Elemental profile of tail hair to evaluate nutritional status of Angus, Nelore and Wagyu cattle breeds (2022)
- Authors:
- USP affiliated authors: FERNANDES, ELISABETE APARECIDA DE NADAI - CENA ; BACCHI, MARCIO ARRUDA - CENA ; SARRIES, GABRIEL ADRIAN - ESALQ ; GONZAGA, CLAUDIO LUIS - CENA ; MOREIRA, GABRIELE ROSSINI - CENA ; MAZOLA, YUNIEL TEJEDA - CENA
- Unidades: CENA; ESALQ
- DOI: 10.1007/s10967-022-08553-1
- Assunto: PÊLOS DE ANIMAL
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Journal of Radioanalytical and Nuclear Chemistry
- ISSN: 0236-5731
- Volume/Número/Paginação/Ano: online, September 2022
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
MOREIRA, Gabriele R et al. Elemental profile of tail hair to evaluate nutritional status of Angus, Nelore and Wagyu cattle breeds. Journal of Radioanalytical and Nuclear Chemistry, 2022Tradução . . Disponível em: https://doi.org/10.1007/s10967-022-08553-1. Acesso em: 29 dez. 2025. -
APA
Moreira, G. R., Fernandes, E. A. D. N., Mazola, Y. T., Bacchi, M. A., Sarriés, G. A., & Gonzaga, C. L. (2022). Elemental profile of tail hair to evaluate nutritional status of Angus, Nelore and Wagyu cattle breeds. Journal of Radioanalytical and Nuclear Chemistry. doi:10.1007/s10967-022-08553-1 -
NLM
Moreira GR, Fernandes EADN, Mazola YT, Bacchi MA, Sarriés GA, Gonzaga CL. Elemental profile of tail hair to evaluate nutritional status of Angus, Nelore and Wagyu cattle breeds [Internet]. Journal of Radioanalytical and Nuclear Chemistry. 2022 ;[citado 2025 dez. 29 ] Available from: https://doi.org/10.1007/s10967-022-08553-1 -
Vancouver
Moreira GR, Fernandes EADN, Mazola YT, Bacchi MA, Sarriés GA, Gonzaga CL. Elemental profile of tail hair to evaluate nutritional status of Angus, Nelore and Wagyu cattle breeds [Internet]. Journal of Radioanalytical and Nuclear Chemistry. 2022 ;[citado 2025 dez. 29 ] Available from: https://doi.org/10.1007/s10967-022-08553-1 - Discriminating beef producing countries by multi-element analysis and machine learning
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Informações sobre o DOI: 10.1007/s10967-022-08553-1 (Fonte: oaDOI API)
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