光谱学与光谱分析 |
|
|
|
|
|
Discrimination of Crude Oil Samples Using Laser-Induced Time-Resolved Fluorescence Spectroscopy |
HAN Xiao-shuang1, 2, LIU De-qing1, LUAN Xiao-ning1, GUO Jin-jia1, LIU Yong-xin2, ZHENG Rong-er1* |
1. Optics and Optoelectronics Laboratory, Ocean University of China, Qingdao 266100, China2. College of Electronic Information Engineering, Inner Mongolia University, Huhhot 010021, China |
|
|
Abstract The Laser-induced fluorescence spectra combined with pattern recognition method has been widely applied in discrimination of different spilled oil, such as diesel, gasoline, and crude oil. However, traditional three-dimension fluorescence analysis method, which is not adapted to requirement of field detection, is limited to laboratory investigatio ns. The development of oil identification method for field detection is significant to quick response and operation of oil spill. In this paper, a new method based on laser-induced time-resolved fluorescence combined with support vector machine (SVM) model was introduced to discriminate crude oil samples. In this method, time-resolved spectra data was descended into two dimensions with selecting appropriate range in time and wavelength domains respectively to form a SVM data base. It is found that the classification accurate rate increased with an appropriate selection. With a selected range from 54 to 74 ns in time domain, the classification accurate rate has been increased from 83.3% (without selection) to 88.1%. With a selected wavelength range of 387.00~608.87 nm, the classification accurate rate of suspect oil was improved from 84% (without selection) to 100%. Since the detection delay of fluorescence lidar fluctuates due to wave and platform swing, the identification method with optimizing in both time and wavelength domains could offer a better flexibility for field applications. It is hoped that the developed method could provide some useful reference with data reduction for classification of suspect crude oil in the future development.
|
Received: 2014-10-31
Accepted: 2015-02-25
|
|
Corresponding Authors:
ZHENG Rong-er
E-mail: rzheng@ouc.edu.cn
|
|
[1] Brown C E, Fingas M F. Marine Pollution Bulletin, 2003, 47(9): 477. [2] LI Xiao-long, ZHAO Chao-fang, QI Min-jun, et al(李晓龙, 赵朝方, 齐敏珺, 等). Periodical of Ocean University of China(中国海洋大学学报), 2010, (8): 145. [3] ZHAO Chao-fang, LI Xiao-long, MA You-jun(赵朝方, 李晓龙, 马佑军). Infrared and Laser Engineering(红外与激光工程), 2011, 40(7): 1263. [4] Leifer I, Lehr W J, Beatty D S, et al. Remote Sensing of Environment, 2012, 124: 185. [5] Brown C E, Fingas M F. Marine Pollution Bulletin, 2003, 47(9): 477. [6] Li J, Fuller S, Cattle J, et al. Analytica Chimica Acta, 2004, 514(1): 51. [7] Brown C E, Marois R, Fingas, et al. International Oil Spill Conference, 2001. 917. [8] LIN Bin, AN Ju-bai(林 彬,安居白). Marine Environmental Science(海洋环境科学), 2004, 23(1): 47. [9] Christensen J H, Hansen A B, Mortensen J, et al. Analytical Chemistry, 2005, 77(7): 2210. [10] Alostaz M, Biggar K, Donahue R, et al. Journal of Environmental Engineering and Science, 2008, 7(3): 183. [11] LIU De-qing, LUAN Xiao-ning, HAN Xiao-shuang, et al (刘德庆, 栾晓宁, 韩晓爽, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(6): 1582.
|
[1] |
SUN Yan-wen1, CHANG Yu2, JIN Yu-fen1, XIE Wen-bing2, CHANG Jing1, YU Ting1*, PAN Li-hua2. Study of Synthesis and Spectral Property of Europium Cryptate[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2189-2193. |
[2] |
ZHANG Jing, LIU Zhong-bao*, SONG Wen-ai, FU Li-zhen, ZHANG Yong-lai. Stellar Spectra Classification Method Based on Multi-Class Support Vector Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2307-2310. |
[3] |
LI Yun1,2,3, ZHANG Ji1,2, LIU Fei4, XU Fu-rong3, WANG Yuan-zhong1,2*, ZHANG Jin-yu1,2,3*. Prediction of Total Polysaccharides Content in P. notoginseng Using FTIR Combined with SVR[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1696-1701. |
[4] |
YU Hui-ling1, MEN Hong-sheng2, LIANG Hao2, ZHANG Yi-zhuo2*. Near Infrared Spectroscopy Identification Method of Wood Surface Defects Based on SA-PBT-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1724-1728. |
[5] |
ZHOU Mu-chun1, ZHAO Qi1, CHEN Yan-ru1, SHAO Yan-ming2. Carbon Content Measurement of BOF by Radiation Spectrum Based on Support Vector Machine Regression[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1804-1808. |
[6] |
MA Jun-cheng1, DU Ke-ming1*, ZHENG Fei-xiang1, ZHANG Ling-xian2, SUN Zhong-fu1. A Segmenting Method for Greenhouse Cucumber Downy Mildew Images Based on Visual Spectral and Support Vector Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1863-1868. |
[7] |
ZHANG Yan-jun, ZHANG Fang-cao, FU Xing-hu*, XU Jin-rui. Raman Spectra Based on QPSO-MLSSVM Algorithm to Detect the Content of Four Components Blent Oil[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1437-1443. |
[8] |
CHANG Meng-fang1, JIA Meng-hui2, LI Lei1, CHEN Jin-quan1, XU Jian-hua1*. Time-Resolved Fluorescence and Thermodynamic Properties of Staphylococcal Nuclease[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1451-1457. |
[9] |
ZHANG Hao-bo1, LIU Tai-ang2, SHU Ru-xin1, YANG Kai1, YE Shun1, YOU Jing-lin2, GE Jiong1*. Using EN-NIR with Support Vector Machine for Classification of Producing Year of Tobacco[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1620-1625. |
[10] |
PENG Cheng1, FENG Xu-ping2*, HE Yong2, ZHANG Chu2, ZHAO Yi-ying2, XU Jun-feng1. Discrimination of Transgenic Maize Containing the Cry1Ab/Cry2Aj and G10evo Genes Using Near Infrared Spectroscopy (NIR)[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1095-1100. |
[11] |
SUN Tong, LIU Jin, GAN Lan-ping, WU Yi-qing, LIU Mu-hua*. Detection of Dimethoate Content with Laser Induced Breakdown Spectroscopy Combined with LSSVM and Internal Standard Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1251-1255. |
[12] |
CHEN Xi-ai1,2,3, WU Xue1, ZHANG Song1, WANG Ling1. Study of Plant Growth Regulators Detection Technology Based on Terahertz Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(03): 665-669. |
[13] |
FU Cai-li1, LI Ying1, CHEN Li-fan1, WANG Shao-yun1, WANG Wu2*. Rapid Detection of Lotus Seed Powder Based on Near Infrared Spectrum Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(02): 424-429. |
[14] |
CHEN Si-yu1, ZHANG Shu-hui2, ZHANG Shu1, TAN Zuo-jun1*. Detection of Early Tiny Bruises in Apples using Confocal Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(02): 430-435. |
[15] |
DONG Jia-lin1, HONG Ming-jian1, 3*, ZHENG Xiang-quan2, 3, XU Yi2, 3. Discrimination of Human, Dog and Rabbit Blood Using Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(02): 459-466. |
|
|
|
|