Identification of Unknown Substances by Terahertz Spectroscopy and Multivariate Data Analysis

被引：0
作者：
Andreas Pohl
Nils Deßmann
Katja Dutzi
Heinz-Wilhelm Hübers
机构：
[1] Humboldt University zu Berlin,Department of Physics
[2] German Aerospace Center (DLR),Institute of Optical Sensor Systems
来源：
Journal of Infrared, Millimeter, and Terahertz Waves | 2016年 / 37卷
关键词：
Substance identification; Spectroscopy; Multivariate data analysis; Principal component analysis; Partial least squares regression; Terahertz;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The identification of various substances by multivariate data analysis of terahertz transmittance spectra is demonstrated. Transmittance spectra were obtained by the use of a Fourier transform infrared spectrometer. By means of principal component analysis and partial least squares regression, the spectral data were analyzed in order to identify substances and mixtures of several substances. With only three principal components, detection and identification of substances are possible with high accuracy. Using these methods, concentration ratios of substances in mixtures of two substances can be determined with an accuracy of 10 %. It is shown that the method is robust against disturbances in the spectra such as standing waves. This is particularly important for practical applications.
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页码：175 / 188
页数：13
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