Electronic tongue for wine discrimination, using PCA and ANN

Author

Rios Diaz, Yennifer Yuliana ; Duran Acevedo, Cristhian Manuel

Author_Institution

Res. Group in Multisensory Syst. & Pattern Recognition, Univ. de Pamplona, Pamplona, Colombia

fYear

2014

fDate

22-24 Oct. 2014

Firstpage

1

Lastpage

5

Abstract

This article explains the discrimination of wines through an electronic tongue, using machine learning algorithms and signal processing techniques: Principal Component Analysis (PCA) and Artificial Neural Networks (ANN), where the model of the multilayer perceptron was implemented. A database for wine quality was used and a study was conducted with a green wine of northeastern Portugal. The analysis of data and subsequent processing was performed for two different types of wine; White and Red. The proposed method was able to classify and identify each of the categories of wine, giving a success rate of 98% with ANN and a variance of 98%.

Keywords

electronic tongues; multilayer perceptrons; principal component analysis; production engineering computing; quality control; wine industry; wineries; ANN; PCA; artificial neural networks; electronic tongue; green wine; machine learning algorithms; multilayer perceptron; northeastern Portugal; principal component analysis; red wine; signal processing techniques; white wine; wine discrimination; wine quality; Artificial neural networks; Databases; Electrodes; Principal component analysis; Tongue; Training; Voltage measurement; Electronic tongue; MLP; artificial neural networks; principal component analysis; wines;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering Mechatronics and Automation (CIIMA), 2014 III International Congress of

Conference_Location

Cartagena

Print_ISBN

978-1-4799-7931-8

Type

conf

DOI

10.1109/CIIMA.2014.6983456

Filename

6983456