Machine learning methods for the prediction of milk fatty acid content

Autor(es)

Petrini, Juliana ; Salgado, Ricardo Menezes ; Petersen Rodriguez, Mary Ana ; Machado, Paulo Fernando ; Mourao, Gerson Barreto

• Tipo de documento: Artigo Científico
• Fonte: INTERNATIONAL JOURNAL OF DAIRY TECHNOLOGY; v. N/A, p. 10-pg., 2022-06-01.

Resumo

Random forest, extreme gradient boosting and artificial neural network machine learning methods were used to predict the content of myristic acid (C14:0) and conjugated linoleic acid (CLA) based on the milk fatty acids (FA) obtained through Fourier transform infrared spectroscopy. The methods had similar prediction performance for C14:0 (mean average percentage error < 7%). The low prediction accuracy for CLA was probably due to the poor association between the CLA and the input variables. Therefore, machine learning can be used to predict C14:0 and other saturated FA fromed from similar metabolic pathways (AU)

• Processo FAPESP: 10/12929-6 - Análise genética quantitativo-molecular para características de produção, perfil de ácidos graxos e qualidade do leite
• Beneficiário: Gerson Barreto Mourão
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 12/15948-7 - Incorporação de informações genômicas no desenvolvimento de índices econômicos para a seleção de bovinos leiteiros
• Beneficiário: Juliana Petrini
• Modalidade de apoio: Bolsas no Brasil - Doutorado