The determination of bacterial identity at the strain level is still a complex and time-consuming endeavor. In this study, visible wavelength spontaneous Raman spectroscopy has been used for the discrimination of four closely related Escherichia coli strains: pathogenic enterohemorrhagic E. coli O157:H7 and non-pathogenic E. coli C, E. coli Hfr K-12, and E. coli HF4714. Raman spectra from 600 to 2000 cm−1 were analyzed with two multivariate chemometric techniques, principal component-discriminant function analysis and partial least squares-discriminant analysis, to determine optimal parameters for the discrimination of pathogenic E. coli from the non-pathogenic strains. Spectral preprocessing techniques such as smoothing with windows of various sizes and differentiation were investigated. The sensitivity and specificity of both techniques was in excess of 95%, determined by external testing of the chemometric models. This study suggests that spontaneous Raman spectroscopy with visible wavelength excitation is potentially useful for the rapid identification and classification of clinically-relevant bacteria at the strain level.

Laser-induced breakdown spectroscopy has been used to obtain spectral fingerprints from live bacterial specimens from thirteen distinct taxonomic bacterial classes representative of five bacterial genera. By taking sums, ratios, and complex ratios of measured atomic emission line intensities three unique sets of independent variables (models) were constructed to determine which choice of independent variables provided optimal genuslevel classification of unknown specimens utilizing a discriminant function analysis. A model composed of 80 independent variables constructed from simple and complex ratios of the measured emission line intensities was found to provide the greatest sensitivity and specificity. This model was then used in a partial least squares discriminant analysis to compare the performance of this multivariate technique with a discriminant function analysis. The partial least squares discriminant analysis possessed a higher true positive rate, possessed a higher false positive rate, and was more effective at distinguishing *Revised Manuscript Click here to view linked References 2 between highly similar spectra from closely related bacterial genera. This suggests it may be the preferred multivariate technique in future species-level or strain-level classifications.