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

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

doi:10.3390/molecules28207048

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

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

Authors

Thomas Ingwani¹, Nhamo Chaukura², Bhekie B Mamba¹, Thabo T I Nkambule¹, Adam M Gilmore^{1

3}

Affiliations

¹ Institute for Nanotechnology and Water Sustainability, College of Engineering, Science and Technology, University of South Africa, Johannesburg 1709, South Africa.
² Department of Physical and Earth Sciences, Sol Plaatje University, Kimberley 8300, South Africa.
³ Horiba Instruments Incorporated Inc., Piscataway, NJ 08854, USA.

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 (R² = 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 R² 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.

Keywords: and validation; method development; optimisation; parallel factor modelling; partial least squares modelling.

Grants and funding

The authors are appreciative of the financial support secured from the University of South Africa.