20.500.12556/DKUM-50427 Characterization of Slovenian wines using multidimensional data analysis from simple enological descriptors 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. 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. wine authentication enological descriptors classification techniques ANN vina enologija klasifikacija enološki deskriptorji tehnike klasifikacije umetne nevronske mreže true false true Angleški jezik Slovenski jezik Znanstveno delo 2015-07-10 12:39:46 2015-07-10 12:39:46 2022-05-29 03:06:58 2021-05-09 03:28:00 2013 0 0 str. 274-286 št. 2 Letn. 60 2013 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 1318-0207 543.21:663.2 14086149 16958998 1318-0207 URN:SI:UM:DK:4IXM41K7 Acta_Chimica_Slovenica_2013_Bednarova_et_al._Characterization_of_Slovenian_Wines_Using_Multidimensional_Data_Analysis_from_Simple_Enolog.pdf Acta_Chimica_Slovenica_2013_Bednarova_et_al._Characterization_of_Slovenian_Wines_Using_Multidimensional_Data_Analysis_from_Simple_Enolog.pdf 1 72339A769183E5D65F4F3849246911C7 efc0e02f016d9d658fbdc08ed7361c350d9c47193bca8746a6c6a6d3c36f6493 856816ee-7c0e-11eb-bb7a-00155d0001ca https://dk.um.si/Dokument.php?lang=slv&id=115428 http://acta-arhiv.chem-soc.si/60/60-2-274.pdf 1 https://dk.um.si/Dokument.php?lang=slv&id=74986 Fakulteta za kemijo in kemijsko tehnologijo 0 0 0