Three-Dimensional Fluorescence Spectroscopy Combined with Alternating Weighted Residue Constraint Quadrilinear Decomposition Algorithm for Detection of Petroleum Mixed Oil
KONG De-ming1, ZHANG Chun-xiang1, CUI Yao-yao2*, LI Yu-meng1, WANG Shu-tao1, SHI Hui-chao3
1. School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
2. School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
3. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Abstract：As an important energy and industrial raw material, petroleum brings benefit to human beings and the environment pollution is increasingly serious. Therefore, rapid and accurate detection of mixed oil becomes an important content of identification of its source and protect ecological environment. Petroleum substances are generally composed of aromatic hydrocarbon and its derivatives with strong fluorescence characteristics, and fluorescence spectroscopy is an important means of detecting mixed oil with the advantages of high sensitivity, fast analysis and small weathering effects. And it has obtained good results for components identificationand concentration prediction of oil spill by various algorithms of second-order calibration algorithm and third-order calibration algorithm. Second-order calibration has the shortcomings of weak tolerance to noise, sensitivity to number of components, and limited real application of mixed oil detection. Aiming at these problems, a novel method is proposed to detect mixed oil in this paper based on the combination of three-dimensional fluorescence spectroscopy and alternating weighted residue constraint quadrilinear decomposition (AWRCQLD) algorithm. Firstly, using ethanol as a solvent, 7 calibration samples, 4 prediction samples and 3 blank samples of jet fuel and lube with different volume ratios were prepared. Secondly, the fluorescence spectra of 42 samples of the mixed oil at different experimental temperatures were obtained by FLS920 fluorescence spectrometer, and the effect of scattering was removed by using blank subtraction. Then, the optimum number of components was estimated by core consistency diagnosis and residual analysis. Finally, using AWRCQLD algorithm, 4-PARAFAC algorithm and second-order calibration algorithm to analyze the fluorescence spectra, and the qualitative identification and quantitative prediction of mixed oil samples were made. The research results show that the interval of the obtained recovery rate of jet fuel prediction samples is 96.7%~102.7%, and the root mean square error of prediction is 0.015 mg·mL-1; the interval of the obtained recovery rate of lube prediction samples is 96.9%~101.7%, and the root mean square error of prediction is 0.009 mg·mL-1. The four-dimensional response matrix constructed can more accurately determine the concentration of jet fuel and lube at different experimental temperatures, and the recovery rate is higher, the root mean square error is smaller, and can meet the requirements of accurate quantitative analysis. Compared with the second-order calibration algorithm and 4-PARAFAC algorithm, AWRCQLD algorithm can better reflect the superiority of the third-order calibration algorithm and the comprehensive prediction ability is stronger under seriously overlapped fluorescence spectra of jet fuel and lube. The purpose of rapid detection of mixed oil can be achieved by AWRCQLD algorithm. The study provides a rapid and accurate “mathematical separation” method to detect mixed oil not based on “physical and chemical separation”, and also provides a necessary technological support for detection of petroleum mixed oil.
孔德明，张春祥，崔耀耀，李雨蒙，王书涛，史慧超. 三维荧光光谱结合交替加权残差约束四线性分解算法对石油类混合油液的检测[J]. 光谱学与光谱分析, 2019, 39(10): 3129-3135.
KONG De-ming, ZHANG Chun-xiang, CUI Yao-yao, LI Yu-meng, WANG Shu-tao, SHI Hui-chao. Three-Dimensional Fluorescence Spectroscopy Combined with Alternating Weighted Residue Constraint Quadrilinear Decomposition Algorithm for Detection of Petroleum Mixed Oil. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(10): 3129-3135.
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