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Application of principal component regression and partial least squares regression in ultraviolet spectrum water quality detection
Author(s): Jiangtong Li; Yongdao Luo; Honglin Dai
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Paper Abstract

Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR’s analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR’s result better than PLSR.

Paper Details

Date Published: 12 January 2018
PDF: 10 pages
Proc. SPIE 10620, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 106200V (12 January 2018); doi: 10.1117/12.2285011
Show Author Affiliations
Jiangtong Li, Yunnan Normal Univ. (China)
Yongdao Luo, Yunnan Normal Univ. (China)
Honglin Dai, Yunnan Normal Univ. (China)


Published in SPIE Proceedings Vol. 10620:
2017 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology
Guohai Situ; Xun Cao; Wolfgang Osten; Liquan Dong, Editor(s)

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