*Tentative band assignment references.[18,20]
3.2 Principal Component Analysis
MVA statistical routines were applied to the raw spectroscopic data of the microorganisms under study. The use of chemometrics in spectroscopy has been well documented in the MIR and Raman spectroscopies [20,22-23]. The MVA routine allows determining the variables responsible for separating and classifying the classes. For this work, the classification of the microorganisms was evaluated by performing pre-processing algorithms to reduce common scattering and background effects that are inevitable during spectral acquisition. It is common to see spectral variation effects due to sample heterogeneity, scattering attributed to particle size, solvent vapor pressure, and analyte solubility. These effects are corrected with the pre-processing algorithms, which allow correction in the data for accurate classification. A comparison between the raw unprocessed spectral data and pre-processed is shown in Appendix A, Figure 1A.
Principal Component Analysis (PCA) models were generated for the microorganisms considered for this discussion as neat and mixtures of these analytes representing a complex environment. The models were built on vibrational information using the 788 - 1884 cm-1spectral region. First, the PCA model for the neat Ec , Bt , and Se bacteria is shown in Figure 5. This model was generated using the Standard Normal Variate (SNV) processing algorithm. It is represented by the scores of principal components 1 (PC-1) vs. PC-2 for the bacteria deposited on SS. PC-1 and PC-2 scores represented the largest percentage of variation for the acquired spectra. The PC-1 accounts for 52% of the total spectral variance and 19% for the PC-2.