Application of a voltammetric electronic tongue combined with chemometric approaches for the early classification of heavy metals in sunflower oil

In the present study, an electronic tongue (e‐tongue) system based on cyclic voltammetry (CV) with three electrodes (pencil graphite (PG), screen printed (SP), and glassy carbon (GC)) was fabricated to investigate and detect heavy metals, such as cadmium (Cd), lead (Pb), tin (Sn), and nickel (Ni) (at three concentrations of 0.05, 0.1, and 0.25 ppm) in sunflower edible oil. The results from cyclic voltammograms showed that GC, PG, and SP electrodes have the highest cathodic current peaks and show higher sensitivity to the presence of heavy metals in sunflower edible oil, respectively. Moreover, the principal component analysis method was used to classify heavy metals. The obtained results showed that the three intended electrodes were capable of well detecting data. Overall, PG, SP, and GC electrodes account for 84%, 98%, and 88% of the variance between data, respectively. Additionally, a support vector machine (SVM) and K‐nearest neighbor (K‐NN) were used for classification. High accuracies were obtained for the PG, SP, and GC electrodes. Besides, The K‐NN method combined with the intended electrodes could well perform classification, and GC was the best electrode. In the following, the partial least square method could predict data for PG, SP, and GC electrodes with an accuracy of 98%, 99%, and 81%, respectively. Finally, it can be said that the fabricated e‐tongue combined with chemometric methods could classify heavy metals, in edible oil with high accuracy. PRACTICAL APPLICATIONS: In this research, an e‐tongue system was developed based on three electrodes (PG, SP, and GC) to identify heavy metals in sunflower edible oil using chemometric methods. According to results, e‐tongue combined with chemometric method has high ability for food quality monitoring.