Electrochemical fingerprints of brominated trihaloacetic acids (HAA3) mixtures in water

In this work, we explore the capabilities of combining electrochemical sensors and chemometrics towards the analysis of haloacetic acids (HAAs) in water samples. Our approach is based on electronic tongue principles. It combines voltammetric measurements on a gold electrode with chemometric data processing, to extract characteristic fingerprints for HAAs. Cyclic and square wave voltammograms were pre-processed by means of fast Fourier transform (FFT) to provide the coefficients used as subsequent inputs for an artificial neural network (ANN) model. We were able to quantitatively detect and discriminate each HAA under study. Quantitation of HAA3 mixtures (i.e. bromodichloroacetic acid, dibromochloroacetic acid and tribromoacetic acid) was achieved at the μg/L level, with a normalized root mean square error (NRMSE) of 0.054 for the validation subset. Finally, successful analysis of spiked water samples was achieved demonstrating the operation of the sensor in the absence of matrix effects.