外文摘要：This study analyses various chicken cuts (breast, leg, and thigh) in terms of ten biocompounds: nicotinamide, anserine, carnosine, malondialdehyde, and biogenic amines (putrescine, cadaverine, histamine, tyramine, spermidine, and spermine). The analysis is conducted on refrigerated chicken meat cuts using three different packaging solutions: modified atmosphere packaging (MAP), vacuum skin packaging (SKIN), and permeable O2 plastic film (STRETCH). Our results show that nicotinamide was the most discriminant compound followed for cuts and time. Remarkably, its concentration decreases over time, unaffected by any of the packaging solutions. The collective contribution of all the examined biocompounds is highlighted through multivariate statistical analysis, utilizing partial least square discriminant analysis (PLS-DA) and the support vector machine (SVM) algorithm. Both machine learning models demonstrate high classification accuracy: for cut, storage time, and packaging, PLS-DA yields classification accuracy rates of 87%, 85%, and 70%, respectively. SVM achieves even higher accuracy rates of 97%, 99%, and 94% for cut, storage time, and packaging, respectively. These results underscore the importance of considering the combined influence of all the biocompounds investigated in this study for a finer classification of chicken meat cuts and packaging. Furthermore, our findings highlight the efficacy of SVM as a discriminant multivariate approach in food classification.
外文关键词：SVM;PLS-DA;Packaged chicken meat;Nicotinamide;Carnosine;Anserine;Biogenic amines;Malondialdehyde
作者：Esposito, Luigi;Mascini, Marcello;Silveri, Filippo;Pepe, Alessia;Mastrocola, Dino;Martuscelli, Maria
作者单位：Univ Teramo
期刊名称：FOOD BIOSCIENCE
期刊影响因子：0.0
出版年份：2024
出版刊次：58
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台