光谱学与光谱分析 |
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Adopting the Method of Principal Components Analysis Combined with Correlation Coefficient to Increase the Predicted Concentration’s Accuracy of Benzene and Its Homology Mixture |
WU Zhong-chen1, XU Xiao-xuan1, YANG Ren-jie1, YU Gang2, ZHANG Cun-zhou1 |
1. The Photonics Center of the Physics Institute, Nankai University, Tianjin 300071, China 2. Dupont Display, California, USA; Visiting Professor of Nankai University, Tianjin 300071, China |
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Abstract The concentrations of benzene and its homology mixture were measured by near infrared spectra, and the emphasis was put on the character of the principal component and its physical significance. It is pointed out that the anterior principal components are very similar to the correlation coefficient of the multi-component solution and the theoretical proof for the right condition is given. The high frequency noise of the system can be checked out by principal component combined with the correlation coefficient. Removing the noise can greatly increase the accuracy of the prediction model.
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Received: 2003-12-06
Accepted: 2004-03-26
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Corresponding Authors:
WU Zhong-chen
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Cite this article: |
WU Zhong-chen,XU Xiao-xuan,YANG Ren-jie, et al. Adopting the Method of Principal Components Analysis Combined with Correlation Coefficient to Increase the Predicted Concentration’s Accuracy of Benzene and Its Homology Mixture [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2004, 24(12): 1566-1570.
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URL: |
https://www.gpxygpfx.com/EN/Y2004/V24/I12/1566 |
[1] WardⅡ Howard W,Sekulic S Sonja, Wheeler Michael J et al. Applied Spectroscopy, 1998, 52: 17. [2] Haaland David M, Robinson M.Ries, Koepp Gary W et al. Applied Spectroscopy, 1992, 46: 1575. [3] QI Xiao-ming, ZHANG Lu-da, DU Xiao-lin, SONG Zhao-juan, ZHANG Yi, XU Shu-yan(齐小明,张录达,杜晓林,宋召娟,张 一,徐淑燕). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2003,23(5): 870. [4] WANG Hui-wen(王惠文). Partial Least-Squares Regression-Method and Applications(偏最小二乘回归方法及其应用). Beijing:National Defence Industry Press(北京: 国防工业出版社),1999. [5] XU Yong-qun, SUN Su-qin, XU Jin-wen(徐永群,孙素琴,许锦文). Chinese Journal of Spectroscopy Laboratory(光谱实验室),2002,19(5):606. [6] HU Wei, TANG Wan-ying, ZHOU Shen-fan, XU Fu-ming(胡 伟,唐婉莹,周申范,徐复铭). Chinese Journal of Analytical Chemistry(分析化学),1997,25(5):614.
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