光谱学与光谱分析 |
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Component Analysis of Complex Mixed Solution Based on Multidimensional Diffuse Reflectance Spectroscopy |
LI Gang1, XIONG Chan1, ZHAO Li-ying1, LIN Ling1, TONG Ying2, ZHANG Bao-ju2* |
1. State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin 300072, China 2. College of Physics and Electronic Information, Tianjin Normal University, Tianjin 300387, China |
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Abstract In the present paper, the authors proposed a method for component analysis of complex mixed solutions based on multidimensional diffuse reflectance spectroscopy by analyzing the information carried by spectrum signals from various optical properties of various components of the analyte. The experiment instrument was designed with supercontinuum laser source, the motorized precision translation stage and the spectrometer. The Intralipid-20% was taken as an analyte, and was diluted over a range of 1%~20% in distilled water. The diffuse reflectance spectrum signal was measured at 24 points within the distance of 1.5~13 mm (at an interval of 0.5 mm) above the incidence point. The partial least squares algorithm model was used to perform a modeling and forecasting analysis for the spectral analysis data collected from single-point and multi-point. The results showed that the most accurate calibration model was created by the spectral data acquired from the nearest 1~13 points above the incident point; the most accurate prediction model was created by the spectral signal acquired from the nearest 1~7 points above the incident point. It was proved that multidimensional diffuse reflectance spectroscopy can improve the spectral signal to noise ratio. Compared with the traditional spectrum technology using a single optical property such as absorbance or reflectance, this method increased the impact of scattering characteristics of the analyte. So the use of a variety of optical properties of the analytes can make an improvement of the accuracy of the modeling and forecasting, and also provide a basis for component analysis of the complex mixed solution based on multidimensional diffuse reflectance spectroscopy.
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Received: 2011-05-30
Accepted: 2011-09-06
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Corresponding Authors:
ZHANG Bao-ju
E-mail: wdxyzbj@163.com
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[1] Jahromia E Z,Bidaria A,Assadia Y,et al. Analytica Chimica Acta, 2007, 585(2): 305. [2] ZHANG Jian,QIU Yu,YU Dao-yong(张 建,邱 宇,于道永). Chinese Journal of Applied Chemistry(应用化学), 2009, 26(12): 1840. [3] Wang Liqun, Mizaikoff Boris. Analytical and Bioanalytical Chemistry, 2008, 391: 1641. [4] Yi Chena, Xie Mingyong, Yan Yan. Analytica Chimica Acta, 2008, 618(2): 121. [5] Sheng Nan, Cai Wensheng, Shao Xueguang. Talanta, 2009, 79(2): 339. [6] ZHANG Hai-liang, SUN Xu-dong, HAO Yong, et al(章海亮,孙旭东,郝 勇,等). Journal of Northwest A&F University (Natural Science Edition)(西北农林科技大学学报·自然科学版), 2010, 38(4): 128. [7] Vishwanath K, Chang K, Klein D, et al. Applied Spectroscopy, 2011, 65(2): 206. [8] Rajaram N, Gopal A, Zhang X, et al. Lasers in Surgery and Medicine, 2010, 42: 680. [9] Shinzawa H, Ritthiruangdej P, Ozaki Y. Applied Spectroscopy, 2001, 65(5): 549. [10] LU Wan-zhen, YUAN Hong-fu, XU Guang-tong, et al(陆婉珍, 袁洪福, 徐广通, 等). Modern Near Infrared Spectroscopy Analytical Technology(Second Edition)(现代近红外光谱分析技术,第2版). Beijing: China Petrochemical Press(北京: 中国石化出版社), 2007.
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