We report on our recent efforts towards identifying bacteria in environmental samples by means of Raman spectroscopy. We established a database of Raman spectra from bacteria submitted to various environmental conditions. This dataset was used to verify that Raman typing is possible from measurements performed in non-ideal conditions. Starting from the same dataset, we then varied the phenotype and matrix diversity content included in the reference library used to train the statistical model. The results show that it is possible to obtain models with an extended coverage of spectral variabilities, compared to environment-specific models trained on spectra from a restricted set of conditions. Broad coverage models are desirable for environmental samples since the exact conditions of the bacteria cannot be controlled.
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