Predicting Vodka Adulteration: A Combination of Electronic Tongue and Artificial Neural Networks

Abstract

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 R2 0.9511 for methanol and 0.9831 for water adulteration.

Computer vision with artificial intelligence for a fast, low-cost, eco-friendly and accurate prediction of beer styles and brands

“…Duarte and collaborators in 2018 used electrophoresis and chemometrics to classify hop bitterness. 1–5,14–17…”

“…Duarte and collaborators in 2018 used electrophoresis and chemometrics to classify hop bitterness. [1][2][3][4][5][14][15][16][17] However, to the best of our knowledge, no developed method has constructed an instrument using CV and ML to classify different types of beer and predict the brand of a new unknown sample. This work aims to construct a CV system with ML using an ANN to accurately predict the brands and styles of commercial beers in an automated manner.…”

Improvement on the Identification and Discrimination Ability for Rice of Electronic Tongue Multi-Sensor Array Based on Information Entropy

“…With high sensitivity and reproducibility, electronic tongue can realize rapid, accurate and real-time analysis, and has been widely used in the field of food. [1][2][3] Different from the traditional analysis methods, the electronic tongue sensor array cannot directly provide the property information of the sample compound, but collect the fingerprint signal of the sample, and then obtain the preset results through the appropriate algorithm. Voltammetric electronic tongue sensor has low selectivity, shows high objectivity and less time consumption in food analysis, and has non-specificity for the quantification of multicomponent in solution, which makes it relatively flexible and possible to use algorithms such as pattern recognition or multivariate data analysis in data processing.…”