bg | en 
Bulgarian Journal of Crop Science   ISSN 0568-465X
Array ( [session_started] => 1732210459 [LANGUAGE] => EN [LEPTON_SESSION] => 1 )
Help
 
Register

Login:


Forgot Details? Sign-up



Comparative technological characteristics of vine varieties for red wines (Vitis vinifera L.).
Venelin Roychev, Neli Keranova
Abstract: The subject of the study are 41 local, introduced and newly developed vine varieties for the production of red wines, examined according to the most important technological indicators reflecting the quality of the wine obtained from them: alcohol, sugars, sugar-free extract, titratable acids, volatile acids, pH, anthocyanins, color intensity and tasting score. The researched vine varieties are grouped on the basis of their phenotypic proximity and remoteness in view of the mentioned indicators. A combination of statistical approaches is applied for this purpose. The groups of the respective varieties with similar phenotypic characteristics are obtained by means of a hierarchical cluster analysis. For the qualitative description of the formed clusters, one factor dispersion analysis, Duncan’s test, and principal component analysis (PCA) are conducted. Three generalized clusters are formed. The first one is the largest, including the varieties whose wines are characterized by a relatively low content of sugar-free extract, moderate pH level, a smaller amount of anthocyanins and low color intensity. The second cluster consists of varieties with comparatively high alcohol content in wines, as well as high pH, anthocyanins, color intensity, and a very good tasting score – Syrah, Petit Verdot and Cabernet Sauvignon. The third cluster comprises of Ancellotta, Grand Noir, Dornfelder, Alicante Bouschet and Saperavi, whose wines contain significant amounts of anthocyanins and possess high color intensity. The amount of anthocyanins, the color intensity and the sugar-free extract in wine exert the most significant influence on the differentiation of red wine varieties into groups.
Keywords: comparative cluster analysis; dispersion analysis; principal component analysis (PCA); technological indicators of wine; vine varieties for red wines
Date published: 2022-04-21
Download full text