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Authentication of beef cuts by multielement and machine learning approaches.
- Source :
-
Journal of trace elements in medicine and biology : organ of the Society for Minerals and Trace Elements (GMS) [J Trace Elem Med Biol] 2023 Jul; Vol. 78, pp. 127164. Date of Electronic Publication: 2023 Mar 29. - Publication Year :
- 2023
-
Abstract
- Background: Brazil has consolidated a relevant position in the world market, being the largest exporter and second producer of beef. Genetics, feeding system, geographic origin and climate influence the multielement profile of beef. The feasibility of combining classification algorithms with major and trace elements was evaluated as a tool for authentication of beef cuts.<br />Methods: Animals of Angus, Nelore and Wagyu crossbreeds, raised in a vertically integrated system, were sampled at the slaughterhouse for chuck steak, rump cap and sirloin steak. Supervised learning algorithms i.e. Classification and Regression Tree (CART), Multilayer Perceptron (MLP), Naïve Bayes (NB), Random Forest (RF) and Sequential Minimal Optimization (SMO) were used to build classification models based on the multielement profile of beef determined by neutron activation analysis.<br />Results: Br, Co, Cs, Fe, K, Na, Rb, Se and Zn were determined in the beef samples. The classification accuracy values obtained for the beef cuts were 96% (MLP), 95% (SMO), 91% (RF), 86% (NB) and 70% (CART).<br />Conclusion: The Multilayer Perceptron algorithm provided the best classification performance towards authentication of beef cuts on basis of major and trace element mass fractions.<br />Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2023 Elsevier GmbH. All rights reserved.)
- Subjects :
- Animals
Cattle
Bayes Theorem
Random Forest
Brazil
Machine Learning
Algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 1878-3252
- Volume :
- 78
- Database :
- MEDLINE
- Journal :
- Journal of trace elements in medicine and biology : organ of the Society for Minerals and Trace Elements (GMS)
- Publication Type :
- Academic Journal
- Accession number :
- 37031660
- Full Text :
- https://doi.org/10.1016/j.jtemb.2023.127164