文献类型：期刊文献

英文题名：Identification of Independent Components in Inhomogeneous Mixture by Differentiating of the Feature Maps from Raman Spectroscopy Mapping

作者：Kang, Yan[1]; Ma, Yunsheng[2]; Yu, Xinhai[3]; Shen, Jiaxi[1]

机构：[1] School of Chemistry & Molecular Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China; [2] Shandong Chambroad Holding Group Co., Ltd, Boxing, 256500, China; [3] Key Laboratory of Pressure Systems and Safety [MOE], School of Mechanical and Power Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China

年份：2025

外文期刊名：SSRN

收录：EI(收录号：20250204493)

语种：英文

外文关键词：Independent component analysis - Least squares approximations - Photointerpretation

摘要：A combination of Raman mapping and computational algorithm was proposed to analyze the components in mixture. The most challenging issue of Raman spectra for mixture analysis is how to resolve the overlapping of signals and how to estimate the number of the significant components. In this paper, heatmaps from the band intensities at different points and their corresponding contour maps were obtained. Discriminating of the overlapping peaks was achieved by analyzing the differences of spatial distribution patterns, followed by fitted and resolved by Voigt function. The number of the significant components can be determined by calculating the area of contour maps. The distribution profiles of peaks from the same component can be grouped into the same group by K-means clustering analysis due to the similar distributions of different bands from the same component. Then, the peaks of all the independent components can be found out. The decompositions of a simulated mixture and a resin have been finished in this work. The resolved main components matched well with the actual components in mixture. Comparing this method with multivariate curve resolution–alternating least squares, this approach showed a better ability for resolving of overlapping peak for analysis of mixtures. The main innovation of this work was the conversion of analysis of complex spectral information into differentiating of differences of visible images. ? 2025, The Authors. All rights reserved.