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, SO2, 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;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UM FKKT - Faculty of Chemistry and Chemical Engineering
UDC: 543.21:663.2
COBISS: 16958998 Link will open in a new window
ISSN: 1318-0207
Views: 1744
Downloads: 41
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Secondary language: Slovenian
Secondary 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.
Secondary keywords: vina;enologija;klasifikacija;
URN: URN:NBN:SI
Type (COBISS): Scientific work
Pages: str. 274-286
Volume: ǂVol. ǂ60
Issue: ǂno. ǂ2
Chronology: 2013
Keywords (UDC): mathematics;natural sciences;naravoslovne vede;matematika;chemistry;crystallography;mineralogy;kemija;analytical chemistry;analizna kemija;chemical methods of analysis;applied sciences;medicine;technology;uporabne znanosti;medicina;tehnika;chemical technology;chemical and related industries;kemijska tehnologija;kemijske in sorodne industrije;industrial microbiology;industrial mycology;zymurgy;fermentation industry;beverage industry;stimulant industry;industrijska mikrobiologija;industrijska mikologija;pivovarstvo;vrenja;industrija pijač;industrija poživil;wine;winemaking;oenology;vino;proizvodnja vina;enologija;
ID: 1439557
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