Trace elements and machine learning for Brazilian beef traceability

Abstract

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.

Keywords: Beef discrimination; Brazilian biomes; Classification techniques; Neutron activation analysis.