Measurement of Oil Pollutants by Three-Dimensional Fluorescence
Spectroscopy Combined With BP Neural Network and SWATLD
ZHU Yan-ping1, CUI Chuan-jin1*, CHENG Peng-fei1, 2, PAN Jin-yan1, SU Hao1, 2, ZHANG Yi1
1. College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210，China
2. Tangshan Key Laboratory of Semiconductor Integrated Circuits, Tangshan 063210，China
Abstract：With the increasing demand for oil resources by economic development, oil pollution problems have become increasingly serious, posing a huge threat to the ecological environment and human health. Therefore, accurate identification and timely treatment of oil pollutants is significant in reducing oil spill hazards. Petroleum is a complex organic compound mainly composed of aromatic hydrocarbons and their derivatives with strong fluorescence characteristics. Different types of petroleum contain different components and contents of polycyclic aromatic hydrocarbons. Three-dimensional fluorescence spectroscopy 3D-EEM is widely used to detect petroleum pollutants. Based on three-dimensional fluorescence spectroscopy, the improved wavelet threshold function and BP(backpropagation) neural network combined with the method of self-weighted alternating trilinear decomposition (SWATLAD) algorithm for qualitative and quantitative research on oil pollutants. The experiment used 0# diesel, 95# gasoline and kerosene as the research objects. Firstly, the samples were detected using an F-7000 fluorescence spectrometer, and the obtained data were processed by excitation, and emission correction. Secondly, an improved threshold function is proposed to solve the problem of signal discontinuity and excessive shrinkage of wavelet coefficients at the threshold of wavelet threshold denoising. The signal-to-noise ratio (SNR) and mean square error (MSE) are 18.354 7 and 10.261 7, respectively, which can more accurately restore useful signals. The preprocessed spectral data were trained by BP neural network based on error backpropagation. After training, the curve of the predicted value after training was in good agreement with the real value, indicating that the subsequent fluorescence data collected by the spectrometer can be directly input into the neural network to output the preprocessed data, simplifying the experimental operation steps. Finally, Finally, SWATLD was used to decompose the data processed by improved wavelet transform and BP neural network. The excitation and emission spectra of 0# diesel, 95# gasoline and kerosene obtained by the analysis were in good agreement with the real spectra, and the calculated average recoveries were 103.64%, 99.33% and 97.85%. It is proved that three-dimensional fluorescence spectroscopy combined with improved wavelet transform and BP neural network can detect fluorescent substances quickly and accurately.
朱燕萍，崔传金，程朋飞，潘金燕，苏 皓，张 怡. 三维荧光光谱结合BP神经网络与SWATLD检测油类污染物[J]. 光谱学与光谱分析, 2023, 43(08): 2467-2475.
ZHU Yan-ping, CUI Chuan-jin, CHENG Peng-fei, PAN Jin-yan, SU Hao, ZHANG Yi. Measurement of Oil Pollutants by Three-Dimensional Fluorescence
Spectroscopy Combined With BP Neural Network and SWATLD. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2467-2475.
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