Molecularly imprinted polymers: an innovative strategy for harvesting polyphenoles from grape seed extracts

In Chile there is a dry farming area known as a traditional wine region, where varieties brought by the Spanish conquerors still persist. These varieties, in general, are cultivated under traditional systems, with low use of technical and economic resources, and low profitability for their grapes and wines. In this region, as in other wine grape growing areas, climatic conditions have changed significantly in recent decades. In particular, the occurrence of spring frosts, when bud break has already begun, have generated significant losses for these growers.

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.

Grapevine pruning is one of the most important practices in the vineyards. Winegrowers use it to provide the vines the shape needed, or to maintain it once achieved, and also to balance vegetative growth and fruit production. In the last decades, careless pruning has been blamed, among other factors, as responsible of the vineyard decay that is been observed even in young vines. However, to our knowledge, there is a lack of systematic research trying to elucidate to which extent the pruning method used affects plant development or its susceptibility to grapevine trunk diseases (GTD). Within this context, the aim of this work is to study the influence of different pruning method strategies on the development of field-planted young vines.

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).

The use of grapevine genetic diversity is a way to mitigate the negative impacts of climate change on viticulture systems. Leaf epidermal flavonoids (including flavonols and anthocyanins) are involved in plant defense mechanisms against environmental stresses, like high temperatures or excessive solar radiation [1,2]. Among other factors, they modulate light absorption, which reduces photoinhibition processes in photosynthetic tissues [1]. Therefore, the identification of grapevine cultivars with an increased content on leaf epidermal flavonoids arises as a potential avenue to improve grapevine tolerance to some detrimental environmental stresses.