Enhanced classification and identification of bacterial and viral microorganisms by integration of MALDI-TOF mass spectrometry with artificial intelligence

Abstract

The accurate and rapid identification of bacterial pathogens is essential in clinical setups and medical biodefense. Matrix-Assisted Laser Desorption Ionization Time-Of-Flight (MALDI-TOF) mass spectrometry has emerged as a powerful tool for fast and reliable microbial identification. This study assesses the performance of eight Machine Learning (ML) and two Deep Learning (DL) models trained using 5-fold cross validation in classifying microorganisms in a series of experiments based on MALDI-TOF mass spectra (n = 255) generated internally from seven cultured bacteria and five viral agents. The results showed that up to seven models achieved consistently robust classification across various tasks, including binary classification of viruses from bacteria and Gram-positive from Gram-negative bacteria, as well as multi-class classification of individual bacterial and viral species. Subsequently, we cross-validated three from our top-performing models, Extra Trees Classifier, Support Vector Classifier and 1-D Convolutional Neural Network, on Gram type and multi-class classification of individual bacterial species against an external dataset selected from a large MALDI-TOF MS database of highly pathogenic bacteria (curated by Robert Koch Institute). Among these, the Extra Trees Classifier consistently achieved the highest average accuracy and F1-score in both Gram type classification and species-level identification, demonstrating superior generalization across datasets. Its ensemble architecture proved particularly effective in capturing subtle spectral patterns associated with microbial cell wall composition and protein expression profiles. These results underscore the strong potential of this model to enhance MALDI-TOF-based classification frameworks in clinical microbiology and biodefense applications.