Voltammetric e-Tongue Based on a Single Sensor and Variable Selection for the Classification of Teas

This work presents a simple and low-cost strategy to obtain voltammetric tongues by using a single voltammetric sensor aided by variable selection techniques. A usual electronic tongue consists of an array of physical sensors followed by data compression before pattern recognition modeling. Alternatively, a single voltammetric sensor can also act as an array of pseudo-sensors—applied potentials in a voltammogram—that can be properly selected to perform a given discrimination. The applicability of this strategy was evaluated in the discrimination of teas. Teas prepared for immediate intake (simulating a home-made tea cup) were analyzed with staircase voltammetry at an epoxy-graphite electrode. Voltammograms were submitted to variable selection previously to linear discriminant analysis (LDA) to identify the applied potentials that improve discrimination of teas according to variety, country of origin, and manufacturer. Successive projections algorithm (SPA), genetic algorithm (GA), and stepwise (SW) formulation were the variable selection techniques evaluated. Best results were achieved with SPA/LDA models, with correct classification rates for the prediction set close to 100%.