the appearance of meat.
CONCLUSION: Illuminant D65 was more useful for relating the visual appraisal with the instrumental color using a machine learning algorithm. The machine learning algorithms showed that the underlying rules used by consumers to evaluate the appearance of suckling kid meat are not at all linear and can be computationally schematized into a simple algorithm.