Raman spectroscopy as a comprehensive tool for microbial identification

Raman spectroscopy has become a powerful tool for the rapid, precise identification of microorganisms in diverse microbiological applications. Over recent years, our research has focused on developing predictive models to classify fungal and bacterial species using their unique Raman signatures, optimizing identification processes for environmental and clinical microbiology.

A key challenge in microbial identification is differentiating similar species in complex environments. Initial studies on entomopathogenic fungi demonstrated that Raman microspectroscopy effectively distinguishes Metarhizium and Beauveria species in conidial form, achieving over 98% prediction accuracy across varying growth conditions. Expanding into bacteria, we developed models based on bulk Raman spectra of 21 species grown under standardized conditions, achieving high theoretical accuracies through machine learning, particularly support vector machines (SVM). Despite substrate differences, such as stainless steel versus silver, practical accuracy remained robust at ~80% when applied to independent spectra.

We also addressed biofilm detection, focusing on dry-surface biofilms (DSBs) prevalent in healthcare settings. Raman spectroscopy successfully differentiated between vegetative cells and spores within these biofilms, advancing disinfection strategies. Incorporating convolutional neural networks (CNNs) further enhanced predictive models, especially for single-cell analysis. While bulk data models showed promise for single-cell predictions, challenges like fluorescence and photodegradation persist. This work contributes to creating adaptable Raman databases for microbial identification down to the single-cell level.

Beyond applications, we investigated why Raman spectroscopy works so effectively. By analyzing spectral variations and baseline effects, we moved beyond peak analysis to biochemical interpretations, gaining insights into factors driving classification accuracy.

Our research underscores Raman spectroscopy"s versatility in microbial systematics. From fungi in environmental studies to bacteria in biofilms and clinical diagnostics, this method offers a fast, reliable, and scalable solution for microbial identification, paving the way for broader adoption across disciplines.