Rapid, universal, and accurate identification of bacteria in their natural states is necessary for on-site environmental monitoring and fundamental microbial research. Surface-enhanced Raman scattering (SERS) spectroscopy emerges as an attractive tool due to its molecule-specific spectral fingerprinting and multiplexing capabilities, as well as portability and speed of readout. Here, we develop a SERS-based surface chemotaxonomy that uses bacterial extracellular matrices (ECMs) as proxy biosignatures to hierarchically classify bacteria based on their shared surface biochemical characteristics to eventually identify six distinct bacterial species at >98% classification accuracy. Corroborating with in silico simulations, we establish a three-way inter-relation between the bacteria identity, their ECM surface characteristics, and their SERS spectral fingerprints. The SERS spectra effectively capture multitiered surface biochemical insights including ensemble surface characteristics, e.g., charge and biochemical profiles, and molecular-level information, e.g., types and numbers of functional groups. Our surface chemotaxonomy thus offers an orthogonal taxonomic definition to traditional classification methods and is achieved without gene amplification, biochemical testing, or specific biomarker recognition, which holds great promise for point-of-need applications and microbial research.
Keywords: Bacteria; Machine Learning; SERS; Sensing; Small Molecular Probes; Surface Chemistry.