Characterization of different clone candidates of xinomavro according to their phenolic composition

One of the main challenges in the wine industry is to understand how different wine processing techniques and practices can influence the overall quality of the final product.

The mechanical strength or firmness of a fruit is considered an important parameter to characterize its state of maturity or conservation, as other parameters such as sugar level or color.

The seasonal’s climatic conditions determine the composition of grapes at harvest as they affect the vine’s physiology and development. High temperatures during the grape ripening period cause a high accumulation of sugars and degradation of fruit acidity ,and alter the synthesis of polyphenols. Therefore, some vineyard management can be applied in order to modify grapevine impact on climate variability. One example is the pre-pruning at the beginning of grape ripening, which can delay the ripening period and modify the composition of the grapes at harvest. This work aims to evaluate the pre-pruning field technique on yield components and alcohol content in wines of Tannat and Merlot varieties.

Under standard wine tasting conditions, volatile organic compounds (VOCs) responsible for the wine’s bouquet progressively invade the glass headspace above the wine surface. Most of wines being complex water/ethanol mixtures (with typically 10-15 % ethanol by volume), gaseous ethanol is therefore undoubtedly the most abundant VOC in the glass headspace [1]. Yet, gaseous ethanol is known to have a multimodal influence on wine’s perception [2]. Of particular importance to flavor perception is the effect of ethanol on the release of aroma compounds into the headspace of the beverage [1].

Implementing artificial intelligence (AI) in viticulture and enology is a rapidly growing field of research with an essential number of potential practical applications.