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The exploitation of secondary metabolic pathways of non-Saccharomyces yeasts is a promising approach to protect traditional wines from the ongoing climate change, which can alter their peculiar features by modifying the chemical composition of grape musts. In this regard, an interesting example is the sequential inoculum of Lachancea thermotolerans and Saccharomyces. Cerevisiae. The aim of the sequential inoculum is to increase titratable acidity by lactic acid accumulation, to lower pH and to reduce the alcohol and acetic acid content in wine.

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.

The assessment of wine authenticity is of great importance for consumers, producers and regulatory agencies to guarantee the geographical origin of wines and grape variety as well. Since mid-infrared (MIR) spectroscopy with chemometrics represent a suitable tool to ascertain the wine composition, including features associated with the polyphenolic compounds, the aim of this study was to generate MIR spectra of red wines to be exploited for classification of red wines based on the relationship between grape variety and wine composition. Several multivariate data analyses were used, including Principal Component Analysis (PCA), Discriminant Analysis (DA), Support Vector Machine (SVM), and Soft Intelligent Modelling of Class Analogy (SIMCA).

Traceability of agro-alimentary products is very important to certify their origin. This work aimed to characterize wines obtained by the same cultivar (Nero d’Avola and Fiano) – grown in regions with different soil and climate conditions during three vintages (2003-2005) – employing isotopic analyses (NMR and IRMS) and sensory analyses.

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.