Open AccessPredicting Vodka Adulteration: A Combination of Electronic Tongue and Artificial Neural Networks
Author(s) -
Leonardo Fabio León Marenco,
Luiza Pereira de Oliveira,
Daniella Lopez Vale,
Maiara Oliveira Salles
Publication year - 2021
Publication title -
journal of the electrochemical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.258
H-Index - 271
eISSN - 1945-7111
pISSN - 0013-4651
DOI - 10.1149/1945-7111/ac393e
Subject(s) - adulterant , electronic tongue , methanol , tap water , chemistry , artificial neural network , partial least squares regression , biological system , chromatography , artificial intelligence , mathematics , food science , statistics , computer science , organic chemistry , environmental science , taste , biology , environmental engineering
An artificial neural network was used to build models caple of predicting and quantifying vodka adulteration with methanol and/or tap water. A voltammetric electronic tongue based on gold and copper microelectrodes was used, and 310 analyses were performed. Vodkas were adulterated with tap water (5 to 50% (v/v)), methanol (1 to 13% (v/v)), and with a fixed addition of 5% methanol and tap water varying from 5 to 50% (v/v). The classification model showed 99.5% precision, and it correctly predicted the type of adulterant in all samples. Regarding the regression model, the root mean squared error was 3.464% and 0.535% for the water and methanol addition, respectively, and the prediction of the adulterant content presented an R 2 0.9511 for methanol and 0.9831 for water adulteration.