Machine learning-assisted matrix-assisted laser desorption/ionization time-of-flight mass spectrometry toward rapid...

This study established a method for rapid classification of milk products by combining MALDI-TOF MS analysis with machine learning techniques. The analysis of 2 different types of milk products was used as an example. To select key variables as potential markers, integrated machine learning strategies based on 6 feature selection techniques combined with support vector machine (SVM) classifier were implemented to screen the informative features and classify the milk samples. The models were evaluated and compared by accuracy, Akaike information criterion (AIC), and Bayesian information criterion (BIC). The results showed the least absolute shrinkage and selection operator (LASSO) combined with SVM performs best, with prediction accuracy of 100% ± 0%, AIC of -360 ± 22, and BIC of -345 ± 22. Six features were selected by LASSO and identified based on the available protein molecular mass data. These results indicate that MALDI-TOF MS coupled with machine learning technique could be used to search for potential key targets for authentication and quality control of food products.