ArticleFrontiers in microbiology2026
Portable Raman spectroscopy combined with machine learning for rapid and label-free recognition of five pathogenic bacteria.
Article in Frontiers in microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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: 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, Results: Pairwise binary classification showed variable performance among bacterial pairs. The best result was obtained for Discussion: These findings support the feasibility of portable Raman spectroscopy combined with machine learning for bacterial recognition under standardized sample conditions.
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