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Oil pollutants is one of the major pollution sources in water. Accurate, rapid, and convenient detection method of oil pollutants in water has very important theoretical value and practical significance. The combination of near-infrared spectroscopy (NIR) and chemometrics is ideal for such a situation. NIR spectroscopy is a powerful and effective technique. traditional NIR methods do not take full account of the absorbance data non-negative characteristics, resulting in the analysis lack of reasonable explanation. In this paper, the qualitative discriminate method of single species oil contaminants based on nonnegative matrix factorization feature extraction combined with support vector machine classification algorithm is studied. Non-negative matrix factorization algorithm and support vector machine classifier parameters on classification accuracy are discussed in depth to optimize NIR qualitative classification model. The present method has a good identification effect and strong generalization ability , and can work as a new method for rapid identification of oil pollutants in water. Keywords-NIR Spectroscopy; Non-negative matrix factorization; oil pollutans;; Support Vector Machine; Genetic

NIR Spectroscopy and NMF Algorithm for Identification of Oil Pollutants in Water