Stochastic generalization models learn to comprehensively detect volatile organic compounds associated with foodborne pathogens <i>via</i> Raman spectroscopy.

ABSTRACT: Ensuring food safety requires continuous innovation, especially in the detection of foodborne pathogens and chemical contaminants. In this study, we present a system that combines Raman spectroscopy with machine learning (ML) algorithms for the precise detection and analysis of VOCs linked to foodborne pathogens in complex liquid mixtures. A remote fiber-optic Raman probe was developed to collect spectral data from 42 distinct VOC mixtures, representing contamination scenarios with dilution levels ranging from undiluted to highly diluted states. A dataset comprising 1445 Raman spectra was analyzed using classification and regression ML models, including multi-layer perceptron (MLP), random forest, and extreme gradient boosting decision trees (XGBDT). The optimized ML models achieved over 9

SUBMITTER: Zhang B 

PROVIDER: S-EPMC11822765 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature