RT Journal Article
T1 Machine learning strategy for light lamb carcass classification using meat biomarkers
A1 García Infante, Manuel
A1 Castro Valdecantos, Pedro
A1 Delgado Pertiñez, Manuel
A1 Teixeira, Alfredo
A1 Guzmán Guerrero, José Luis
A1 Horcada Ibáñez, Alberto
AB In Mediterranean areas, lamb meat is considered to be of great commercial value. Moreover, consumers arebecoming increasingly interested in understanding the origin of lamb meat and its associated production andbreeding systems. Among many applications, algorithms based on artificial intelligence are used to identify theorigin of food products, and in this context, algorithms such as the Support Vector Machine (SVM), K-NearestNeighbours (KNN), and the Artificial Neural Network (ANN) have been proposed to differentiate the origin of theanimals according to their feeding diet. The objective of this study was to evaluate the performance of a variablereduction method based on a multiple regression model and three widely-used machine learning algorithms(SVM, KNN and ANN) for the classification of three commercial light lamb carcasses, from three feeding diets, inan indigenous Spanish breed (Mallorquina), using fatty acid and volatile compound biomarkers of meat. Machinelearning algorithms were employed to discriminate lamb carcasses using 14 identified significant biomarkers,which were arranged based on an estimation of the relative importance (stepwise forward multiple regression Fscore)of the input variables. We achieved high performances for the SVM, KNN and ANN algorithms, with 86%,98% and 98% prediction accuracy, respectively. Among the 14 biomarkers used, 7 were identified as showing thehighest discriminant capacity. The F-scores indicate that C17:1 and C20:5 n-3 fatty acids, and 2,5-dimethylpyrazineand 3-methylbutanal volatile compounds are the four most relevant biomarkers for predicting three lambfeeding diets.
PB Elsevier
SN 2212-4292
SN 2212-4306 (electrónico)
YR 2024
FD 2024-04
LK https://hdl.handle.net/10272/23845
UL https://hdl.handle.net/10272/23845
LA eng
NO García-Infante, M., Castro-Valdecantos, P., Delgado-Pertiñez, M., Teixeira, A., Guzmán, J. L., & Horcada, A. (2024). Machine learning strategy for light lamb carcass classification using meat biomarkers. In Food Bioscience (Vol. 59, p. 104104). Elsevier BV. https://doi.org/10.1016/j.fbio.2024.104104
NO This research has been financed by the Institute for Agricultural and Fisheries Research and Training (IRFAP) of the Government of the Balearic Islands (PRJ201502671-0781), the Spanish National Institute of Agricultural and Food Research and Technology and the European Social Fund (FPI2014-00013).
DS Repositorio Institucional de la Universidad de Huelva
RD 31 may 2026