significant Raman vibrational bands were identified as: esters at 1731 cm-1 (C=O, stretching), amide I at 1651 cm-1 (C=O, stretching), lipids and nucleic acids at 1484 cm-1 deformation), proteins at 1375 cm-1, tyrosine (ring breathing) and/or nucleic acids stretching) at 826 cm-1, and a combination cytosine and uracil at 782 cm-1 (C=O, C-N and ring deformations)., The HCA dendogram (Figure 5B) of the Raman PC-DFA scores also exhibited this clear separation. Validation of the PLS-DA classification models (Figure 6B), generated using Raman spectral data confirmed the accuracy and validity of the model (between 95.3 – 99.9%) with an average prediction accuracy of 97.8% (Table S2). SERS fingerprints Unlike Raman, SERS spectra of S. cerevisiae provided detailed features (Figure 3C) showing that the nanoparticles had caused an enhancement and had quenched fluorescence. By contrast, SERS spectra obtained from P. putida samples contained high background fluorescence, possibly due to the presence of an exopolysaccharide capsule surrounding the cell, and were therefore excluded from further analysis. PC-DFA scores plot of the SERS spectral data displayed clear separation of all samples (Figure 4E, F), the two Bacillus spp. were separated from all other samples based on DF1. Although S. cerevisiae are yeast cells with different biochemical composition compared to that of bacterial cells, and despite any prior assumptions, these samples clustered closely with S. lividans. It was clear that in comparison to FT-IR and Raman data (Figure 4) that the clustering of the biological replicates was not so tight and this suggests that these data have
wavelength), prior to their application to a specific type of biological sample. Here for example, using our comparative approach, it was found that whilst background fluorescence was observed from Raman spectra for S. cerevisiae, this fluorescence quenched when applying SERS to the same species, and conversely SERS appeared to introduce strong fluorescence when analysing P. putida. Furthermore, whilst it is well known that midinfrared is a strong absorber of water, after drying samples for a short period prior to analysis, FT-IR spectra were shown to provide for a highly reproducible and accurate classification model for the whole range of samples used in this study. Thus, despite the limited number of microorganisms examined, the findings of this study suggest FTIR as ideal method for differentiation of microorganisms in pure, or potentially mixed cultures. In conclusion, this combined and comparative study of a range of clinically and industrially relevant microorganisms, clearly demonstrates the potential application of Raman, SERS, and FT-IR spectroscopies for microbial classification and differentiation. Although this is perhaps not surprising, given the rapidly expanding literature regarding individual applications of each of the three techniques explored here. The opposite observation is that our findings bring into focus and highlight the fact that when compared directly, there is not ‘one method that fits all’. Indeed, careful thought is required prior to selection of one or more of these techniques, especially regarding sample type, format, preparation, and the biological questions being asked. These spectroscopies, individually or when combined, will continue to reveal much detailed and useful knowledge, and enable novel insights into biological systems and bioprocesses, though only if applied correctly. Finally, these rapid methods, with their potential to be combined within single benchtop, bedside, or handheld portable units, and continued advances in miniaturisation, computation, and materials, only
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