Influence of irrigation frequency on berry phenolic composition of red grape varieties cultivated in four spanish wine-growing regions

The barrel-making process is widely recognized as a crucial practice that affects the composition of barrel-aged wine. After the drying process, the staves are considered ready for barrel assembly, which includes the processes of bending and toasting the barrel structure. Toasting is considered one of the most critical stages in determining the physical and chemical composition of the staves, which can influence the chemical and sensory composition of the wine aged in barrels made from them [1].

Performing accurate vineyard yield estimation is of upmost importance as it provides important benefits to the whole vine and wine industry. Recently, image-analysis approaches have been explored to address this issue however this approach has as main challenge the bunch occlusion, mostly by vegetation but also by neighboring bunches. The present work aims at assessing the magnitude of bunch occlusion by neighboring bunches and to evaluate its dependency on a selection of vegetative and reproductive vine parameters assessed at fruiting zone. Forty vine segments (1 m) of two vineyard plots of the white cultivars ‘Alvarinho’ and ‘Arinto’ were assessed for vegetative and reproductive features at fruiting zone and imaged with a 2D camera.

Wine acidity is a parameter of great importance that influences different quality factors of the product such as biological stability or organoleptic characteristics. In the current context of climate change, which gives rise to wines with higher levels of ethanol and lower acidity, the biological acidification with yeast species such as Lachancea thermotolerans could be a solution.
In this work, the effect of the inoculation of different L. thermotolerans on the acidity of wine was studied.

Wine is one of the most representative components of Mediterranean diet. Moderate wine intake together with food, has been positively correlated with reduced risk of many chronic diseases. This beneficial effect seems to be ascribed to elevated polyphenolic content of wine [1]. Traditional approaches for the identification of wine biomarkers consumption include targeted metabolomics that focuses on the quantification of well-defined metabolites, losing a valuable information about a massive number of compounds. On the other hand, untargeted metabolomics can disclose a large quantity of signals corresponding to potential biomarkers in a single analysis with high sensitivity and resolution.

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).