Application of texture image analysis for the classification of bovine meat

Texture analysis has been used to classify photographic images of meat slic es. Among the multiple muscular tissue characteristics that influence meat quality, the connective tissue content and spatial distribution, which defi ne the grain of meat, are of great importance because they are directly rel ated to its tenderness. Connective tissue contains two important components , fat and collagen, which are variable with muscles, breed and also with ag e. These components are clearly visible on photographic images. Fat and col lagen are particularly emphasised by ultraviolet light. The meat slices ana lysed came from 26 animals raised at INRA of Their by the LCMH Laboratory. Three different muscles were selected and cut off from carcasses of animals of different breeds and of different ages. The biological factors (muscle type, age and breed) directly influence the structure and composition of th e muscle samples. The image analysis led to a representation of each meat s ample with a 58 features vector. Classification experiments were performed to identify the samples according to the three variation factors. This stud y shows the potential of image analysis for meat sample recognition. The co rrelation of the textural features with chemical and mechanical parameters measured on the meat samples was also examined. Regression experiments show ed that textural features have potential to indicate meat characteristics. (C) 2000 Elsevier Science Ltd. All rights reserved.