Slavica Todic

Validation of non-destructive methodology of grapevine leaf area estimation on cv. Blaufränkisch (Vitis vinifera L.).

This paper evaluates the application of a statistical model to estimate leaf surface area in grapevines, expressed as leaf area of an individual leaf as well as total leaf area per shoot, including any lateral shoots. Estimating the area of an individual leaf was based on a formula obtained by regression analysis that uses the sum of length of two inferior leaf veins as an independent variable. To estimate the leaf area per main shoot and per lateral shoot we used the model derived from multiple regression analysis that has the following independent variables: the number of leaves, and surface area of the largest and of the smallest leaf on the shoot. High values of the coefficient of determination (r2), as well as satisfactory levels of mean absolute error (MAE), suggest that the applied models appear valid. These results have shown a good fit of observed and predicted values of shoot and lateral leaf areas.

Phenolic profiles of Serbian autochthonous variety ‘Prokupac’ and monovarietal international wines from the Central Serbia wine region

Abstract Phenolic profiles of young monovarietal white and red wines from the Central Serbia were investigated. Quantification of phenolics was done using ultra-high-performance liquid chromatography (UHPLC) coupled with a diode array detector and a triple-quadrupole mass spectrometer. Generally, red wines were characterised with higher phenolics content when compared to the white wines. This applies also for the total phenolics content (TPC) and radical scavenging activity (RSA) values. Among all the samples, the red wine from autochthonous Serbian variety ‘Prokupac’ stood out with unique composition, showing the highest TPC, RSA and the highest concentration of the individual phenolics. Finally, based on a specific phenolic pattern, principal component analysis (PCA) indicated clustering of red and white wines, with the ‘Prokupac’ as a prominent outlier.