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
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摘要
多环芳烃(PAHs)具有强致癌性,威胁人类身体健康。在复杂水质检测环境中,利用荧光光谱检测PAHs浓度时,由于测量光谱中存在瑞利散射影响,使得PAHs光谱信号包含明显的非平稳噪声,常用的多次采样求均值法容易使PAHs光谱存在明显的测量误差,导致PAHs检测精度下降。为此,提出了一种基于3D荧光光谱分析和多维偏最小二乘(N-PLS)的PAHs浓度优化检测方法,首先分析了菲、芴、苊与荧蒽4种PAHs溶液的光谱特性,通过拟合散射带数据点值消除光谱中的瑞利散射噪声,同时尽可能地保留原光谱信息。提取4种PAHs光谱的均值、方差和一维边际分布等特征参数,利用聚类分析方法对其光谱数据做样本分类,将相似光谱数据样本进行合并;然后根据校正集的光谱信号与不同PAHs浓度之间的关系,建立N-PLS模型,对各类PAHs的浓度进行预测分析,并且验证PAHs浓度与光谱数据荧光强度的关系;最后利用双线性分解对浓度残差进行修正,对含有各类PAHs的水溶液与实际水样进行浓度残差验证,分析了不同参数下PAHs的预测误差。实验结果表明,溶剂菲有2个明显的荧光峰值,激发与发射波长分别为285/245和315/345 nm;芴与荧蒽均存在6个明显的荧光特征峰值,分别为265/255, 325/345, 335/325, 365/355, 385/395和405/415 nm,且与其他PAHs的荧光峰值相距较远;溶液苊在发射波长300～485 nm的范围内存在连续波峰,且对应激发波长在255～360 nm范围内; N-PLS方法对不同水质环境下的PAHs预测误差较小,其中菲与芴均方根误差均小于0.4μg·L~(-1),相对误差小于6%,苊与荧蒽均方根误差均小于1.0μg·L~(-1),相对误差均小于9%。对4种不同的PAHs在河流中的扩散趋势进行了仿真分析,确定出了其扩散程度,其中芴与菲扩散速率约为51 mg·L~(-1),苊与荧蒽扩散速率为21 mg·L~(-1),且扩散速率在一定范围内呈线性增长趋势, PAHs与其浓度之间符合朗伯比尔定律的线性关系;通过不同迭代次数下N-PLS方法的均方根误差分析,得到了均方根误差精度最高时的迭代次数;对比了不同主因子数时N-PLS方法对PAHs预测的适应度与相关系数,结果表明当主因子数为3时,适应度可达96.5%,此时N-PLS预测模型效果最佳。相比其他检测方法,本文方法检测精度较高,回收率较好,具有较强的鲁棒性。
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(3 D) 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.
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(3 D) 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.
引文
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[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,Ibaňez 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.
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