Université Lille , CNRS, UMR 8516 Laboratoire de Spectrochimie Infrarouge et Raman , F-59000 Lille , France.
University of Sistan and Baluchestan , Department of Chemistry, Faculty of Science , P.O. Box 98135-674, Zahedan , Iran.
Anal Chem. 2019 Sep 3;91(17):10943-10948. doi: 10.1021/acs.analchem.9b02890. Epub 2019 Aug 15.
We propose a methodology to select essential spectral pixels (ESPs) of chemical images. These pixels are on the outer envelope of the principal component scores of the data and can be identified by convex-hull computation. As ESPs carry all the linearly mixed spectral information, large hyperspectral images can be dramatically reduced before multivariate curve resolution (MCR) analysis. We investigated chemical images of different spectroscopies, sizes, and complexities and show that the analysis of full data sets of hundreds of thousands of spectral pixels only require a few tenths of them.
我们提出了一种选择化学图像基本光谱像素（ESPs）的方法。这些像素位于数据主成分得分的外包络线上，可以通过凸壳计算来识别。由于 ESPs 携带所有线性混合的光谱信息，因此在进行多元曲线分辨（MCR）分析之前，可以大大减少大的高光谱图像的数据量。我们研究了不同光谱学、大小和复杂度的化学图像，结果表明，分析数十万光谱像素的完整数据集只需要其中的十分之几。
