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Human health and ecological environment are badly affected due to oil pollution. A novel strategy for identifying oil pollutants has been proposed based on generic modular design. A total of three modules are included in the oil identification strategy and each module is consisted of a series of steps. The different steps of each module were experimented and evaluated by excitation-emission matrix fluorescence spectroscopy data set of oil. The experimental results show that the average accuracy with 13.6% was improved by using the histogram equalization than using thresholding in module 1. The average accuracy with 5% was improved by using the low-order Zernike moments than using high-order Zernike moments in module 2. The average accuracy with 28.9% was improved by using angle similarity measure in the nearest-neighbor classifier compared to the other six in module 3. The optimal accuracy with 95% was obtained by combining the margin features of excitation-emission matrix fluorescence spectroscopy extracted by low-order Zernike moments with the nearest-neighbor classifier applied to angle similarity measure. The combination also has a good specificity and sensitivity. The results provide references for identifying oil pollutants.

A Novel Strategy for Identifying Oil Pollutants Based on Excitation-Emission Matrix Fluorescence Spectroscopy and Zernike Moments