Hyperspectral imaging of ribeye muscle on hanging beef carcasses for tenderness assessment

Abstract

A prototype hyperspectral image acquisition system (λ=400–1000nm) was developed to acquire images of exposed ribeye muscle on hanging beef carcasses in commercial beef packing or slaughter plants and to classify beef based on tenderness. Hyperspectral images (n=338) of ribeye muscle on hanging beef carcasses of 2-day postmortem were acquired in two regional beef packing plants in the U.S. After image acquisition, a strip steak was cut from each carcass, vacuum packaged, aged for 14days, cooked, and slice shear force values were collected as a measure of tenderness. Different hyperspectral image features namely descriptive statistical features, wavelet features, gray level co-occurrence matrix features, Gabor features, Laws’ texture features, and local binary pattern features, were extracted after reducing the spectral dimension of the images using principal component analysis. The features extracted from the 2-day images were used to develop tenderness classification models for forecasting the 14-day beef tenderness. Evaluation metrics such as tender certification accuracy, overall accuracy, and a custom defined metric called accuracy index were used to compare the tenderness classification models. Based on a third-party true validation with 174 samples, the model developed with the gray level co-occurrence matrix features outperformed the other models and achieved a tenderness certification accuracy of 87.6%, overall accuracy of 59.2%, and an accuracy index of 62.9%. The prototype hyperspectral image acquisition system developed in this study shows promise in classifying beef based on tenderness.