Machine learning methods for the prediction of milk fatty acid content

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)