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

Abstract

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.

Combination of machine learning and intelligent sensors in real-time quality control of alcoholic beverages

“…The schematic diagram of working principles of E-nose and E-tongue. (Geană et al, 2020;Cao et al, 2021). In order to sell a higher price, some alcohol producers adulterate the information of their products, which damages the reputation of alcohol industry and the benefit of consumers (Zhang et al, 2019;Geană et al, 2020).…”

“…In order to sell a higher price, some alcohol producers adulterate the information of their products, which damages the reputation of alcohol industry and the benefit of consumers (Zhang et al, 2019;Geană et al, 2020). ML-enabled sensory systems such as E-nose and E-tongue have been developed to establish a real-time quality control for alcoholic beverages (Geană et al, 2020).…”

Beta-Lactam Antibiotic Discrimination Using a Macromolecular Sensor in Water at Neutral pH

“…LDA finds the linear combinations of the raw instrumental measurements that minimize the size of the cluster of replicates pertaining to each sample (intracluster distances) while maximizing the distances among clusters belonging to different analytes (intercluster distances), thus providing optimal separation among analyte clusters [29,30]. This method has been used for the discrimination of various analytes, including bacteria [31], proteins [32], wines [33], sugars [34,35], metal ions [36,37], food additives [38], and drugs [39].…”

Quick classification of strong-aroma types of base Baijiu using potentiometric and voltammetric electronic tongue combined with chemometric techniques

“…Due to the high alcohol content of base SAB, the ability of alcohol tolerance of sensors determines whether they are suitable for the analysis of base SAB. Currently, it has been proved by numerous researchers that E-tongue based on inert metal electrodes or modified epoxy-composite sensors could be a good instrument for the distilling spirits analysis ( 19 , 20 ). Among them, the Smartongue is a kind of E-tongue based on multifrequency large amplitude pulse voltammetry (MLAPV).…”

“…Among them, the Smartongue is a kind of E-tongue based on multifrequency large amplitude pulse voltammetry (MLAPV). More importantly, based on a combination of pulse applied relaxation techniques combined with the specific pattern recognition system and multivariate statistical analysis, a lot of signals could be processed more accurately and effectively by MLAPV compared with other sensors ( 16 , 20 ). In 2007, Tian et al discriminated six Baijiu samples successfully by using an electronic tongue based on MLAPV coupled with a series of metal electrodes at different frequency segments ( 21 ).…”