Determination of 1-Naphthol and 2-Naphthol Based on Fluorescence Spectrometry Combined with Improved FastICA-SVR
WANG Yu-tian1, LIU Ling-fei1*, ZHANG Li-juan1,2, ZHANG Zheng-shuai1, LIU Ting-ting1, WANG Shu-tao1, SHANG Feng-kai1
1. Measurement Technology and Instrument Key Lab of Hebei Province，Yanshan University，Qinhuangdao 066004, China
2. Hebei University of Environmental Engineering, Qinhuangdao 066102，China
Abstract：As the source of life, water is closely related to the survival of human beings. In recent years, there have been more and more reports on water pollution. Water pollution has become a serious problem, which can not be ignored. Two isomers of naphtol, 1-naphthol and 2-naphthol, were used as the research object in the experiment, and a new algorithm, which was used for qualitative and quantitative analysis of naphthol in water by analyzing the three-dimensional fluorescence spectrum of the mixture, was proposed. Using FS920 steady-state fluorescence spectrometer to scan the mixed solution and get the required experimental data. Then, a series of preprocessing steps for data are needed to remove the effects of Raman scattering and Rayleigh scattering. Independent component analysis (ICA) which is always used to solve the problem of blind source separation (BSS) will be applied to solve the problem in quantitative and qualitative analysis of fluorescence spectrum. BBS is an algorithm that uses the measured mixed signals as the processing objects to realize the decomposion of the source signals in the unknown system, as well as, to get the mixed matrix. The problem in identification and measurement of a single substance in a mixture is similar to the problem in blind source separation. The fast independent component analysis (FastICA) algorithm based on the maximum negative entropy is used to decompose the experimental data. The three-dimensional fluorescence data of all samples need to be expanded into a vector along the direction of the emission wavelength, and a matrix whose size is N×M can be obtained (N is the number of samples and M is the number of wavelength). This matrix is used as the input of fast independent component analysis to extract independent component, and the output is the expansion fluorescence spectrum of the single component material and a mixed matrix. The key to the fast independent component analysis algorithm is using Newton iterative algorithm to obtain the solution matrix, but the complex derivation of iteration process makes this algorithm have some problems, such as large computation and slow iteration. In order to overcome the shortcomings of fast independent component analysis, the differential method, also called double point chord cut method, is proposed to replace the complex derivation problem in the iterative process. In order to verify the feasibility of the algorithm, five times independent component extraction experiments were carried out on the spectral data with the improved algorithm and five times independent component extraction experiments were carried out on the spectral data with the original algorithm. The average running time of original FastICA algorithm is 17.78 seconds, and improved FastICA algorithm is 3.22 seconds, which is 14.56 seconds lower than original algorithm. The experiment result proves that differential method instead of the complex derivation problem in the iterative process can effectively reduce the amount of calculation and improve the speed of the iteration of the fast independent component analysis algorithm and the convergence is more stable. It can be seen from the experiment result that the fluorescence spectrum which was obtained by the decomposition are closer to the real spectrum. The mixture matrix obtained by FastICA is related to concentration matrix, which is the basis for quantitative analysis of materials. But the relationship between the mixture matrix and the concentration matrix may be nonlinear. Therefore, it is necessary to take the nonlinear fitting method to realize the fitting between the two. Support vector regression (SVR) machine can realize nonlinear regression, so SVR will be used to obtain predicted concentration. The mixed matrix decomposed and the actual concentration matrix are as the input and output of support vector regression machine respectively. The parameters of SVR are crucial to the prediction. Genetic algorithm (GA) is used to optimize the parameters and radial basis function (RBF function) is selected as the kernel function of SVR. Then the regression model is established by using the algorithm to realize quantitative analysis of the fluorescence spectrum. The fitting correlation coefficient (r) of 1-naphthol is 0.998 6 and 2-naphthol is 0.998 8; the recovery rate of 1-naphthol is 96.6%~104.2% and 2-naphthol is 96.8%～105.5%; the prediction of root mean square error (RMSEP) of 1-naphthol is 0.119 μg·L-1 and 2-naphthol is 0.100 μg·L-1. The results of the prediction are satisfactory and meet the requirements of the prediction. The experiment proved that the improved fast independent component analysis algorithm based on negative entropy combined with support vector regression algorithm can accurately identify and measure 1-naphthol and 2-naphthol in mixture, and this algorithm can also increase the speed of analysis for the hybrid system.
王玉田，刘凌妃，张立娟，张正帅，刘婷婷，王书涛，商凤凯. 改进的FastICA-SVR结合荧光光谱技术测定1-萘酚、2-萘酚[J]. 光谱学与光谱分析, 2019, 39(01): 142-149.
WANG Yu-tian, LIU Ling-fei, ZHANG Li-juan, ZHANG Zheng-shuai, LIU Ting-ting, WANG Shu-tao, SHANG Feng-kai. Determination of 1-Naphthol and 2-Naphthol Based on Fluorescence Spectrometry Combined with Improved FastICA-SVR. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(01): 142-149.
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