Optimization of Sensors to be Used in a Voltammetric Electronic Tongue Based on Clustering Metrics

A priori tailored selection of sensor arrays for electronic tongues

Electronic tongues (ETs) are multisensor systems based on the coupling of different sensors with high stability and cross-sensitivity towards different species, along with an appropriate chemometric tool that allow the classification of samples and/or the quantification of selected analytes. Despite the great advantages that the data processing stage may offer (e.g. allowing to offset matrix effects from the sample itself, or to resolve the presence of interferences, drifts or non-linearities obtained with the sensors), the stability of the sensors response together with its varied cross-sensitivity are of utmost importance. In this regard, despite the progress that has been made over the last years and the increasing number of publications dealing with ETs, researchers have focused mainly on the developed application itself or on the data treatment stage, but very few report on the choice of the sensors. In this direction, herein we explore on the development of a simple methodology that allows the a priori selection of the optimal sensor array to carry out a specific application. Concretely, the usage of principal component analysis (PCA) in combination with different clustering indices is proposed as a simple approach to simultaneously assess the cross-response and the reproducibility of the different sensors in a single step, what allows to define which sensors may constitute an ET array for a given qualitative or quantitative application. The above ideas have been applied to the study case of aromatic essential oils.

A novel electronic tongue using electropolymerized molecularly imprinted polymers for the simultaneous determination of active pharmaceutical ingredients

The combination of chemometrics and electrochemical sensors modified with molecularly imprinted polymers (MIPs) towards the development of MIP-based electronic tongues (ETs) was explored herein. To demonstrate the potential of such an approach, the simultaneous determination of paracetamol, ascorbic acid and uric acid mixtures in pharmaceutical samples was evaluated. To this aim, MIP-based sensors for the different compounds were prepared by in situ electropolymerization of pyrrole in the presence of p-toluenesulfonate anion (pTS^-), which acted as functional doping ion of the polypyrrole (PPy) MIP backbone. Morphological characterization of the MIPs was done by scanning electron microscopy (SEM), while functionalization of the electrodes was monitored electrochemically. Under the optimized measuring conditions, the developed sensors showed a good performance, with good linearity at the μM level (R² > 0.992, limits of detection between 1 and 24 μM) as well as good repeatability (intra- and inter-day RSD values between 3 and 6% over 30 consecutive measurements). Finally, the quantification of the individual substances in different pharmaceutical samples was achieved by an artificial neural networks (ANNs) model, showing satisfactory agreement between expected and obtained values (R² > 0.987).