DOI: 10.1016/j.marpolbul.2016.01.001
Scopus记录号: 2-s2.0-84962829109
论文题名: Oil species identification technique developed by Gabor wavelet analysis and support vector machine based on concentration-synchronous-matrix-fluorescence spectroscopy
作者: Wang C. ; Shi X. ; Li W. ; Wang L. ; Zhang J. ; Yang C. ; Wang Z.
刊名: Marine Pollution Bulletin
ISSN: 0025-326X
EISSN: 1879-3363
出版年: 2016
卷: 104, 期: 2018-01-02 起始页码: 322
结束页码: 328
语种: 英语
英文关键词: Concentration-synchronous-matrix-fluorescence (CSMF)
; Gabor wavelet analysis
; Oil spill
; Partial least square analysis (PLS)
; Principle component analysis (PCA)
; Supported vector machine (SVM)
Scopus关键词: Cost effectiveness
; Crude oil
; Drug products
; Feature extraction
; Fluorescence
; Fluorescence spectroscopy
; Oil spills
; Petroleum analysis
; Support vector machines
; Wavelet analysis
; Weathering
; Controlled laboratories
; Feature extraction methods
; Gabor wavelets
; Identification techniques
; Oil species identifications
; Partial least square analysis
; Principle component analysis
; Supported vector machines
; Principal component analysis
; diesel fuel
; fuel
; petroleum
; concentration (composition)
; crude oil
; fluorescence spectroscopy
; least squares method
; oil spill
; principal component analysis
; support vector machine
; wavelet analysis
; Article
; chemical analysis
; China
; concentration synchronous matrix fluorescence spectroscopy
; extraction
; fluorescence spectroscopy
; Gabor wavelet analysis
; intermethod comparison
; oil spill
; partial least squares regression
; principal component analysis
; support vector machine
; wavelet analysis
; weathering
; Bohai Sea
; China
; Pacific Ocean
; Yellow Sea
Scopus学科分类: Agricultural and Biological Sciences: Aquatic Science
; Earth and Planetary Sciences: Oceanography
; Environmental Science: Pollution
英文摘要: Concentration-synchronous-matrix-fluorescence (CSMF) spectroscopy was applied to discriminate the oil species by characterizing the concentration dependent fluorescence properties of petroleum related samples. Seven days weathering experiment of 3 crude oil samples from the Bohai Sea platforms of China was carried out under controlled laboratory conditions and showed that weathering had no significant effect on the CSMF spectra. While different feature extraction methods, such as PCA, PLS and Gabor wavelet analysis, were applied to extract discriminative patterns from CSMF spectra, classifications were made via SVM to compare their respective performance of oil species recognition. Ideal correct rates of oil species recognition of 100% for the different types of oil spill samples and 92% for the closely-related source oil samples were achieved by combining Gabor wavelet with SVM, which indicated its advantages to be developed to a rapid, cost-effective, and accurate forensic oil spill identification technique. © 2016 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/87197
Appears in Collections: 过去全球变化的重建 全球变化的国际研究计划
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作者单位: Department of Physics and Electronic Science, Weifang University, Weifang, China; College of Resources Science and Technology, Beijing Normal University, Beijing, China; Optics and Optoelectronics Laboratory, Ocean University of China, Qingdao, China; Emergencies Science and Technology Section(ESTS), Science and Technology Branch, Environment Canada, 335 River Rd., Ottawa, ON, Canada
Recommended Citation:
Wang C.,Shi X.,Li W.,et al. Oil species identification technique developed by Gabor wavelet analysis and support vector machine based on concentration-synchronous-matrix-fluorescence spectroscopy[J]. Marine Pollution Bulletin,2016-01-01,104(2018-01-02)