| 光谱学与光谱分析 |
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| Quantitative Analysis Method of Natural Gas Combustion Process Combining Wavelength Selection and Outlier Spectra Detection |
| CAO Hui1, HU Luo-na1, ZHOU Yan2* |
1. State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2. School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China |
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Abstract The present paper uses a combination method of wavelength selection and outlier spectra detection for quantitative analysis of nature gas combustion process based on its near infrared spectra. According to the statistical distribution of partial least squares (PLS) model coefficients and prediction errors, the method realized wavelength selection and outlier spectra detection, respectively. In contrast with PLS, PLS after leave-one-out for outlier detection (LOO-PLS), uninformative variable elimination by PLS (UVE-PLS) and UVE-PLS after leave-one-out for outlier detection (LOO-UVE-PLS), the root-mean-squared error of prediction (RMSEP)based on the method for CH4 prediction model is reduced by 14.33%, 14.33%, 10.96% and 12.21%; the RMSEP value for CO prediction model is reduced by 67.26%, 72.58%, 11.32% and 4.52%; the RMSEP value for CO2 prediction model is reduced by 5.95%, 19.7%, 36.71% and 4.04% respectively. Experimental results demonstrate that the method can significantly decrease the number of selected wavelengths, reduce model complexity and effectively detect outlier spectra. The established prediction model of analytes is more accurate as well as robust.
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Received: 2012-02-28
Accepted: 2012-06-18
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Corresponding Authors:
ZHOU Yan
E-mail: yan.zhou@mail.xjtu.edu.cn
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[1] FENG Ai-ming, FANG Li-min, LIN Min(冯爱明, 方利民, 林 敏). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2011, 31(6): 1514.
[2] Bian X H, Cai W S, Shao X G, et al. Analytical Methods, 2010, 135(11): 2841.
[3] Balabin R M, Safieva R A, Lomakina E I. Microchemical Journal, 2011, 98(1): 121.
[4] Liu Fei, He Yong, Wang Li, et al. Food and Bioprocess Technology, 2011, 4(8): 1331.
[5] Zou X B, Zhao J W, Malcolm J W, et al. Analytica Chimica Acta, 2010, 667(1-2): 14.
[6] Zhang J F, Jiang Y Y, Chang K H, et al. Pattern Recognition Letters, 2009, 30(15): 1434.
[7] Joan Ferré, Enric Comas. Talanta, 2011, 83(4): 1147.
[8] Vitézslav Centner, Désiré-Luc Massart, Onno E de Noord, et al. Anal. Chem., 1996, 68(21): 3851.
[9] Cao D S, Liang Y Z, Xu Q S, et al. Journal of Computer-Aided Molecular Design, 2011, 25(1): 67. |
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