Spectral signatures for the classification of microbial species using Raman spectra.

In general, classification-based methods based on confocal Raman microscopy are focused on targeted studies under which the spectral libraries are collected under controlled instrument parameters, which facilitate analyses via standard multivariate data analysis methods and cross-validation. We develop and compare approaches to transform spectra collected at different spectral ranges and varying levels of resolution into a single lower-dimension spectral signature library. This will result in a more robust analysis method able to accommodate spectra accumulated at different times and conditions. We demonstrate these approaches on a relevant test case; the identification of microbial species from a natural environment. The training data were based on samples prepared for three unique species collected at two time points and the test data consisted of blinded unknowns prepared and analyzed at a later date with different instrument parameters. The results indicate that using reduced dimension representations of the spectral signatures improves classification accuracy over basic alignment protocols. In particular, utilizing the microbial species partial least squares discriminant analysis classifier on the blinded samples based on alignment achieved ~78 % accuracy, while both binning and peak selection approaches yielded 100 % accuracy.