Abstract：Polycyclic aromatic hydrocarbons (PAHs) have strong carcinogenicity and threaten human health. In the complex water quality detection environment, when the concentrations of PAHs are detected by fluorescence spectrum, the spectral signal may contain obvious non-stationary noise due to the influence of Rayleigh scattering in the measured spectrum. The common multiple sampling and averaging method tend to generate obvious measurement error in the PAHs spectrum, thereby leading to low detection accuracy of PAHs. In this paper, an optimal detection method for PAHs concentration based on three-dimensional (3D) fluorescence spectral analysis and N-way partial least square (N-PLS) is proposed. First, the spectral features of the four PAHs solutions of phenanthrene, fluorene, acenaphthene and fluoranthene were analyzed. The Rayleigh scattering noise in the spectrum was eliminated by fitting the scattering band data point values while the original spectrum information was preserved as much as possible. The features such as the mean, variance, and one-dimensional marginal distribution of the four PAHs spectrum were extracted，and the similar spectral data samples were merged according to samples classification of four spectral data by feature clustering analysis. Secondly, the N-PLS model was established based on the relationship between the spectral signal of the correction set and the different PAHs concentration. Subsequently the N-PLS model was used to predict and analyze the concentration of various PAHs, and verify the relationship between the PAHs concentration and the fluorescence intensity of the spectral data. Finally, the concentration residuals were modified by bilinear decomposition, the concentration residuals between aqueous solutions containing various PAHs and real water samples were verified, and the prediction errors of PAHs under different parameters were also analyzed. The experimental results showed that the phenanthrene solvent exists two obvious fluorescence peaks, and their excitation and emission wavelengths are 285/245 and 315/345 nm respectively. Both fluorene and fluoranthene have six obvious fluorescence feature peaks. Their excitation and emission wavelengths are 265/255, 325/345, 335/325, 365/355 nm, 385/395 and 405/415 nm respectively. Moreover, the fluorescence peaks are far away from the other PAHs. There appear continuous peaks in the acenaphthene solution where the emission wavelength is in the range of 300~485 nm, and the corresponding excitation wavelength is 255~360 nm. The PAHs prediction error of N-PLS method for different water quality is small, where the RMS error of phenanthrene and fluorene are less than 0.4 μg·L-1, the relative error is less than 6%, and the RMS error of acenaphthene and fluoranthene are less than 1.0 μg·L-1, their relative error are less than 9%. The diffusion degree of PAHs is determined by the simulation and analysis of the diffusion tendency of four different kinds of PAHs in river, where the diffusion rate of fluorene and phenanthrene is about 51 mg·L-1, and acenaphthene and fluoranthene is 21 mg·L-1. Their diffusion rate is linear in a certain range and there is a linear relationship between PAHs and its concentration in accordance with Lambert-Beer law. The iteration times with the highest RMS error accuracy are obtained through the RMS error analysis of N-PLS method with different iteration times. The fit and correlation coefficient of the N-PLS method for PAHs prediction with different main factor numbers are compared The results showed that when the number of main factors is 3, the fitness could be up to 96.5, and the effect of N-PLS prediction model is optimal. Overall, the proposed method has higher detection accuracy, better recovery rate and stronger robustness compared with other detection methods.
王小鹏，麻文刚，蔡祥云，吴 旭，朱天亮. 基于3D荧光光谱分析和多维偏最小二乘的PAHs浓度优化检测[J]. 光谱学与光谱分析, 2019, 39(06): 1798-1805.
WANG Xiao-peng, MA Wen-gang, CAI Xiang-yun, WU Xu, ZHU Tian-liang. Optimal Detection of PAHs Concentration Based on 3D Fluorescence Spectral Analysis and N-Way Partial Least Square. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(06): 1798-1805.
[1] YAN Rui，SHAO Ming-yuan，SUN Chang-hua，et al（闫 蕊, 邵明媛, 孙长华, 等）. Chinese Journal of Analytical Chemistry(分析化学), 2014, （6）: 897.
[2] LI Zhi-gang(李志刚). Journal of Textile Research(纺织学报), 2015, 36(5): 69.
[3] SHI Long-kai, LIU Yu-lan(石龙凯, 刘玉兰). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2015, 30(12): 114.
[4] WANG Shu-tao, ZHENG Ya-nan, WANG Zhi-fang, et al(王书涛, 郑亚南, 王志芳, 等). Chinese Journal of Luminescence(发光学报), 2017, 38(6): 807.
[5] WANG Yu-tian, LIU Ting-ting, LIU Ling-fei, et al(王玉田, 刘婷婷，刘凌妃, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(4): 1171.
[6] YANG Rui-fang, ZHAO Nan-jing, XIAO Xue, et al(杨瑞芳, 赵南京, 肖 雪, 等). Journal of Atmosphere and Environmental Optics(大气与环境光学学报), 2015, 10(5): 386.
[7] DU Shu-xin, SHEN Jin-chang, YUAN Zhi-bao(杜树新, 沈进昌, 袁之报). Laser Journal(激光杂志), 2012, 33(1): 36.
[8] Sara Mostafapour, Hadi Parastar. Analytical and Bioanalytical Chemistry, 2015, 407(1): 285.
[9] LI Ai-min，LIAN Zeng-yan，YANG Ren-jie, et al(李爱民, 连增艳, 杨仁杰, 等). Environmental Chemistry(环境化学)，2018, 37(4): 910.
[10] LIU Tie, LIU De-long, WEI Yong-ju(刘 铁, 刘德龙, 魏永巨). Journal of Instrumental Analysis(分析测试学报), 2018, 37(3): 313.
[11] YANG Ren-jie, DONG Gui-mei, YANG Yan-rong, et al(杨仁杰, 董桂梅, 杨延荣, 等). Optics and Precision Engineering(光学 精密工程)，2016, 24(11): 2665.
[12] TAO Chun-xian，RUAN Jun, SHU Shun-peng, et al(淘春先, 阮 俊, 舒顺朋, 等). Chinese Journal of Lasers(中国激光), 2016, 43(1): 0115001.
[13] Lozano V A, Ibañez G A, Olivieri A C. Analytica Chimica Acta, 2008, 610 (2): 186.
[14] Mouazen A M, Kuang B, de Baerdemaeker J, et al. Geoderma, 2010, 158(1): 23.
