Identification of spilled oils by NIR spectroscopy technology based on KPCA and LSSVM

Ailing Tan; Weihong Bi

doi:10.1117/12.900803

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8 September 2011 Identification of spilled oils by NIR spectroscopy technology based on KPCA and LSSVM

Ailing Tan, Weihong Bi

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Proceedings Volume 8193, International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications; 81933K (2011) https://doi.org/10.1117/12.900803
Event: International Symposium on Photoelectronic Detection and Imaging 2011, 2011, Beijing, China

Abstract

Oil spills on the sea surface are seen relatively often with the development of the petroleum exploitation and transportation of the sea. Oil spills are great threat to the marine environment and the ecosystem, thus the oil pollution in the ocean becomes an urgent topic in the environmental protection. To develop the oil spill accident treatment program and track the source of the spilled oils, a novel qualitative identification method combined Kernel Principal Component Analysis (KPCA) and Least Square Support Vector Machine (LSSVM) was proposed. The proposed method adapt Fourier transform NIR spectrophotometer to collect the NIR spectral data of simulated gasoline, diesel fuel and kerosene oil spills samples and do some pretreatments to the original spectrum. We use the KPCA algorithm which is an extension of Principal Component Analysis (PCA) using techniques of kernel methods to extract nonlinear features of the preprocessed spectrum. Support Vector Machines (SVM) is a powerful methodology for solving spectral classification tasks in chemometrics. LSSVM are reformulations to the standard SVMs which lead to solving a system of linear equations. So a LSSVM multiclass classification model was designed which using Error Correcting Output Code (ECOC) method borrowing the idea of error correcting codes used for correcting bit errors in transmission channels. The most common and reliable approach to parameter selection is to decide on parameter ranges, and to then do a grid search over the parameter space to find the optimal model parameters. To test the proposed method, 375 spilled oil samples of unknown type were selected to study. The optimal model has the best identification capabilities with the accuracy of 97.8%. Experimental results show that the proposed KPCA plus LSSVM qualitative analysis method of near infrared spectroscopy has good recognition result, which could work as a new method for rapid identification of spilled oils.

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Ailing Tan and Weihong Bi "Identification of spilled oils by NIR spectroscopy technology based on KPCA and LSSVM", Proc. SPIE 8193, International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, 81933K (8 September 2011); https://doi.org/10.1117/12.900803

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