Portable Raman spectroscopy combined with machine learning for rapid and label-free recognition of five pathogenic bacteria

Abstract

Introduction Antimicrobial resistance (AMR) continues to rise globally, highlighting the need for rapid, label-free, and cost-effective bacterial identification methods. In this proof-of-concept study, portable Raman spectroscopy combined with machine learning was used to identify five clinically relevant bacterial species: Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Porphyromonas gingivalis, and Streptococcus mutans. Methods Raman spectra were acquired from cultured, washed, PBS-resuspended, and OD-standardized bacterial suspensions. After SNIP baseline correction, binary and five-class classification models were constructed using Auto-Sklearn with eight algorithms: ADB, ET, GB, LDA, SVM, MLP, PA, and QDA. Model performance was evaluated using accuracy, precision, recall, F1-score, MCC, and ROC-AUC. Results Pairwise binary classification showed variable performance among bacterial pairs. The best result was obtained for P. gingivalis versus S. aureus, with a testing accuracy of 98.3%, precision of 0.984, recall of 0.983, F1-score of 0.983, MCC of 0.967, and ROC-AUC of 1.000. Five-class classification was more limited, with LDA achieving the highest testing accuracy of 60.1%, MCC of 0.506, and ROC-AUC of 0.867. Discussion These findings support the feasibility of portable Raman spectroscopy combined with machine learning for bacterial recognition under standardized sample conditions.