Analysis of image-based measurements and USDA characteristics as predictors of beef lean yield

Abstract

This paper describes a comprehensive analysis of the 12th rib image measurements, USDA yield characteristics and USDA yield grade as predictors of beef lean yield. The predictors were used in separate groups to construct three sets of multiple linear regression models for the prediction of lean yield of 241 carcasses. Fat thickness is traditionally considered the most useful predictor. The analysis showed that the percent rib eye area was a more useful single predictor than fat thickness, and that the average fat thickness was insignificant when rib eye area and fat area were used. While marbling is traditionally considered a predictor of quality, the results showed that marbling characteristics were also useful for yield prediction. The usually-observed large differences in accuracy between the predictions of lean weight and lean percentage were shown to result from the variations in carcass weight. Statistical diagnostics confirmed the suitability of the models developed. A neural network model was tested and the results suggested that the inclusion of nonlinearity in the predictive models did not prove beneficial. Important predictors were identified and the advantages of computer vision and image processing techniques were further demonstrated.