RESUMO
Herein, we investigated the potential of REIMS analysis for classifying muscle composition and meat sensory quality. The study utilized 116 samples from 29 crossbred Angus × Salers, across three muscle types. Prediction models were developed combining REIMS fingerprints and meat quality metrics. Varying efficacy was observed across REIMS discriminations - muscle type (71 %), marbling level (32 %), untrained consumer evaluated tenderness (36 %), flavor liking (99 %) and juiciness (99 %). Notably, REIMS demonstrated the ability to classify 116 beef across four Meat Standards Australia grades with an overall accuracy of 37 %. Specifically, "premium" beef could be differentiated from "unsatisfactory", "good everyday" and "better than everyday" grades with accuracies of 99 %, 84 %, and 62 %, respectively. Limited efficacy was observed however, in classifying trained panel evaluated sensory quality and fatty acid composition. Additionally, key predictive features were tentatively identified from the REIMS fingerprints primarily comprised of molecular ions present in lipids, phospholipids, and amino acids.