Trace elements and machine learning for Brazilian beef traceability.

Autor: De Nadai Fernandes EA; Nuclear Energy Center for Agriculture, University of São Paulo, Avenida Centenário 303, 13416-000 Piracicaba, SP, Brazil. Electronic address: lis@cena.usp.br., Sarriés GA; College of Agriculture Luiz de Queiroz, University of São Paulo, Avenida Pádua Dias 11, 13418-900 Piracicaba, SP, Brazil., Bacchi MA; Nuclear Energy Center for Agriculture, University of São Paulo, Avenida Centenário 303, 13416-000 Piracicaba, SP, Brazil., Mazola YT; Nuclear Energy Center for Agriculture, University of São Paulo, Avenida Centenário 303, 13416-000 Piracicaba, SP, Brazil., Gonzaga CL; Nuclear Energy Center for Agriculture, University of São Paulo, Avenida Centenário 303, 13416-000 Piracicaba, SP, Brazil., Sarriés SRV; Nuclear Energy Center for Agriculture, University of São Paulo, Avenida Centenário 303, 13416-000 Piracicaba, SP, Brazil.
Jazyk: angličtina
Zdroj: Food chemistry [Food Chem] 2020 Dec 15; Vol. 333, pp. 127462. Date of Electronic Publication: 2020 Jul 04.
DOI: 10.1016/j.foodchem.2020.127462
Abstrakt: 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.
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.
(Copyright © 2020 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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