Authentication of beef cuts by multielement and machine learning approaches.

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. 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. 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). CONCLUSION: The Multilayer Perceptron algorithm provided the best classification performance towards authentication of beef cuts on basis of major and trace element mass fractions.

Trace elements and machine learning for Brazilian beef traceability.

Brazilian livestock with a herd of more than 215 million animals is distributed over a vast area of 160 million hectares, leading the country to the first position in the world beef exports and second in beef production and consumption. Animals risen in the biomes Amazônia, Caatinga, Cerrado, Pampa and Pantanal were selected for this study. Beef samples were analyzed for their elemental content by neutron activation analysis and classified according to their origin by three machine learning algorithms (Multilayer Perceptron, Random Forest and Classification and Regression Tree). Significant differences (p < 0.0001) were observed between the beef elemental content from the different biomes for all multivariate contrasts using NPMANOVA. The highest classification performance was obtained for the biomes Amazônia and Caatinga using Multilayer Perceptron. Results showed the feasibility of combining trace element content and machine learning approaches for the Brazilian beef traceability.