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Nova Biotechnologica et Chimica

The Journal of University of SS. Cyril and Methodius

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CiteScore 2016: 0.42

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Prediction of Wine Sensorial Quality by Routinely Measured Chemical Properties

Adriána Bednárová
  • Department of Chemistry, Faculty of Natural Sciences, University of SS Cyril and Methodius in Trnava, Nám. J. Herdu 2, Trnava, SK-917 01, Slovak Republic
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/ Roman Kranvogl
  • Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia
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/ Darinka Brodnjak-Vončina
  • Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia
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/ Tjaša Jug
  • Chamber of Agriculture and Forestry of Slovenia, Institute for Agriculture and Forestry, Pri hrastu 18, 5000 Nova Gorica, Slovenia
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Published Online: 2015-02-06 | DOI: https://doi.org/10.1515/nbec-2015-0008


The determination of the sensorial quality of wines is of great interest for wine consumers and producers since it declares the quality in most of the cases. The sensorial assays carried out by a group of experts are time-consuming and expensive especially when dealing with large batches of wines. Therefore, an attempt was made to assess the possibility of estimating the wine sensorial quality with using routinely measured chemical descriptors as predictors. For this purpose, 131 Slovenian red wine samples of different varieties and years of production were analysed and correlation and principal component analysis were applied to find inter-relations between the studied oenological descriptors. The method of artificial neural networks (ANNs) was utilised as the prediction tool for estimating overall sensorial quality of red wines. Each model was rigorously validated and sensitivity analysis was applied as a method for selecting the most important predictors. Consequently, acceptable results were obtained, when data representing only one year of production were included in the analysis. In this case, the coefficient of determination (R2) associated with training data was 0.95 and that for validation data was 0.90. When estimating sensorial quality in categorical form, 94 % and 85 % of correctly classified samples were achieved for training and validation subset, respectively.

Keywords : overall sensorial quality; prediction; Slovenian wine; artificial neural networks; multivariate data analysis


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About the article

Published Online: 2015-02-06

Published in Print: 2014-12-01

Citation Information: Nova Biotechnologica et Chimica, Volume 13, Issue 2, Pages 182–196, ISSN (Online) 1338-6905, DOI: https://doi.org/10.1515/nbec-2015-0008.

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© by Adriána Bednárová. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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