Fuente: PubMed "meat" Meat Sci. 2026 Feb 26;237:110076. doi: 10.1016/j.meatsci.2026.110076. Online ahead of print.ABSTRACTThe Rapid evaporative ionization mass spectrometry (REIMS) technique enables real-time metabolite analysis of intact beef tissues, potentially predicting carcass traits and quality. Two 10-day aged muscles from 66 Limousine cows of a specific production system, rump (m. gluteus medius, RMP, n = 63) and striploin (m. longissimus thoracis, STR, n = 57) were measured using REIMS. The REIMS outputs were used to develop classification and prediction models via orthogonal partial to latent structures-discriminant (OPLS-DA). Variables studied included carcass traits (transport distance, muscle type, EUROP conformation, carcass weight, Meat Standards Australia (MSA) marbling score), laboratory measurements (intramuscular fat (IMF) content, Warner-Bratzler shear force (WBSF), compression force), and sensory traits (untrained consumer scores and the calculated meat-eating quality score (MQ4)). Variables were divided into two binary groups based on median values. The REIMS accurately distinguished RMP from STR muscles (test accuracy = 100%), and showed strong associations with transport distance, carcass traits, and laboratory measurements (R2Y = 0.86-0.94; Q2 = 0.55-0.78; test accuracy = 41.7%-66.7%). For sensory quality traits, RMP-based models achieved test accuracies of 63.3%-73.3%, while STR-based models failed to reliably predict any traits (test accuracies <60%). Tentative identification of REIMS features reflected the distinct intrinsic metabolic characteristics between the two muscles. Sensory quality traits in RMP were predominantly influenced by molecules involved in proteolysis and energy metabolism, whereas STR traits were primarily associated with lipid metabolism. These results highlight that REIMS is a rapid, muscle-specific evaluation tool, offering a promising alternative to traditional carcass grading and beef sensory quality assessment, especially in binary classification.PMID:41797203 | DOI:10.1016/j.meatsci.2026.110076