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Identification of Marine Oil Spills by Fisher Discriminant Based on Discrete Wavelet Transform |
LIU Xiao-xing, WEI Qi-gong, WANG Si-tong, HUANG Yi, ZHANG Ting-ting, QI Chao-yue, LI Jia |
College of Environmental Sciences & Engineering, Dalian Maritime University, Dalian 116026, China |
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Abstract The fluorescence characteristics of 8 kinds of fuel oil, 7 kinds of Middle East crude oil and 14 kinds of non-Middle East crude oil were analyzed by a constant-wavelength synchronous fluorescence spectrometry. Discrete wavelet transform and Fisher discriminant were combined to establish a model for the identification of marine oil spills. Twenty-nine kinds of oil before and after weathering had typical fluorescence peaks at the wavelength of (280±2), (302±2), (332±2) and (380±2) nm, but the dispersion degree of fluorescence intensity of weathered oil at (380±2) nm was too high, which was not suitable for the identification of oil species. The db7 wavelet basis function was used to resolve 6 levels for fluorescence spectra of 29 kinds of original oil samples, and the d3 detailed coefficient characteristics were extracted. The wavelet coefficients corresponding to (255±2), (280±2), (302±2), (332±2) and (354±2) nm were determined which were used to establish the Fisher discriminant model. All of oil samples all had extreme points at (280±2) nm, the wavelet coefficients of marine fuels were between 44.06±5.62, and that of crude oils were between 22.47±5.12. These two wavelet coefficients could be used to distinguish the marine fuel and crude oil. The established Fisher discriminant model distinguished not only marine fuel and crude oil but also further Middle East crude oil. The P-values corresponding to Wilks’s lambda distribution were 0 and 0.02, respectively, which indicated the model was feasible. The validated results of the model showed that the identification accuracy reached 96.6% for modeling oils after weathering and 95.7% for 23 kinds of non-modeling oils. Since adjusted cosine similarity of modeling oil before and after weathering was ranged from 0.91 to 0.98, the identification model established by the original oils can also be used for the identification of weathered oil species.
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Received: 2016-12-30
Accepted: 2017-03-26
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[1] Weng N, Wan S, Wang H, et al. Journal of Chromatography A, 2015, 1398: 94.
[2] Greene L V, Elzey B, Franklin M, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2017, 174: 316.
[3] Li J, Fuller S, Cattle J, et al. Analytica Chimica Acta, 2004, 514(1): 51.
[4] LIU Xiao-xing, LIU Zheng-jiang, ZHANG Shuo-hui, et al(刘晓星, 刘正江, 张硕慧, 等). Marine Environmental Science(海洋环境科学), 2013, 32(4): 605.
[5] Ventura G T, Hall G J, Nelson R K, et al. Journal of Chromatography A, 2011, 1218: 2584.
[6] Skov T, Ballabio D, Bro R. Analytica Chimica Acta, 2008, 615: 18.
[7] Wang Z, Yang C, Kelly-Hooper F, et al. Journal of Chromatography A, 2009, 1216: 1174.
[8] Wang C, Shi X, Li W, et al. Marine Pollution Bulletin, 2016, 104(1-2): 322.
[9] Ha D, Park D, Koo J, et al. Computers & Chemical Engineering, 2016, 94: 362. |
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