Identification of Spill Oil Species Based on Low Concentration Synchronous Fluorescence Spectra and RBF Neural Network
LIU Qian-qian1, WANG Chun-yan1, 2, 3*, SHI Xiao-feng1, LI Wen-dong1, LUAN Xiao-ning1, HOU Shi-lin1, ZHANG Jin-liang2, ZHENG Rong-er1
1. Optics & Optoelectronics Laboratory, Ocean University of China, Qingdao 266100, China 2. College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China 3. Department of Physics & Electronics Science, Weifang University, Weifang 261061, China
Abstract：In this paper, a new method was developed to differentiate the spill oil samples. The synchronous fluorescence spectra in the lower nonlinear concentration range of 10-2~10-1 g·L-1 were collected to get training data base. Radial basis function artificial neural network (RBF-ANN) was used to identify the samples sets, along with principal component analysis (PCA) as the feature extraction method. The recognition rate of the closely-related oil source samples is 92%. All the results demonstrated that the proposed method could identify the crude oil samples effectively by just one synchronous spectrum of the spill oil sample. The method was supposed to be very suitable to the real-time spill oil identification, and can also be easily applied to the oil logging and the analysis of other multi-PAHs or multi-fluorescent mixtures.
刘倩倩1，王春艳1, 2, 3*，史晓凤1，李文东1，栾晓宁1，侯世林1，张金亮2，郑荣儿1 . 基于RBF神经网络的较低浓度下同步荧光光谱的溢油鉴别[J]. 光谱学与光谱分析, 2012, 32(04): 1012-1015.
LIU Qian-qian1, WANG Chun-yan1, 2, 3*, SHI Xiao-feng1, LI Wen-dong1, LUAN Xiao-ning1, HOU Shi-lin1, ZHANG Jin-liang2, ZHENG Rong-er1. Identification of Spill Oil Species Based on Low Concentration Synchronous Fluorescence Spectra and RBF Neural Network. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(04): 1012-1015.
[1] Wang Z D, Fingas M F. Marine Pollution Bulletin, 2003, 47(9-12): 423. [2] Wang Z D, Fingas M F, Page D S. Journal of Chromatography A, 1999, 843(1-2): 369. [3] CHEN Guo-hua, WANG Shu-mei, ZHAO Ru-xiang(陈国华, 王淑美, 赵如箱). Water Oil Pollution Governance(水体油污染治理). Beijing: Chemical Industry Press(北京: 化学工业出版社), 2002. [4] Deepa S, Sarathi R, Mishra A K. Talanta, 2006, 70(4): 811. [5] Li J F, Fuller S, Cattle J, et al. Analytica Chimica Acta, 2004, 514(1): 51. [6] Abbas O, Rebufa C, Dupuy N, et al. Fuel, 2006, 85(17-18): 2653. [7] Patra D, Mishra A K. Analytica Chimica Acta, 2002, 454(2): 209. [8] SONG Cheng-wen, LIU Yu, LI Ying, et al(宋成文，刘 瑀，李 颖，等). Ship&Ocean Engineering(航海工程), 2009, 38(3): 16. [9] Wang C Y, Li W D, Luan X N, et al. Talanta, 2010, 81(1-2): 684. [10] Bianchini M, Frasconi P, Gori M. IEEE Transactions On Neural Networks, 1995, 6(3): 749. [11] GAO Juan(高 隽). Artificial Neural Networks Principle and Simulation Examples(人工神经网络原理及仿真实例). Beijing: China Machine Press(北京: 机械工业出版社), 2003. [12] MA Jun, SHI Xiao-feng, ZHENG Rong-er, et al(马 君, 史晓凤, 郑荣儿, 等). Acta Laser Biology Sinica(激光生物学报), 2005, 14(6): 432. [13] WANG Chun-yan, DENG Mei-yin, YANG Xiao-ming, et al(王春艳, 邓美寅, 杨晓明, 等). Petroleum Exploration and Development(石油勘探与开发), 2006, 33(2): 205.
