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Discrimination of beef composition and sensory quality by using rapid Evaporative Ionisation Mass Spectrometry (REIMS).

Authors :
Liu, Jingjing
Birse, Nick
Álvarez, Carlos
Liu, Jiaqi
Legrand, Isabelle
Ellies-Oury, Marie-Pierre
Gruffat, Dominique
Prache, Sophie
Pethick, David
Scollan, Nigel
Hocquette, Jean-Francois
Source :
Food Chemistry. Oct2024, Vol. 454, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

• REIMS has the capability to differentiate various levels of meat sensory quality. • REIMS has greater efficacy in distinguishing lipid-dominated traits such as flavour. • REIMS has the sensitivity to capture and characterize metabolite features. 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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
03088146
Volume :
454
Database :
Academic Search Index
Journal :
Food Chemistry
Publication Type :
Academic Journal
Accession number :
177873928
Full Text :
https://doi.org/10.1016/j.foodchem.2024.139645