光谱学与光谱分析 |
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Application of Near-Infrared Spectra in the Determination of Water Soluble Chloride Ion in Plant Samples |
WU Rong-hui, SHAO Xue-guang* |
Department of Chemistry, University of Science and Technology of China, Hefei 230026, China |
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Abstract A new method was proposed to extract relevant information from near-infrared(NIR) spectra for multivariate calibration of water soluble chloride ion in complex plant samples. The method is a combination of discrete wavelet transform (DWT) and least squares support vector regression (LSSVR). After data compression and background removal in NIR spectra by DWT, the LSSVR approach was used to build NIR spectra regression models on the retained wavelet coefficients. Compared with partial least square regression (PLSR) and LSSVR, the proposed method is superior both in calculation speed and prediction accuracy.
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Received: 2005-03-08
Accepted: 2005-06-18
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Corresponding Authors:
SHAO Xue-guang
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Cite this article: |
WU Rong-hui,SHAO Xue-guang. Application of Near-Infrared Spectra in the Determination of Water Soluble Chloride Ion in Plant Samples[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2006, 26(04): 617-619.
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URL: |
https://www.gpxygpfx.com/EN/Y2006/V26/I04/617 |
[1] Vapnik V. The Nature of Statistical Learning Theory. New York: Springer Verlag, 1998. [2] LIANG Lu-hong, AI Hai-zhou, XIAO Xi-pan, et al(梁路宏, 艾海舟, 肖习攀, 等). Chinese Journal of Computer(计算机学报), 2002, 25(1): 22. [3] Thissen U, van Brakel R, de Weijer A P, et al. Chemom. Intell. Lab. Syst., 2003, 69(1-2): 35. [4] QU Hai-bin, LIU Xiao-xuan, CHENG Yi-yu(瞿海斌, 刘晓宣, 程翼宇). Chemical Journal of Chinese Universities(高等学校化学学报), 2004, 25(1): 39. [5] CHEN Nian-yi, LU Wen-cong, et al(陈念贻, 陆文聪, 等). Computers and Applied Chemistry(计算机与应用化学), 2004, 21(6): 886. [6] Suykens J A K, De Brabanter J, Lukas L, et al. Neurocomputing, 2002, 48: 85. [7] Thissen U, Ustun B, Melssen W J, et al. Anal. Chem., 2004, 76(11): 3099. [8] Chauchard F, Cogdill R, Roussel S, et al. Chemom. Intell. Lab. Syst., 2004, 71(2): 141. [9] SHAO Xue-guang, GU Hua, CAI Wen-sheng, PAN Zhong-xiao(邵学广,顾 华,蔡文生,潘忠孝). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 1999, 19(2): 139. [10] TIAN Gao-you, YUAN Hong-fu, LIU Hui-ying,LU Wan-zhen(田高友, 袁洪福, 刘慧颖, 陆婉珍). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2003, 23(6): 1111. [11] Jetter K, Depczynski U, Molt K, et al. Anal. Chim. Acta, 2000, 420: 169. [12] MA Xiao-guo, ZHANG Zhan-xia(马晓国, 张展霞). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2000, 20(4): 507. [13] Shao X G, Leung A K M, Chau F T. Accounts Chem. Res., 2003, 36(4): 276. [14] Chau F T, Liang Y Z, Gao J B, et al. Chemometrics: From Basic to Wavelet Transform. New Jersey: A John Wiley & Sons, Hoboken, 2004.
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