Selecting and visualizing the spectra variability relevant for sample classification using Principal Component Analysis
In this work we present a simple procedure based on a Principal Component Analysis (PCA) to reconstruct a measured spectrum by selecting the portion of its total variance of interest. We also provide an approach to the understanding of the results provided by a PCA, which may be useful for spectroscopist that are unfamiliar to PCA. Our proposed procedure is useful when studying the correlations between energy channels of a given spectra and it also leads to the construction of a new filtering method. Its potential is shown by applying it to X-Ray Emission and X-Ray Resonant Raman Scattering spectra. Since the proposed prodecure is independent of the spectra under study it can be a useful tool when addressing and interpreting the covariance structure of a measured spectrum for any spectroscopist.