光谱学与光谱分析 |
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Study on the Gasoline Classification Methods Based on Near Infrared Spectroscopy |
ZHANG Jun1,JIANG Li1,CHEN Zhe1,YU Qian1,LIANG Jing-qiu2,WANG Jing-hua3 |
1. Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Educational Institutes, Jinan University, Guangzhou 510632, China 2. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China 3. China Petrochemical Corporation, Guangzhou Branch, Guangzhou 510726, China |
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Abstract The purpose of the present paper is to study the classification methods of gasoline. First, two classific models are compared using discriminant cluster analysis method in 700-1 100 nm and 1 100-1 700 nm spectral region. The sample is 90#, 93# and 97# gasoline. The results show that the model in 1 100-1 700 nm spectral region is veracious. And then a new model has been educed based on principal component analysis (PCA) and self-organizing competitive neural networks in order to classify 90#, 93# and 97# gasoline. The spectral data were condensed by PCA method before modeling, and three principal components were chosen because their cumulative credibility had reached 97%. A three-layer self-organizing competitive neural network model was established. Thirty-two wavelengths’ absorbance is the concentrated spectral data by PCA method, and served as inputs of the self-organizing competitive neural network. The learning parameter is set as 0.01 and the training iteration is taken as 500. The conclusion is that it is feasible to apply near infrared spectroscopy to discriminate the gasoline products as the PCA and self-organizing competitive neural networks method is used. Also the PCA and self-organizing competitive neural networks method is better than the discriminant cluster analysis method.
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Received: 2010-01-11
Accepted: 2010-04-26
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Corresponding Authors:
ZHANG Jun
E-mail: ccdbys@163.com
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[1] ZHUGE Jing-chang, ZENG Zhou-mo, LU Li, et al(诸葛晶昌,曾周末,陆 丽,等). Optics and Precision Engineering(光学精密工程),2009, 17(6):1479. [2] LU Wan-zhen(陆婉珍). Modern Near Infrared Spectroscopy Analytical Technology(Second Edition)(现代近红外光分析技术·第2版). Beijing: Chinese Petrochemical Press(北京: 中国石化出版社), 2007. [3] ZHANG Qi-ke, DAI Lian-kui(张其可, 戴连奎). Control and Instruments in Chemical Industry(化工自动化及仪表), 2005, 32(4): 53. [4] SHI Yue-hua, LU Yong, XU Guang-ming, et al(史月华, 陆 勇, 徐光明,等). Chinese Journal of Analytical Chemistry(分析化学), 2001, 29(1): 87. [5] SHI Yong-gang, LIU Shao-pu, SONG Shi-yuan, et al(史永刚, 刘绍璞, 宋世远,等). Journal of Instrumental Analysis(分析测试学报), 2007, 26(3): 343. [6] Kelly J J. Anal. Chem., 1989, 61: 313. [7] Skrobot Vinicius L, Castro Eustquio V R. Energy & Fuels, 2005, 19(6): 2350. [8] CHU Xiao-li, XU Yu-peng, LU Wan-zhen(褚小立, 许育鹏, 陆婉珍). Journal of Instrumental Analysis(分析测试学报), 2008, 27(6): 619. [9] LIU Sa, ZHU Hong, CHU Xiao-li(刘 莎, 朱 虹, 褚小立). Journal of Instrumental Analysis(分析测试学报), 2002, 21(1): 40. [10] DAN Tu-nan, DAI Lian-kui(淡图南, 戴连奎). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2009, 29(2): 351. [11] YANG Jian-gang(杨建刚). Practical Course of Artificial Neural Network(人工神经网络实用教程). Hangzhou: Publishing House of Zhejiang University(杭州:浙江大学出版社), 2001: 41. [12] YUAN Hong-fu, CHU Xiao-li, LU Wan-zhen(袁洪福, 褚小立, 陆婉珍). Chinese Journal of Analytical Chemistry(分析化学), 2004, 32(2): 255.
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