In the present work, ternary mixtures of Acetaminophen, Ascorbic acid and Uric acid were resolved using the Electronic tongue (ET) principle and Cyclic voltammetry (CV) technique. The screen-printed integrated electrode array having differentiated response for the three oxidizable compounds was formed by Graphite, Prussian blue (PB), Cobalt (II) phthalocyanine (CoPc) and Copper oxide (II) (CuO) ink-modified carbon electrodes. A set of samples, ranging from 0 to 500 µmol·L⁻¹, was prepared, using a tilted (3³) factorial design in order to build the quantitative response model. Subsequently, the model performance was evaluated with an external subset of samples defined randomly along the experimental domain. Partial Least Squares Regression (PLS) was employed to construct the quantitative model. Finally, the model successfully predicted the concentration of the three compounds with a normalized root mean square error (NRMSE) of 1.00 and 0.99 for the training and test subsets, respectively, and R² ≥ 0.762 for the obtained vs. expected comparison graphs. In this way, a screen-printed integrated electrode platform can be successfully used for voltammetric ET applications.

Graphene and its derivates offer a wide range of possibilities in the electroanalysis field, mainly owing to their biocompatibility, low-cost, and easy tuning. This work reports the development of an enzymatic biosensor using reduced graphene oxide (RGO) as a key nanomaterial for the detection of contaminants of emerging concern (CECs). RGO was obtained from the electrochemical reduction of graphene oxide (GO), an intermediate previously synthesized in the laboratory by a wet chemistry top-down approach. The extensive characterization of this material was carried out to evaluate its proper inclusion in the biosensor arrangement. The results demonstrated the presence of GO or RGO and their correct integration on the sensor surface. The detection of CECs was carried out by modifying the graphene platform with a laccase enzyme, turning the sensor into a more selective and sensitive device. Laccase was linked covalently to RGO using the remaining carboxylic groups of the reduction step and the carbodiimide reaction. After the calibration and characterization of the biosensor versus catechol, a standard laccase substrate, EDTA and benzoic acid were detected satisfactorily as inhibiting agents of the enzyme catalysis obtaining inhibition constants for EDTA and benzoic acid of 25 and 17 mmol·L⁻¹, respectively, and a maximum inhibition percentage of the 25% for the EDTA and 60% for the benzoic acid.

In this work, a graphite-epoxy electrode with cyclic voltammetry electrodeposited reduced graphene oxide and nickel oxyhydroxide nanoparticles was prepared by decomposition in NaOH alkaline solution of cyclic voltammetry electrodeposited nickel hexacyanoferrate. FE-SEM studies were performed to confirm the NiOOH nanoparticle; the average size of the NiOOH nanoparticles was 61 ± 16 nm and EDX was applied to analyze chemical composition. To verify the performance of the prepared electrode, it was used in the electrooxidation of alcohols in alkaline medium by cyclic voltammetry. By performing different calibration experiments of methanol, ethanol, and glycerol, it was possible to extract some information about the electrode in the presence of alcohols. The LOD for methanol, ethanol, and glycerol were 2.16 mM, 2.73 mM and 0.09 mM, respectively, with sensitivity values of 1.32 µA mM⁻¹, 1.80 µA mM⁻¹ and 24.60 µA mM⁻¹, respectively. Multivariate inspection of the data using Principal Component Analysis (performed with the ClustVis online tool) demonstrated the potential ability to discriminate between the different alcohols, whereas the explained variance with the first two components was as high as 89.7%.

Biogenic amines (BAs), which are produced naturally due to the decomposition of amino acids, are crucial for the food industry because its formation is directly related to improper storage and the presence of bacteria. High concentrations of BAs can be easily related with the quality and spoilage of the products of this sector. The necessity to quickly and efficiently quantify these targets makes mandatory the use of alternatives to conventional analytical methods used up to now. For example, the combination of sensors with chemometric tools (known as electronic tongue) are a promising alternative for quick and informative analysis in the food sector. Chemometric tools allow us to develop models for the quantification of specific compounds in a complex matrix, making it a feasible tool for the development of more user-friendly methods than the traditional ones. In this context, the work presents an electronic tongue created for the detection of histamine, cadaverine and tyramine using a set of five modified GEC (graphite epoxy composite) electrodes: ZnO NPs, CuO NPs, SnO₂ NPs, Bi₂O₃ NPs, and polypyrrole, as the voltammetric multisensor array. The chemometric model was obtained with an Artificial Neural Network (ANN) with 51 input neurons, five neurons in the hidden layer and three neurons in the output layer. The functions used for the hidden and output layers were tansig and purelin, respectively. The results show slopes near to 1 and intercepts close to 0, indicating the feasibility of the model.