Identification And Determination Of Oil In Water Based On Three-Dimensional Fluorescence Spectra Parameterization And Pattern Recognition

Mineral oils are the principal contaminations in water environment. Identifying the species of mineral oil in water and measuring its concentration are needed to determine the contamination oil source and evaluating pollution fast and correctly. It has important significance for water environment protection under control. Based on comprehensive analysis and synthesis of the present conditions, as well as its tendency of contamination oil fluorescent analysis techniques, this paper is aimed at identification and concentrate measurement of contamination mineral oil, as well as its application in water environment detection or monitoring. Based on experiment and parameterization of three-dimensional fluorescence spectra, oil pattern recognition using artificial neural network is presented. Based on fluorescence principle and its measurement technique, many kinds of mineral oils such as diesel, kerosene, engine oil and crude oils were detected and researched in excitation–emission fluorescence scanning experiments. Firsthand materials of large amount of fluorescence data were obtained, based on which many three-dimensional spectra were reconstructed visually, examined and analyzed by programmed data processing. Quantitative relationship between the maximal fluorescence value and concentration were discussed. The intensities curves of fluorescence versus concentration were given with reference lines companioned Parameterization based on apparent features of 3D fluorescence spectra was studied, and apparent statistic parameters of various oils were extracted and calculated. The effect of some factors such as solvents on three-dimensional spectra was visually discussed by parameter clustering for typical samples (distilled or pure water was selected as the background), and by this way, the effectiveness of parameterization was demonstrated. However the limitation of the parameters based on appearance manifested. To find sophisticated feature parameters forming profound feature vector with more physical meaning, which should be able to identify various or complex contamination oils with similar or overlapping fluorescence spectra, deeper data mining was carried on. As the result of systematic clustering and validity comparing, the singular values of the excitation-emission matrix (EEM) were selected as the identification information. The concentration information is get independent to the stability of excitation light as well as the system gain by making use of the second-order dispersion light working as a surveyor's pole. By this way, the accuracy of concentration measurement can be improved. By associating species and concentration information, associated features of three-dimensional fluorescence spectra of oil have been made up of, providing foundation for solving the problem of both qualitative and quantitative analysis of contamination oil in water. A double neural network has been designed to implement qualitative and quantitative processing together. The result of identification is fed back to the input of concentration net, wherein the identification result and relative fluorescence intensity are fused to predict oil concentration of corresponding species. At last, with associated features as the pattern input, various samples of contamination oils in water have been identified correctly and measured with an acceptable accuracy.