| Title: | Prediction of wine sensorial quality by routinely measured chemical properties |
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| Authors: | ID Bednárová, Adriána (Author)
ID Kranvogl, Roman (Author)
ID Brodnjak-Vončina, Darinka (Author)
ID Jug, Tjaša (Author) |
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| Files: |  Nova_Biotechnologica_et_Chimica_2014_Bednarova_et_al._Prediction_of_Wine_Sensorial_Quality_by_Routinely_Measured_Chemical_Properties.pdf (1,02 MB)
MD5: 825D1FA27A0C1B13C97F592A111173DD
 https://journals.scicell.org/index.php/NBC/article/view/407
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FKKT - Faculty of Chemistry and Chemical Engineering
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| Abstract: | 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. |
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| Keywords: | overall sensorial quality, prediction, Slovenian wine, artificial neural networks, multivariate data analysis |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Year of publishing: | 2014 |
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| Number of pages: | str. 182-196 |
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| Numbering: | Letn. 13, št. 2 |
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| PID: | 20.500.12556/DKUM-65389  |
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| ISSN: | 1338-6905 |
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| ISSN on article: | 1338-6905 |
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| COBISS.SI-ID: | 4821243 |
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| DOI: | 10.1515/nbec-2015-0008 |
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| NUK URN: | URN:SI:UM:DK:VD5EIBLL |
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| Publication date in DKUM: | 03.04.2017 |
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| Views: | 1760 |
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| Downloads: | 448 |
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| Metadata: |    |
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| Categories: | Misc.
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