Classification of tough and tender beef by image texture analysis

Abstract

Texture features of fresh-beef images were extracted and used to classify steaks into tough and tender groups in terms of cooked-beef tenderness. Crossbred steers varying in quality were processed in a commercial plant and two short loin steaks were sampled from each carcass. One sample was used for imaging and the other was broiled for sensory evaluation of tenderness by a trained panel. The samples were segregated into tough and tender groups according to the sensory scores. A wavelet-based decomposition method was used to extract texture features of fresh-beef images. The texture feature data for 90 sample images were used to train and test sample calssifiers in a rotational leave-one-out scheme. A correct classification rate of 83.3% was obtained in cross validations. While texture features alone may not be sufficient to segregate beef products into many levels of tenderness, they can be significant members in a set of indicators that will lead to adequate tenderness prediction.