Effect of foliar application of Ca, Si and their combination on grape volatile composition

The Mediterranean basin is one of the most vulnerable regions to Climate Change effects. According to unanimous forecasts, the vineyards of Castilla-La Mancha will be among the most adversely affected by rising temperatures and water scarcity during the vine’s vegetative period. One potential strategy to mitigate the negative impacts of these changes involves the identification of grapevine varieties with superior water use efficiency, while ensuring satisfactory yields and grape quality.

Chile is considered vulnerable to climate change; and these phenomena affect several mechanisms in the grape physiology and quality. The global temperature increase affects sugar contents, organic acids, and phenolic compounds in grapes, producing an imbalance maturity. In this sense, an alternative to reduce the impact is to perform pruning after vine budburst, known as “Late Pruning” (LP).

In winemaking grapes, it is known that most aroma compounds are present as non-volatile precursors, such as glycosidic precursors. In fact, there is strong evidence supporting the connection between the content of aroma precursors and the aromatic quality of wine [1]. Acid hydrolysis is preferred to reveal the aroma potential of winemaking grapes, as it predicts more accurately the chemical rearrangements occurring during fermentation in acidic environments [2]. In this study, a method involving a fast fermentation followed by acid hydrolysis at 75ºC was used to evaluate the accumulation of aroma compounds over time in fractions obtained from six different varieties of winemaking grapes.

The wine industry produces an enormous amount of waste every year. A wider inclusion of disregarded by-products in the human diet or its use as a source of bioactive compounds is a good strategy for reducing waste. It will not only introduce an added value to a waste product but also come upon the European Union and United Nations’ demands towards more sustainable agricultural approaches and circular economy.

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).