Novel method for rapid identification of Listeria monocytogenes based on metabolomics and deep learning

Metabolomics based on the mass spectrometry approach can serve as a platform to detect pathogens and spoilage microorganisms. However, the accurate quantification of biomarkers with lower molecular weight based on mass spectrometry is generally limited by isotope-labeled standards and complicated protocols, which is not conducive to large-scale applications. Here, we developed a novel method that combined metabolomics with deep learning for the identification of Listeria monocytogenes. A convolutional neural network (CNN) model of these three potential biomarkers for L. monocytogenes was established, with a prediction accuracy of 82.2%. Furthermore, metabolic fingerprints composed of 29 metabolites were obtained using pseudotargeted metabolomics approach, which successfully distinguished six common Listeria species in hierarchical cluster analysis. The binary and multiple classifiers of CNN models were established to identify L. monocytogenes and common pathogens, which prediction accuracies were 96.7% and 96.3%, respectively. This novel method combined pseudotargeted metabolomics with deep learning is a promising powerful tool for pathogen identification and classification.

Automated summary

In this study, a novel method that combines metabolomics with deep learning was developed for the detection and identification of Listeria monocytogenes. By establishing a convolutional neural network model and using a pseudotargeted metabolomics approach, high prediction accuracies were achieved and different Listeria species were successfully distinguished. This is a promising powerful tool for the detection and classification of pathogens.