Prediction precision for lean meat percentage in Norwegian pig carcasses using ‘Hennessy grading probe 7’: Evaluation of methods emphasized at exploiting additional information from computed tomography

Abstract

ABSTRACTThe present study aims at improving the prediction of lean meat percentage (LMP) for pig carcasses based on on-line measurements from the slaughterhouses using the ‘Hennessy Grading Probe 7’ (HGP7) and auxiliary information such as gender and breed. The prediction performance is evaluated using an empirical Bayes method capable of utilizing information from a surrogate variable, that is, LMP from computed tomography. HGP7 measures thicknesses of fat and meat layers. The HGP7 measurements of subcutaneous fat, sirloin height and interior fat layer should be included as predictor variables together with gender. For efficiency at the slaughter-line gender might be omitted. The empirical Bayes method improved prediction precision only marginally compared with the standard ordinary least-squares method when applied to the full set of data. However, simulations show that the empirical Bayes method enables a considerable reduction of the data sample size without appreciable loss of prediction precision.