拉曼光谱结合偏最小二乘法定量分析水中聚乙烯和聚苯乙烯两种微塑料  

Raman Spectroscopy Combined with Partial Least Squares for Quantitative Analysis of Two Kinds of Microplastics in Water Samples

作　　者：丁建明 王鑫 张容玲 周利园 张天龙 汤宏胜 李华 DING Jian-Ming;WANG Xin;ZHANG Rong-Ling;ZHOU Li-Yuan;ZHANG Tian-Long;TANG Hong-Sheng;LI Hua(Key Laboratory of Synthetic and Natural Functional Molecular Chemistry,Ministry of Education,College of Chemistry&Materials Science,Northwest University,Xi′an 710127,China;Institute for Hygiene of Ordnance Industry,Xi′an 710065,China;College of Chemistry and Chemical Engineering,Xi′an Shiyou University,Xi′an 710065,China)

机构地区：[1]西北大学化学与材料科学学院,合成与天然功能分子化学教育部重点实验室,西安710127 [2]兵器工业卫生研究所,西安710065 [3]西安石油大学化学化工学院,西安710065

出　　处：《分析化学》2024年第10期1581-1590,共10页Chinese Journal of Analytical Chemistry

基　　金：国家自然科学基金项目(Nos.22173071,22073074)资助。

摘　　要：微塑料(MPs)作为一种水环境的新兴污染物,具有极性结构、粒径小(通常小于5 mm)、比表面积大、稳定性好和生物降解难等特点,能够对水生生物的正常生理活动产生不利影响,并在包括人类在内的生物体内积聚,因此亟需建立水环境中MPs的快速且准确的定量分析方法。本研究将拉曼光谱结合偏最小二乘(Partial least squares,PLS)法用于实际水样中聚乙烯(Polyethylene,PE)和聚苯乙烯(Polystyrene,PS)的快速准确的定量分析。首先,制备了33个含有不同浓度MPs的模拟水样,并采集其拉曼光谱;其次,考察了6种光谱预处理方法(归一化、多元散射校正、标准正态变换、一阶导数、二阶导数和小波变换)对PLS校正模型预测性能的影响,并分别采用协同区间偏最小二乘算法(Synergy interval partial least squares,SiPLS)、变量重要性投影(Variable importance in projection,VIP)和互信息(Mutual information,MI)3种变量选择方法对PLS校正模型的输入变量进行选择与优化,采用留一法对PLS校正模型的预测能力进行考察与验证。在最优的光谱预处理、变量选择、输入变量和潜变量等条件下,建立了基于蒸馏水的小波变换-偏最小二乘(WT-PLS)校正模型,并对实际水样中PE和PS的含量进行了预测(PE和PS的预测相关系数(R2 p)分别为0.9540和0.8472,预测误差(Errorp)分别为0.0690和0.1126);建立了基于混合水样的MI-PLS校正模型,对实际水样具有最佳的预测性能(PE和PS的R2 p分别为0.9776和0.9755,Errorp分别为0.0360和0.0392)。本方法为实际水样中MPs以及其它有机污染物的定量分析提供了新思路与新方法。Microplastics(MPs)are emerging contaminants in aquatic environments characterized by their polar structure,small particle size(Typically less than 5 mm),large surface area,good stability,and resistance to biodegradation.They pose adverse effects on the normal physiological activities of aquatic organisms and can accumulate in biota,including humans.Therefore,there is an urgent need for rapid and accurate quantitative analysis of MPs in water environments.In this study,Raman spectroscopy combined with partial least squares(PLS)was employed for rapid and accurate quantitative analysis of polyethylene(PE)and polystyrene(PS)MPs in real water samples.Initially,33 simulated water samples containing different concentrations of MPs were prepared,and their Raman spectra were collected.Six spectral preprocessing methods(Normalization,multiplicative scatter correction,standard normal variate transformation,first derivative,second derivative,and wavelet transform)were investigated for their impact on the predictive performance of PLS calibration models.Subsequently,three variable selection methods including synergy interval partial least squares(SiPLS),variable importance in projection(VIP)and mutual information(MI)were employed to optimize the input variables of the PLS calibration model.The predictive capability of the PLS calibration model was evaluated and validated using leave-one-out crossvalidation.Under the optimal conditions of spectral preprocessing,variable selection,input variables and latent variables,the wavelet transform-partial least squares(WT-PLS)calibration model based on distilled water was established,and the contents of PE and PS in real water samples were predicted with prediction correlation coefficients(R2 p)of 0.9540 and 0.8472 for PE and PS,respectively,and prediction errors(Errorp)of 0.0690 and 0.1126,respectively.Furthermore,a mixed sample MI-PLS calibration model was developed,demonstrating the best predictive performance in real water samples(With R2 p values of 0.9776 and 0.9755 for PE and PS,

关 键 词：微塑料 偏最小二乘法 拉曼光谱 化学计量学 

分 类 号：O657.37[理学—分析化学] X832[理学—化学]