Wine racking in the winery and the use of inerting gases

Grape quality potential for wine production is strongly influenced by environmental parameters and agronomic factors. Several studies underline the rootstock effect on scions vegetative growth and berry composition [1] with an impact on wine quality. Rootstocks are promising agronomic tools for climate change adaptation and in most grape-growing regions the potential diversity of rootstocks is not fully used and only a few genotypes are planted. Moreover, little is known about the effect of rootstock genetic variability on the aromatic composition in wines.

Inter-row soil tillage in vineyards, stimulates vigor and production due to the absence of competition for water and nutrients, however negatively affects organic matter content, soil erosion, and compaction, resulting in reduced fertility. In this study, we investigated the effects of different cover crop management approaches, including cultivation type and termination timing, on the physiological and productive responses of a Glera vineyard.
The experimental trial was conducted in Precenicco (UD) from 2019 to 2021. A commercial mixture for autumn cover cropping was sown in alternating rows, and the sowing pattern was changed each year.

The context of climate crisis leads to the acceleration of technological ripening of grapes, with unsuitable loss of acidity, so various vineyard management alternatives are being considered to delay the grape ripening. The delay of the vegetative cycle towards a period of milder temperatures affects ripening, but vine behavior can vary according to the area, conduction, watering, variety, etc. A work is proposed to know the response to the green pruning of shoots, executed in fruit set and in pea size, in cv. Verdejo.

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).

Many works have been carried out to identify the key aroma volatile compounds of oak wood (e.g., whisky-lactone, furfural, maltol, eugenol, guaiacol, vanillin) using conventional gas chromatography coupled with olfactometry and mass spectrometry (GC-O-MS). Inspired by recent untargeted approaches in the field of food “omics”, this work aims to extend our knowledge on the impact of cooperage process on the volatile composition of oak wood using two-dimensional comprehensive gas chromatography coupled with time of flight mass spectrometry (GCxGC-ToFMS).