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采用吸光度-透光率和荧光激发-发射矩阵(A-TEEM)结合多元技术检测和定量地表水中的双酚A

Detection and Quantification of Bisphenol A in Surface Water Using Absorbance-Transmittance and Fluorescence Excitation-Emission Matrices (A-TEEM) Coupled with Multiway Techniques.

作者信息

Ingwani Thomas, Chaukura Nhamo, Mamba Bhekie B, Nkambule Thabo T I, Gilmore Adam M

机构信息

Institute for Nanotechnology and Water Sustainability, College of Engineering, Science and Technology, University of South Africa, Johannesburg 1709, South Africa.

Department of Physical and Earth Sciences, Sol Plaatje University, Kimberley 8300, South Africa.

出版信息

Molecules. 2023 Oct 12;28(20):7048. doi: 10.3390/molecules28207048.

Abstract

In the present protocol, we determined the presence and concentrations of bisphenol A (BPA) spiked in surface water samples using EEM fluorescence spectroscopy in conjunction with modelling using partial least squares (PLS) and parallel factor (PARAFAC). PARAFAC modelling of the EEM fluorescence data obtained from surface water samples contaminated with BPA unraveled four fluorophores including BPA. The best outcomes were obtained for BPA concentration (R = 0.996; standard deviation to prediction error's root mean square ratio (RPD) = 3.41; and a Pearson's r value of 0.998). With these values of R and Pearson's r, the PLS model showed a strong correlation between the predicted and measured BPA concentrations. The detection and quantification limits of the method were 3.512 and 11.708 micro molar (µM), respectively. In conclusion, BPA can be precisely detected and its concentration in surface water predicted using the PARAFAC and PLS models developed in this study and fluorescence EEM data collected from BPA-contaminated water. It is necessary to spatially relate surface water contamination data with other datasets in order to connect drinking water quality issues with health, environmental restoration, and environmental justice concerns.

摘要

在本实验方案中,我们使用三维荧光光谱(EEM)结合偏最小二乘法(PLS)和平行因子分析(PARAFAC)建模,测定了地表水样中添加的双酚A(BPA)的存在情况和浓度。对受双酚A污染的地表水样获得的EEM荧光数据进行PARAFAC建模,揭示了包括双酚A在内的四种荧光团。双酚A浓度的预测效果最佳(R = 0.996;预测误差的均方根与标准差之比(RPD)= 3.41;皮尔逊相关系数r值为0.998)。基于这些R值和皮尔逊相关系数r值,PLS模型显示预测的和实测的双酚A浓度之间具有很强的相关性。该方法的检测限和定量限分别为3.512和11.708微摩尔(µM)。总之,使用本研究中开发的PARAFAC和PLS模型以及从受双酚A污染的水中收集的荧光EEM数据,可以精确检测双酚A并预测其在地表水中的浓度。有必要将地表水污染数据与其他数据集进行空间关联,以便将饮用水质量问题与健康、环境恢复和环境正义问题联系起来。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f49/10609475/608d90dfb1ae/molecules-28-07048-g001.jpg

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