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Neutron activation analysis and data mining techniques to discriminate between beef cattle diets

  • Yuniel Tejeda MazolaEmail author
  • Elisabete A. De Nadai Fernandes
  • Gabriel A. Sarriés
  • Márcio A. Bacchi
  • Cláudio L. Gonzaga
Article
  • 13 Downloads

Abstract

Neutron activation analysis and data mining techniques were combined for assessing the mineral composition of diets commonly used to feed beef cattle in Brazil. Among twenty chemical elements determined, Br, Ca, Cs, La, Sc, Se, Sr, Th and Zn showed statistically significant differences between the two cattle diets studied. Chi square indicated that Cs, Se and Sc provided better diets discrimination. The highest classification performances using these elements were achieved for multilayer perceptron and sequential minimal optimization with prediction accuracy of 100%.

Keywords

Multielement isotopic technique Machine learning Trace elements Beef cattle grain-finished 

Notes

Acknowledgements

Yuniel Tejeda Mazola would like to thank the Coordination of Improvement of Higher Level Personnel (CAPES) for the scholarship. Special thanks to Beef Passion, in particular, in the person of its president Antônio Ricardo Sechis and the general manager Amauri José Maria Sechis, for offering all technical support during sampling campaigns.

Supplementary material

10967_2019_6874_MOESM1_ESM.docx (36 kb)
Supplementary material 1 (DOCX 36 kb)

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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  1. 1.Nuclear Energy Center for AgricultureUniversity of São PauloPiracicabaBrazil
  2. 2.College of Agriculture Luiz de QueirozPiracicabaBrazil

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