In this work we present a simple procedure based on 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 PCA, which may be useful for spectroscopists that are unfamiliar with PCA. Our proposed procedure is useful for studying the correlations between the energy channels of a given spectrum 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 procedure is independent of the spectra under study it can be a useful tool for addressing and interpreting the covariance structure of a measured spectrum for any spectroscopist.

中文翻译：

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使用主成分分析选择和可视化与样品分类有关的光谱变异性

在这项工作中，我们提出了一个基于主成分分析（PCA）的简单程序，可以通过选择感兴趣的总方差的一部分来重建测得的频谱。我们还提供了一种了解PCA提供的结果的方法，这可能对不熟悉PCA的光谱学家有用。我们提出的程序对于研究给定频谱的能量通道之间的相关性很有用，它也导致了一种新的滤波方法的构建。通过将其应用于X射线发射和X射线共振拉曼散射光谱可显示其潜力。由于拟议的程序与研究中的光谱无关，因此对于任何光谱学家来说，它都是解决和解释测量光谱协方差结构的有用工具。