Journal Article

Electrochemical Sensors Coupled with Multivariate Statistical Analysis as Screening Tools for Wine Authentication Issues: A Review

Geană, Elisabeta-Irina, Apetrei, Constantin, Ciucure, Corina Teodora

2020

Overview

Consumers are increasingly interested in the characteristics of the products they consume, including aroma, taste, and appearance, and hence, scientific research was conducted in order to develop electronic senses devices that mimic the human senses. Thanks to the utilization of electroanalytical techniques that used various sensors modified with different electroactive materials coupled with pattern recognition methods, artificial senses such as electronic tongues (ETs) are widely applied in food analysis for quality and authenticity approaches. This paper summarizes the applications of electrochemical sensors (voltammetric, amperometric, and potentiometric) coupled with unsupervised and supervised pattern recognition methods (principal components analysis (PCA), linear discriminant analysis (LDA), partial least square (PLS) regression, artificial neural network (ANN)) for wine authenticity assessments including the discrimination of varietal and geographical origins, monitoring the ageing processes, vintage year discrimination, and detection of frauds and adulterations. Different wine electrochemical authentication methodologies covering the electrochemical techniques, electrodes types, functionalization sensitive materials and multivariate statistical analysis are emphasized and the main advantages and disadvantages of using the proposed methodologies for real applications were concluded.

• Publisher: MDPI AG
• Subject: Aging / Analytical chemistry / Aroma / Artificial neural networks / Authenticity / Biosensors / Carbon / Chemical sensors / Chromatography / Discriminant analysis / Discrimination / Electrical measurement / Electroactive materials / electrochemical sensor / Electrodes / Electronic devices / electronic tongue / Electronic tongues / Enzymes / Food quality / Geographical distribution / Mass spectrometry / Multivariate analysis / multivariate data analysis / Multivariate statistical analysis / Neural networks / NMR / Nuclear magnetic resonance / Pattern recognition / Phenols / Polymers / Polyphenols / Principal components analysis / Regression analysis / Scientific imaging / Sensors / Statistical analysis / Statistics / Voltammetry / Wine / wine authentication