Estimation of lean meat percentage in pig carcass with the use of objective methods with regard to sex

Inthe Czech Republic, the pig carcass classification ismandatory inslaughterhouses processing over 200 pigs weekly. Asbreeding practices evolve toenhance lean meat yield, itisessential toupdate regression equations used inclassification systems. This study presents new regression models for the Fat-O-MeaterII (FOMII) device, using computed tomography (CT) asthe reference method. Separate equations were developed for barrows, gilts, and boars toimprove the accuracy oflean meat percentage (LMC) estimation. To calibrate the CT method, 24carcasses were selected across arange ofbackfat thicknesses and sexes. CT scans were performed onchilled left carcass halves, followed bymanual dissection todetermine the true LMC. Acorrection model was applied toalign the CT-derived LMC with dissection results. Subsequently, 128 carcasses were measured using FOMII and CT todevelop sex-specific regression equations using ordinary least squares. The models revealed sex-specific differences inprediction accuracy. Gilts achieved anR²of0.66 and RMSEP of1.35; barrows had higher R² (0.759) and greater RMSEP (1.46); boars showed the most consistent composition (R²=0.734, RMSEP=1.14). Compared tothe standard method, gilts and boars had slightly higher LMC (+0.03% and +0.82%), while barrows had lower LMC (–0.14%). These differences translated into economic impacts, with gains ofCZK1.23 and CZK4.33 per gilt and boar carcass, respectively, and aloss ofCZK5.55 per barrow carcass. These results support the formulated hypotheses, and the fact that sex-specific calibration enhances classification accuracy and economic efficiency.