光谱学与光谱分析 |
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Research on Analysis of Oil in Water Based on the Joint Optimization of Savitzky-Golay Smoothing and IBPLS Models |
HOU Pei-guo1, LI Ning1, CHANG Jiang1, WANG Shu-tao1, SONG Tao1, 2 |
1. Instrument Science and Engineering, School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China 2. Qinhuangdao Audio-Visual Machinery Insititute, Qinhuangdao 066004, China |
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Abstract Rapidly and accurately detection of the type and content of mineral oil in water pollution has important significance for the timely screening and control of pollution sources. The use of infrared spectral analysisi technology to detect mineral oil has advantanges of efficient, fast and pollution-free. Infrared spectrum technology is very for the detection of mineral oil in the water. In order to obtain a more reliable results, Fourier transforms attenuated total reflection infrared spectrometry (FITR-ATR) technology is used to get the spectral information of the mineral oil sample, and SPXY method is used to divide the sample set. The paper not only analyzed partial least squares (PLS) and iterative Bagging partial least squares (IBPLS) the two different methods to build regression model, also compared the difference of using the method of the combination of Savitzky-Golay (SG) smoothing and the method of a single iterative Bagging partial least squares (IBPLS) regression model. Based on the comparison of the predictive regression curve, we can get that the SG smooth has a better reflection on the results. And when the method of the combination of Savitzky-Golay (SG) smoothing and the method of a single iterative Bagging partial least squares (IBPLS) is used to build the regression model, the gasoline model parameters RMSEP is 0.001 125 g·mL-1, R is 0.992 5; diesel model parameters RMSEP is 0.001 384 g·mL-1, R is 0.989 3.
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Received: 2014-04-29
Accepted: 2014-08-05
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Corresponding Authors:
HOU Pei-guo
E-mail: pghou@ysu.edu.cn
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[1] Zhang D, Ding A, Cui S, et al. Water Research, 2013, 47(3): 1191. [2] Ugochukwu U C, Jones M D, Head I M, et al. Applied Clay Science, 2014, 87: 81. [3] Haule K, Toczek H. J of KONES Powertrain and Transport, 2014, 21(3): 127. [4] Wrona M, Pezo D, Nerin C. Food Chemistry, 2013, 141(4): 3993. [5] Ajayi, Ibironke Adetolu. Bioresource Technology, 2008, 99(11): 5125. [6] Lewis S P, Lewis A T, Lewis P D. Vibrational Spectroscopy, 2013, 69: 21. [7] ZOU Rui-jie, CHEN Yu-bang, FANG Yan-jun, et al(邹瑞杰, 陈玉榜, 房彦军,等). Chinese Journal of Scientific Instrument(仪器仪表学报), 2012, 33(3): 655. [8] Thygesen O, Hedegaard M A B, Zarebska A, et al. Vibrational Spectroscopy, 2014, 72: 119. [9] Galvo R K H, Araujo M C U, José G E, et al. Talanta, 2005, 67(4): 736. [10] Zhong B, Yuan X, Ji R, et al. Neurocomputing, 2014, 133: 317. [11] Huang D Y, Zhang Z, Ge S S. Computer Speech & Language, 2014, 28(2): 392. [12] WANG Li, ZHU Xue-feng(王 立, 朱学峰). Control Engineering of China(控制工程), 2009, 16(1): 59. [13] CHENG Long, WANG Gui-zeng(程 龙, 王桂增). J Tsinghua Univ·Sci. & Tech.(清华大学学报·自然科学版), 2008, 48(S2): 1780. [14] XIE Jun, PAN Tao, CHEN Jie-mei, et al(谢 军, 潘 涛, 陈洁梅,等). Chinese Journal of Analytical Chemistry(分析化学), 2010, 38(3): 342. [15] CHEN Hua-zhou, PAN Tao, CHEN Jie-mei(陈华舟, 潘 涛, 陈洁梅). Computer and Applied Chemistry(计算机与应用化学), 2011, 28(5): 518. [16] Chen H, Pan T, Chen J, et al. Chemometrics and Intelligent Laboratory Systems, 2011, 107(1): 139. [17] Des A M F L Miranda, De Seixas J M, Junior J D C, et al. Measurement, 2015, 60: 121 [18] DUAN Yan-qing, ZHE Wei, LI Qing-qing, et al(段焰青, 者 为, 李青青,等). Laster & Infrared(激光与红外), 2008, 38(7): 662. |
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