Fluorescence-kinetic four-way data generation and modeling for abacavir determination in water samples

Abstract

The aim of this study was to develop a fluorescence-detection based analytical methodology consisting in the photolysis of abacavir induced by UV irradiation to determine the concentration of the analyte in complex samples. Initially, with the goal of maximizing the fluorescence signal, the optimal experimental conditions were determined by means of design of experiments using the response surface methodology. Once these conditions were established, two types of data were generated: excitation-emission fluorescence matrices acquired after UV irradiation of the analyte (second-order data), and excitation-emission fluorescence matrices recorded at different irradiation times (third-order data), which were processed using both multivariate curve resolution by alternating least squares (MCR-ALS) and parallel factor analysis (PARAFAC) algorithms. These strategies were carried out with the objective of assessing the chemometric advantages in terms of predictive ability by increasing the order of the data, and improvement in figures of merit, such as sensitivity and limits of detection and quantitation. Finally, test samples, containing the analyte and potential interferences, and water samples were evaluated. This study allowed the development of a simple and sensitive methodology for the determination of abacavir in water samples, and showed similar predictive ability between both algorithms. The analytical figures of merit displayed limits of detection and quantitation of 3-5 µg L?1 and 11-14 µg L?1, respectively. The main advantages of third-order data with respect to second-order data were associated with improvements in selectivity and precision.