Image Analysis Prediction of Beef Carcass Composition from the Carcass Section

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The 5-6th rib cross section of beef carcasses was analyzed by the Image Analyzer. The measurements useful-for the prediction of carcass composition were evaluated. The measurements were area, circular length, long and short axis length and the center of gravity of the total cross section, several muscles and fat area. The repeatability and coefficient of variation of measures repeated twice by the I'mage Analyzer were used as the index of precision of the method. Stepwise regression analysis was used to choose the best regression equation to predict carcass composition as total kilograms and percentages of lean, fat and bone.1. The correlations between actual area done by tracing and those done by the Image Analyzer were from +. 88 to +. 98. The repeatabilities ranged from +. 89 to +. 99. The most important variable to predict the percentage of lean and that of fat was subcutaneous fat area percentage, while to predict total kilograms of-lean and fat was each tissue area. In predicting total kilograms of bone the distance between the centers of gravity of muscles was an important independent variable.2. The most important variable to prediet the percentage of lean in the Japanese Black was total area or fat area(cm^2), while that to predict the percentages of fat or bone was fat area percentage. Coefficents of determination adjusted for the degrees of freedom(R^2)by the regression-equations for the percentages of lean, fat and bone were 0.727, 0.864 and 0.905, -respectively. On the other hand, the most important variable to predict total kil(igrams of lean, fat arid bone was total area(cm^2). In predicting total kilograms of fat and bone, the distance between the centers of muscles was an important independent variable. The R^2 were as high as around 0.9.