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Title:Characterization of Slovenian wines using multidimensional data analysis from simple enological descriptors
Authors:ID Bednárová, Adriána (Author)
ID Kranvogl, Roman (Author)
ID Brodnjak-Vončina, Darinka (Author)
ID Jug, Tjaša (Author)
ID Beinrohr, Ernest (Author)
Files:.pdf Acta_Chimica_Slovenica_2013_Bednarova_et_al._Characterization_of_Slovenian_Wines_Using_Multidimensional_Data_Analysis_from_Simple_Enolog.pdf (261,13 KB)
MD5: 72339A769183E5D65F4F3849246911C7
 
URL http://acta-arhiv.chem-soc.si/60/60-2-274.pdf
 
Language:English
Work type:Scientific work
Typology:1.01 - Original Scientific Article
Organization:FKKT - Faculty of Chemistry and Chemical Engineering
Abstract:Determination of the product's origin is one of the primary requirements when certifying a wine's authenticity. Significant research has described the possibilities of predicting a wine's origin using efficient methods of wine components' analyses connected with multivariate data analysis. The main goal of this study was to examine the discrimination ability of simple enological descriptors for the classification of Slovenian red and white wine samples according to their varieties and geographical origins. Another task was to investigate the inter-relations available among descriptors such as relative density, content of total acids, non-volatile acids and volatile acids, ash, reducing sugars, sugar-free extract, $SO_2$, ethanol, pH, and an important additional variable - the sensorial quality of the wine, using correlation analysis, principal component analysis (PCA), and cluster analysis (CLU). 739 red and white wine samples were scanned on a Wine Scan FT 120, from wave numbers 926 $cm^{–1}$ to 5012 $cm^{–1}$. The applied methods of linear discriminant analysis (LDA), general discriminant analysis (GDA), and artificial neural networks (ANN), demonstrated their power for authentication purposes.
Keywords:wine authentication, enological descriptors, classification techniques, ANN
Publication status:Published
Publication version:Version of Record
Year of publishing:2013
Number of pages:str. 274-286
Numbering:Letn. 60, št. 2
PID:20.500.12556/DKUM-50427 New window
ISSN:1318-0207
UDC:543.21:663.2
ISSN on article:1318-0207
COBISS.SI-ID:16958998 New window
NUK URN:URN:SI:UM:DK:4IXM41K7
Publication date in DKUM:10.07.2015
Views:3213
Downloads:96
Metadata:XML DC-XML DC-RDF
Categories:Misc.
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Record is a part of a journal

Title:Acta Chimica Slovenica
Shortened title:Acta Chim. Slov.
Publisher:Slovensko kemijsko društvo
ISSN:1318-0207
COBISS.SI-ID:14086149 New window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:10.07.2015

Secondary language

Language:Slovenian
Abstract:Določevanje izvora je ena od osnovnih zahtev, ko želimo certificirati pristnost vin. Raziskava opisuje možnosti napovedovanja izvora vin z uporabo učinkovitih metod analize parametrov vin in multivariantno analizo. Glavni namen študije je proučevanje možnosti razlikovanja enostavnih enoloških deskriptorjev za klasifikacijo vzorcev slovenskih rdečih in belih vin glede na vrsto in geografski izvor. Drugi cilj je bil proučevanje razmerij med deskriptorji, kot so: relativna gostota, vsebnost skupnih kislin, nehlapne kisline, hlapne kisline, pepel, reducirajoči sladkor, prosti sladkor, $SO_2$, etanol, pH in med pomembnimi dodatnimi spremenljivkami, kot je senzorična kakovost vina z uporabo korelacijske analize, metode glavnih osi (PCA) in analizo grupiranja podatkov (CLU). 739 vzorcev rdečih in belih vin je bilo posnetih na aparatu Wine Scan FT 120, od valovnega števila 926 $cm^{–1}$ do 5012 $cm^{–1}$. Uporabljene metode linearne diskriminantne analize (LDA), splošne diskriminantne analize (GDA) in umetnih nevronskih mrež (ANN) potrjujejo sposobnost določanja pristnosti vin.
Keywords:vina, enologija, klasifikacija, enološki deskriptorji, tehnike klasifikacije, umetne nevronske mreže


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