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Selecting and visualizing the spectra variability relevant for sample classification using Principal Component Analysis

Abstract

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.

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Article information


Submitted
06 Apr 2020
Accepted
13 May 2020
First published
13 May 2020

J. Anal. At. Spectrom., 2020, Accepted Manuscript
Article type
Paper

Selecting and visualizing the spectra variability relevant for sample classification using Principal Component Analysis

J. I. Robledo and E. Cuestas, J. Anal. At. Spectrom., 2020, Accepted Manuscript , DOI: 10.1039/D0JA00148A

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