| 光谱学与光谱分析 |
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| Classification of Oils by Attenuated Total Reflectance-Fourier Transform Infrared Spectrometry Combined with Pattern Recognition Techniques |
| LIU Qian1, 2, SUN Pei-yan2, 3, GAO Zhen-hui2, 3, CAI Wen-sheng1, SHAO Xue-guang1* |
1. College of Chemistry, Nankai University, Tianjin 300071, China
2. Key Laboratory of Marine Spill Oil Identification and Damage Assessment Technology, State Oceanic Administration, Qingdao 266033, China
3. North China Sea Marine Monitoring Center, State Oceanic Administration, Qingdao 266033, China |
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Abstract In the present work, the combination of attenuated total reflectance-Fourier transform infrared spectrometry (ATR-FTIR) and pattern recognition, including principal components analysis (PCA) and hierarchical cluster analysis (HCA), is used as a fast and convenient analytical tool to classify oil samples. Twenty five samples including crude oils and fuel oils with different total contents of n-alkanes were analyzed. It was found that multiplicative scatter correction (MSC) and continuous wavelet transform (CWT) as a pretreatment method could improve the classification results of pattern recognition. The classification results were proved to be in agreement with the origin of the oil samples. The oils with high content of n-alkanes and those with low content were classified clearly by this developed method, but it still had some constraint to differentiating oils with little difference. The present work provides a feasible method for quick classification of oils, which can be used for the initial identification of spill oils and afford useful information for the further identification of the oils.
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Received: 2009-03-18
Accepted: 2009-06-22
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Corresponding Authors:
SHAO Xue-guang
E-mail: xshao@nankai.edu.cn
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[1] Wang Z, Yang C, Hollebone B, et al. Environmental Science & Technology, 2006, 40 (18): 5636.
[2] Hupp A M, Marshall L J, Campbell D I, et al. Analytica Chimica Acta, 2008, 606(2): 159.
[3] Sun P, Bao M, Li G, et al. Journal of Chromatography A, 2009, 1216 (5): 830.
[4] Fernandez-Varela R, Andrade J M, Muniategui S, et al. Marine Pollution Bulletin, 2008, 56(2): 335.
[5] Mattson J S, Mark H B, Kolpack R L, et al. Analytical Chemisty, 1970, 42 (2): 234.
[6] Christensen J H, Hansen A B, Mortensen J. Analytical Chemisty, 2005, 77(7): 2210.
[7] Mansuy L, Philp R P, Allen J. Environmental Science & Technology, 1997, 31 (12): 3417.
[8] Villalobos M, Avila-Forcada A P, Gutierrez-Ruiz M E. Water Air Soil Pollution, 2008, 194(1-4): 151.
[9] Kawahara F K. Environmental Science & Technology, 1969, 3(2): 150.
[10] Permanyer A, Douifi L, Dupuy N, et al. Fuel, 2005, 84(2-3): 159.
[11] Permanyer A, Rebufa C, Kister J. Journal of Petroleum Science and Engineering, 2007, 58(3-4): 464.
[12] Fernandez-Varela R, Suarez-Rodriguez D, Gomez-Carracedo M P, et al. Talanta, 2005, 68(1): 116.
[13] Fernandez-Varela R, Gomez-Carracedo M P, Fresco-Rivera P, et al. Talanta, 2006, 69(2): 409.
[14] Fresco-Rivera P, Fernandez-Varela R, Gomez-Carracedo M P, et al. Talanta, 2007, 74(2): 163.
[15] Geladi P, MacDougall D, Martens H. Applied Spectroscopy, 1985, 39(3): 491.
[16] Shao X G, Leung A K M, Chau F T. Accounts of Chemical Research, 2003, 36(4): 276.
[17] Ma C X, Shao X G. Journal of Chemical Information and Computer Science, 2004, 44(3): 907.
[18] CHU Xiao-li, YUAN Hong-fu, LU Wan-zhen(褚小立，袁洪福，陆婉珍). Progress in Chemistry(化学进展), 2004, 16(4): 528.
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