20 November 2009 Identification of oil spills by near-infrared spectroscopy (NIR) and support vector machine (SVM)
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Abstract
The identification of the spilled oil is an essential and important part in the investigation and handling of oil spill accidents. The combination of near-infrared spectroscopy (NIR) and chemometrics is ideal for such a situation. NIR spectroscopy is a powerful and effective technique and qualitative information can be obtained with classification models. Support vector machines (SVM) have been introduced recently in chemometrics and have proven to be powerful in NIR spectra classification tasks, such as material identification and food discrimination. In this work, the SVM is utilized to classify near infrared spectroscopy of simulated spilled oils of gasoline, diesel fuel and kerosene on the marine. A good classification performance is obtained :the identification rate were 100%, 96% and 98% on the test sets respectively.
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Weihong Bi, Ailing Tan, Yong Zhao, Meijing Gao, "Identification of oil spills by near-infrared spectroscopy (NIR) and support vector machine (SVM)", Proc. SPIE 7511, 2009 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems, 75111R (20 November 2009); doi: 10.1117/12.838054; https://doi.org/10.1117/12.838054
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