Classification of beef carcasses from Portugal using animal characteristics and pH/temperature decline descriptors.

Previous research showed that meat of optimal tenderness is produced when rigor mortis temperature falls between 12 - 35 °C. This study aimed to classify beef carcasses quality according to the ideal window rule using pH/temperature decay descriptors and animal characteristics. Seventy-four Mirandesa breed and 52 Crossbreds, with an average age of 10.1 ±â€¯2.32 months, were slaughtered at one abattoir located in the Northeast of Portugal. Carcass temperature and pH, logged during 24 h post-mortem, were modelled by exponential decay equations that estimated temperature (kT) and pH (kpH) decay rates. Additionally, other pH/temperature descriptors were estimated from the fitted models. From linear models adjusted to each descriptor, it was found that hot carcass weight, age, breed, gender, age class, fat cover, conformation and transport and lairage time had influence (P < 0.05) on pH and temperature decay rates. Thus, combining the variables kT and kpH, and selected animal/carcass characteristics as linear predictors, a system to classify quality of carcasses was developed.

The use of seemingly unrelated regression to predict the carcass composition of lambs.

The aim of this study was to develop and evaluate models for predicting the carcass composition of lambs. Forty male lambs were slaughtered and their carcasses were cooled for 24 hours. The subcutaneous fat thickness was measured between the 12th and 13th rib and breast bone tissue thickness was taken in the middle of the second sternebrae. Left side of carcasses was dissected and the proportions of lean meat (LMP), subcutaneous fat (SFP), intermuscular fat (IFP), kidney and knob channel fat (KCFP), and bone plus remainder (BP) were obtained. Models were fitted using the seemingly unrelated regression (SUR) estimator which is novel in this area, and compared to ordinary least squares (OLS) estimates. Models were validated using the PRESS statistic. Our results showed that SUR estimator performed better in predicting LMP and IFP than the OLS estimator. Although objective carcass classification systems could be improved by using the SUR estimator, it has never been used before for predicting carcass composition.