Simultaneous sensitive determination of benzenediol isomers using multiwall carbon nanotube–ionic liquid modified carbon paste electrode by a combination of artificial neural network and fast Fourier transform admittance voltammetry

Abstract

In this study, a combination of artificial neural networks (ANNs) and fast Fourier transform admittance voltammetry (FFTAdV) was used as a novel electrochemical method for the simultaneous determination of catechol (CT), hydroquinone (HQ), and resorcinol (RC). The electrochemical performance of the carbon paste working electrode toward the oxidation of isomers was improved by modification using multiwall carbon nanotube and 1-octyl-3-methylimidazolium hexafluorophosphate ionic liquid. FFTAdV responses were processed with artificial neural networks (ANNs). Before applying ANNs, data was preprocessed by fast Fourier transform to reduce the complexity, noise, and FFT coefficients used as input for ANNs. The ANNs model reveals excellent prediction ability of nanomolar level concentration (root mean square error range 2.01–3.21). The concentration range of each isomer was 5–240 nM with detection limits of 2.0, 2.5, and 1.1 nM (RSD 2.1, 2.6, and 2.3% for n = 5) for HQ, CT, and RC, respectively. The method was validated by determining the isomers in surface water and wastewater.