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%.

Biochar is a charcoal produced from the biomass pyrolysis process that presents a highly porous and functionalized surface. In the present work an array of carbon paste electrodes (CPE) made of different forms of carbon (graphite, carbon nanotubes and activated biochar) was evaluated in the development of an electronic tongue for discrimination and stripping voltammetric determination of catechol (CAT), 4‐ethylcatechol (4‐EC) and 4‐ethylguaiacol (4‐EG) phenolic compounds. Morphological characterization of carbon materials and electrodes surfaces was performed by scanning electron microscopy (SEM) and semi‐quantitative elemental composition by energy dispersive spectroscopy (EDS). Electrochemical Impedance Spectroscopy (EIS) measurements were used for electrochemical characterization of electrodes. Cyclic voltammetry measurements were performed for the phenolic compounds evaluated using different concentrations. Principal component analysis (PCA) was performed to evaluate the qualitative analysis. Quantitative data modeling was done using artificial neural networks (ANN). The proposed sensor array presented analytical potentiality allowing the distinction and determination of CAT, 4‐EC and 4‐EG by using chemometric processing. The method showed sensibility, reproducibility and a good linearity (R2>0.9940) for three compounds evaluated. Spontaneous preconcentration of three compounds was possible using all three sensors, which can allow the application of these as passive samplers for remote determinations of phenolic compounds in wine and food samples.

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