In this study, square wave anodic stripping voltammetry using two different types of electrodes (carbon nanotube electrode and graphite electrode) was combined with chemometric methods - partial least squares (PLS) and artificial neural networks (ANN) for determining copper, zinc, cadmium and lead in cachaça. The objectives were comparison of methods developed and the verification of the quality of artisanal cachaças in terms of metal content. For the development of the methodology, inductively coupled plasma optical emission spectrometry (ICP OES) was used as reference technique. The performance of multivariate models obtained was evaluated by the coefficient of determination (R2) and root mean square error of prediction (RMSEP). F test was utilized for comparing methods at confidence level of 95%. Better results were observed by using carbon nanotube electrode regardless of the multivariate method proposed. The methodology is simple, fast, and inexpensive and it can be used in quality control laboratories.
Keywords: Artificial neural network; Cachaça; Carbon nanotube electrode; Metals; Partial least square; Square wave anodic stripping voltammetry.
Copyright © 2019 Elsevier Ltd. All rights reserved.