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

KRUZLICOVA, Dasa, Jan MOCÁK, Branko BALLA, Jan PETKA, Marta FARKOVÁ and Josef HAVEL. Classification of Slovak white wines using artificial neural networks and discriminant techniques. Food Chemistry. 2008 Elsevier Ltd, 2009, vol. 112, No 4, 7 pp. ISSN 0308-8146.

Basic information

• Original name: Classification of Slovak white wines using artificial neural networks and discriminant techniques
• Name in Czech: Klasifikace slovenských vín použitím umělých neuronových sítí a diskriminantních technik
• Authors: KRUZLICOVA, Dasa (203 Czech Republic), Jan MOCÁK (703 Slovakia), Branko BALLA (703 Slovakia), Jan PETKA (703 Slovakia), Marta FARKOVÁ (203 Czech Republic, belonging to the institution) and Josef HAVEL (203 Czech Republic, guarantor, belonging to the institution).
• Edition: Food Chemistry, 2008 Elsevier Ltd, 2009, 0308-8146.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10406 Analytical chemistry
• Country of publisher: United Kingdom of Great Britain and Northern Ireland
• Confidentiality degree: is not subject to a state or trade secret
• Impact factor: Impact factor: 3.146
• RIV identification code: RIV/00216224:14310/09:00036167
• Organization unit: Faculty of Science
• UT WoS: 000259893600046
• Keywords in English: Artificial neural networks; wine; classification
• Tags: artificial neural networks, CLASSIFICATION, Wine
• Tags: International impact, Reviewed

Abstract

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. The developed 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 highly influence 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.

Abstract (in Czech)

Tato práce umožňuje klasifikaci vín umělými neuronovými sítěmi