Induction of polyphenols in seedlings of Vitis vinifera cv. Monastrell by the application of elicitors

Vine growth circumstances are becoming warmer and drier because of climate change. Higher temperatures advance ripening to a point in the season less conducive to the production of fine wine, while drought reduces yields (Van Leeuwen et al., 2019). Several wine-producing regions around the world have already recognized threats to their viticultural viability (Santos et al., 2020). An economical and cost-effective strategy for adaptation is the employment of late-ripening, drought-resistant plant material (varieties, clones, and rootstocks).

The quality and color stability of red wines are directly related to content and distribution of phenolic compounds. However, the climate change produces the asynchrony between the dates of technological and maturity of grapes. The crop-forcing technique (CF) restores the coupling between phenolic and technological ripeness while limits vineyard yields. Blending of wines is frequently used to equilibriate composition of wines and to increase their stability, color and quality. The aim of the present work is to study the phenolic composition and color of wine blends made with FW (wines from vines subjected to CF) and CW (wines for vines under the usual cultivation practices).

The plant rhizosphere microbial communities are an essential component of plant microbiota, which is crucial for sustaining the production of healthy crops. The main drivers of the composition of such communities are the growing environment and the planted genotype. Recent viticulture studies focus on understanding the effects of these factors on soil microbial composition since microbial biodiversity is an important determinant of plant phenotype, and of wine’s organoleptic properties. Microbial biodiversity of different wine regions, for instance, is an important determinant of wine terroir.

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).

Light is responsible for adverse reactions in wine including the formation of unpleasant flavors, loss of vitamins or photodegradation of anthocyanins. Among them, the riboflavin degradation leads to the formation of undesirable volatile compounds, known as light-struck taste. These photo-chemical reactions could be avoided by simply using opaque packaging. However, most rosé wines are kept in transparent bottles due to different commercial reasons. Some agri-food waste extracts have been studied for their photoprotective action which turn to be highly correlated with phenolic content [1].