Classification of Slovak white wines using artificial neural networks and discriminant techniques

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F09%3A00036167" target="_blank" >RIV/00216224:14310/09:00036167 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Classification of Slovak white wines using artificial neural networks and discriminant techniques

Original language description

This work demonstrates the possibility to use artificial neural networks (ANN) for the classification of white varietal wines. A multilayer perceptron technique using quick propagation and quasi-Newton propagation algorithms was the most successful. Thedeveloped methodology was applied to classify Slovak white wines of different variety, year of production and from different producers. The wine samples were analysed by the GC-MS technique taking into consideration mainly volatile species, which highlyinfluence the wine aroma (terpenes, esters, alcohols). The analytical data were evaluated by means of the ANN and the classification results were compared with the analysis of variance (ANOVA). A good agreement amongst the applied computational methods has been observed and, in addition, further special information on the importance of the volatile compounds for the wine classification has been provided.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

CB - Analytical chemistry, separation

OECD FORD branch

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Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2009

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Food Chemistry

ISSN

0308-8146

e-ISSN

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Volume of the periodical

112

Issue of the periodical within the volume

4

Country of publishing house

GB - UNITED KINGDOM

Number of pages

7

Pages from-to

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UT code for WoS article

000259893600046

EID of the result in the Scopus database

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