Machine learning methods for the prediction of milk fatty acid content

Author(s)

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

• Document type: Journal article
• Source: INTERNATIONAL JOURNAL OF DAIRY TECHNOLOGY; v. N/A, p. 10-pg., 2022-06-01.

Abstract

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)

• FAPESP's process: 10/12929-6 - Quantitative-molecular genetic analysis for production traits, fatty acid profile and milk quality
• Grantee: Gerson Barreto Mourão
• Support Opportunities: Regular Research Grants
• FAPESP's process: 12/15948-7 - Inclusion of genomic information in the development of economic index for dairy cattle selection
• Grantee: Juliana Petrini
• Support Opportunities: Scholarships in Brazil - Doctorate