Image texture features as indicators of beef tenderness

Image processing techniques were developed to predict cooked-beef tenderness from fresh-beef image characteristics. Cattle from different finishing treatments were processed in a commercial plant. Two short loin steaks were sampled from each carcass; one used for sensory evaluation and the other for imaging. The samples varied significantly in both US quality grades and sensory tenderness scores. Color, marbling and texture features were extracted from the beef images. Statistical and neural network analyses were performed to relate the image features to sensory tenderness scores. Image texture features were found to be useful indicators of beef tenderness. Partial least squares and neural network models were able to predict beef tenderness from color, marbling and image texture features to R(2)-values up to 0.70.