new simple method for estimating the pork carcass mass of primal cuts and lean meat content of the carcass

• Published in: Animal production science Vol. 55; no. 8; pp. 1044 - 1050
• Main Authors: Lisiak, Dariusz, Duziński, Kamil, Janiszewski, Piotr, Borzuta, Karol, Knecht, Damian
• Format: Journal Article
• Language: English
• Published: CSIRO Publishing 01.01.2015
• Subjects: backfat; carcass weight; equations; ham; lean meat; longissimus muscle; pig carcasses; spinal cord; swine; waist
• ISSN: 1836-0939; 1836-5787
• DOI: 10.1071/AN13534

The aim of this study was to develop regression equations for estimating lean meat content and the mass of primal cuts (ham, loin, shoulder, belly) based on selected linear measurements. The experiment involved a classification of 141 pigs from the Polish commercial pig population, with hot carcass weight ranging between 60 and 120 kg. The study population was characterised by high variability in terms of analysed measurements. Eight measurements were made including: mass of half-carcass, backfat thickness at different points (over shoulder, over last rib, over the middle of M. gluteus medius), width and thickness of the M. longissimus dorsi measured over the last rib, thickness of the lumbar and the gluteal muscle layer located between the spinal cord and beginning of the M. gluteus medius and waist width – the width of the carcass measured at the narrowest point of the lumbar. A subjective five-point scale was used to score difficulties in obtaining linear measurements (workload rate). The lean meat percentage and mass of cuts were determined by dissection. The study enabled equations to be devised for estimating lean meat content with an accuracy greater than most devices used for carcass classification (estimation error 1.67). Regression coefficients for the mass of primal cuts were: 0.92 for ham, 0.87 for loin, 0.87 for shoulder, and 0.74 for belly. The error of equations used to estimate the mass of primal cuts were: 391 g for ham, 447 g for loin, 263 g for shoulder and 257 g for belly. The workload rate for all the developed regression equations ranged from 1.3 to 1.6 points. The outcome of this study was the development of equations to predict carcass value without the need to use expensive classification equipment.