Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy.

Periodontitis is a chronic inflammation of the periodontium caused by a persistent bacterial infection, resulting in destruction of the supporting structures of teeth. Analysis of microbial composition in saliva can inform periodontal status. Actinobacillus actinomycetemcomitans (Aa), Porphyromonas gingivalis (Pg), and Streptococcus mutans (Sm) are among reported periodontal pathogens, and were used as model systems in this study. Our atomic force microscopic (AFM) study revealed that these pathogens are biological nanorods with dimensions of 0.6-1.1 μm in length and 500-700 nm in width. Current bacterial detection methods often involve complex preparation steps and require labeled reporting motifs. Employing surface-enhanced Raman spectroscopy (SERS), we revealed cell-type specific Raman signatures of these pathogens for label-free detection. It overcame the complexity associated with spectral overlaps among different bacterial species, relying on high signal-to-noise ratio (SNR) spectra carefully collected from pure species samples. To enable simple, rapid, and multiplexed detection, we harnessed advanced machine learning techniques to establish predictive models based on a large set of raw spectra of each bacterial species and their mixtures. Using these models, given a raw spectrum collected from a bacterial suspension, simultaneous identification of all three species in the test sample was achieved at 95.6 % accuracy. This sensing modality can be applied to multiplex detection of a broader range and a larger set of periodontal pathogens, paving the way for hassle-free detection of oral bacteria in saliva with little to no sample preparation.