Portable near infrared (NIR) spectrometer coupled with machine learning to classify milk with subclinical mastitis

Abstract

Milk is one of the traditional foods in the human diet. However, subclinical mastitis infection in cows could compromise the nutritional composition of milk as well as the consumer safety. In this study, we investigated the possibility of implementing near infrared spectroscopy (NIRS), using a portable spectrometer, as a screening method to detect milk contaminated with subclinical mastitis. The spectra was acquired from a highly complex milk sample set (two seasons: winter and summer; two years: 2021 and 2022; from five geographical locations within Brazil: Goiás, Pará, Paraíba, São Paulo, and Santa Catarina; different feeding systems.) were analyzed using Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA), as well as machine learning models like Random Forest (RF) and Support Vector Machine (SVM). PCA revealed that the clustering of mastitis milk and non-mastitis milk samples was associated with variations in lactose content. This observation was also noted in PLS-DA, which achieved accuracy values of 78%, compared to the 62% achieved by RF and SVM in detecting mastitis milk. However, the sensitivity (recall) for detecting mastitis samples was higher for RF (78%), and SVM notably excelled in detecting non-mastitis milk (81%). Furthermore, it was demonstrated that the removal of outliers using Isolation Forest enhances the performance of RF and SVM models based on NIR spectra, with increases of up to 25% in the precision. In conclusion, the portable NIR spectrometer could potentially serve as a screening method in the dairy industry to detect mastitis milk samples.