Román Bataller

Quantification of organic acids using voltammetric tongues

Recently, electronic tongues (ET) have appeared as an excellent alternative to traditional techniques for the evaluation of food quality and processes. ET systems are based on arrays of low selectivity sensors that are simultaneously sensitive to several components in a measured sample (cross-sensitivity). The aim of this study was to determine the ability of an ET based on pulse voltammetry to quantify organic acids (ascorbic, citric and malic acids) in simple (SS) and binary solutions (BS) using different electrodes. The most significant electrodes for ascorbic acid prediction were Ni and Ag for SS, and Ag and Ir for BS where positive pulses were more suitable than negative ones. The prediction of citric and malic acids in SS and BS were suitable using Ir, Rh, Pt, Ag and Cu electrodes, using both positive and negative pulses.

Glyphosate Detection by Means of a Voltammetric Electronic Tongue and Discrimination of Potential Interferents

A new electronic tongue to monitor the presence of glyphosate (a non-selective systemic herbicide) has been developed. It is based on pulse voltammetry and consists in an array of three working electrodes (Pt, Co and Cu) encapsulated on a methacrylate cylinder. The electrochemical response of the sensing array was characteristic of the presence of glyphosate in buffered water (phosphate buffer 0.1 mol·dm−3, pH 6.7). Rotating disc electrode (RDE) studies were carried out with Pt, Co and Cu electrodes in water at room temperature and at pH 6.7 using 0.1 mol·dm−3 of phosphate as a buffer. In the presence of glyphosate, the corrosion current of the Cu and Co electrodes increased significantly, probably due to the formation of Cu2+ or Co2+ complexes. The pulse array waveform for the voltammetric tongue was designed by taking into account some of the redox processes observed in the electrochemical studies. The PCA statistical analysis required four dimensions to explain 95% of variance. Moreover, a two-dimensional representation of the two principal components differentiated the water mixtures containing glyphosate. Furthermore, the PLS statistical analyses allowed the creation of a model to correlate the electrochemical response of the electrodes with glyphosate concentrations, even in the presence of potential interferents such as humic acids and Ca2+. The system offers a PLS prediction model for glyphosate detection with values of 098, −2.3 × 10−5 and 0.94 for the slope, the intercept and the regression coefficient, respectively, which is in agreement with the good fit between the predicted and measured concentrations. The results suggest the feasibility of this system to help develop electronic tongues for glyphosate detection.