Abstract
INTRODUCTION: Mastitis is one of most impacting health issues in bovine dairy farming that reduces milk yield and quality, leading to important economic losses. Subclinical forms of the disease are routinely monitored through the measurement of somatic cell count (SCC) and microbiological tests. However, their identification can be tricky, reducing the possibilities of early treatments. In this study, a MALDI-TOF mass spectrometry approach was applied to milk samples collected from cows classified according to the SCC, to identify differences in polypeptide/protein profiles. MATERIALS AND METHODS: Twenty-nine raw milk samples with SCC >200,000 cell/ml (group H) and 91 samples with SCC lower than 200,000 (group L) were randomly collected from 12 dairy farms. Spectral profiles from skim milk were acquired in the positive linear mode within the 4,000-20,000 m/z mass acquisition range. RESULTS AND DISCUSSION: Based on signal intensity, a total of 24 peaks emerged as significant different between the two groups. The most discriminant signals (4,218.2 and 4,342.98 m/z) presented a ROC curve with AUC values higher than 0.8. Classification algorithms (i.e., quick classifier, genetic algorithm, and supervised neural network) were applied for generating models able to classify new spectra (i.e., samples) into the two classes. Our results support the MALDI-TOF mass spectrometry profiling as a tool to detect mastitic milk samples and to potentially discover biomarkers of the disease. Thanks to its rapidity and low-cost, such method could be associated with the SCC measurement for the early diagnosis of subclinical mastitis.