222 High Throughput Screening of Bovine Mastitis from Raw Milk Using Maldi-Tof Ms and Machine Learning

Abstract

Abstract To prevent pathogenic microorganisms associated with mastitis from entering the food supply, raw milk must be screened. This study investigated whether MALDI-TOF-MS combined with machine learning algorithms is a cost-effective method for screening dairy milk samples at high throughput. Raw milk from animals was analyzed using matrix-assisted laser desorption ionization mass spectrometry. Mass spectra were obtained for a training dataset of composite milk samples (n = 226) collected from a local dairy. The peaks from the mass spectra were then transported into a machine learning model, which detected non-obvious patterns that correspond with the mastitis condition. Finally, the diagnostic accuracy of the new model was evaluated using a separate set of milk samples (scoring set; n = 100). For optimal performance, decision trees, random forest models, gradient boosted trees, and neural network models were investigated. The results show that the gradient boosted decision tree model performs best for mastitis diagnosis, with simultaneous sensitivity and specificity greater than 0.8. The decision tree approach is conceptually straightforward and identifies candidate biomarker molecules indicative of mastitis. The combined MALDI TOF plus machine learning approach is effective for detecting mastitis in dairy cows and is likely a viable option for diagnosing a wide range of animal health issues. The MALDI TOF method is suitable for high-throughput screening because it only requires a microliter of inexpensive reagents.