Management of varietal thiols in white and rosé wines using biotechnical tools

The introduction of ai-driven tools in digital content creation represents a significant shift in the landscape of marketing, particularly for industries reliant on rich visual storytelling such as the wine sector. The development of ai models like openai’s sora, runway’s gen-2 or google’s lumiere, which can generate realistic video content from textual descriptions, offers promising new avenues for enhancing brand narrative and consumer engagement. This research explores the potential of text-to-video (t2v) ai models to revolutionize wine marketing by creating dynamic, engaging content that captures the essence of vineyards and their products without the need for traditional video production processes.

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 1365 ha of the Genevese vineyard are located at the south-western corner of the Swiss plateau, between 395m and 505 m altitude.

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.

Temperature fluctuations can significantly affect the chemical composition of wine and in turn its taste and aromas.