W. Barragán-Hernández , Ó. López-Campos , J.L. Aalhus , N. Prieto
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引用次数: 0
Abstract
Machine learning classification approaches were used to discriminate a fishy off-flavour identified in beef with health-enhanced fatty acid profiles. The random forest approach outperformed (P < 0.001; receiver operating characteristic curve: 99.8 %, sensitivity: 99.9 % and specificity: 93.7 %) the logistic regression, partial least-squares discrimination analysis and the support vector machine (linear and radial) approaches, correctly classifying 100 % and 82 % of the fishy and non-fishy meat samples, respectively. The random forest algorithm identified 20 volatile compounds responsible for the discrimination of fishy from non-fishy meat samples. Among those, seven volatile compounds (pentadecane, octadecane, γ-dodecalactone, dodecanal, (E,E)-2,4-heptadienal, 2-heptanone, and ethylbenzene) were selected as significant contributors to the fishy off-flavour fingerprint, all being related to lipid oxidation. This fishy off-flavour fingerprint could facilitate the rapid monitoring of beef with enhanced healthy fatty acids to avoid consumer dissatisfaction due to fishy off-flavour.
期刊介绍:
The aim of Meat Science is to serve as a suitable platform for the dissemination of interdisciplinary and international knowledge on all factors influencing the properties of meat. While the journal primarily focuses on the flesh of mammals, contributions related to poultry will be considered if they enhance the overall understanding of the relationship between muscle nature and meat quality post mortem. Additionally, papers on large birds (e.g., emus, ostriches) as well as wild-captured mammals and crocodiles will be welcomed.