Impact of toasting and botanical origin on oak wood (Q. sp.) volatilome using untargeted GCxGC-ToFMS analysis

The wooden barrel is a key factor in enology, since wine chemical composition and sensory properties changes significantly in contact with the barrel[1]. Today’s highly competitive market constantly demands new differentiated products and wineries search innovations continuously.
Wood selection is crucial: barrels stability to keep constant their contribution and the result on products, and additional and differentiated wood contributions to impact their new products. Oak wood selection has traditionally been carried out using parameters such as specie, location and grain, however, it goes one step further nowadays. Large cooperage work with non-destructive techniques that allow classifying oak wood quickly and easily according to their organoleptic contribution[2].

For sustainable viticulture, the substitution of chemical inputs with biocontrol products has become one of the most considered strategies. This strategy is based on elicitor-triggered immunity that requires a deep understanding of the molecular mechanisms involved in plant defense activation. Plant immune responses are triggered through the perception of conserved microbe-associated molecular patterns (MAMPs) which are recognized by pattern recognition receptors (PRRs) at the plasma membrane.

In recent years there has been a growing demand for alternative packaging designs in the food industry focused on diminishing the carbon footprint. Despite the environmental advantages of cans versus bottles, the traditional environment of wine has hindered the establishment of less contaminant containers. In this context, the objective of this study was to understand and generate knowledge about consumers´ perception of canned wines in comparison to bottled wines.

Due to climate change, the occurrence of heatwaves and drought events is increasing, with significant impact on viticulture. Common ways to adapt viticulture to a changing climate include site selection, genotype selection, irrigation management and canopy management. The latter mentioned being for instance source-sink manipulations, such as leaf removal, with the aim to delay ripening.

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).