Understanding and managing wine production from different terroirs

One of the major challenges for food security and sovereignty is to produce stress-tolerant plants without introducing foreign DNA, because the legislative process, that bans transgenics, challenges us to find new solutions for producing plants that can survive the drought. To achieve this goal, we need to identify genes that can be modified to improve stress tolerance in plants. In this work, we present an online tool for exploring the transcriptome of grapevines under water stress, which is one of the most important abiotic stresses affecting viticulture. The tool is based on a comprehensive collection of rna-seq data from 997 experiments, covering four different tissues (leaf, root, berry, and shoot), various levels of water stress, and diverse genetic backgrounds (cultivars and rootstocks) with different levels of tolerance to water stress.

Sensory analysis of grape berries is a common tool to evaluate the degree of grape maturation and to make sound picking decisions.

Context and Purpose. Climate change presents significant challenges to agricultural sustainability, particularly through the increasing frequency of drought and water scarcity.

The use of oenological additives is becoming a common practice due to the technological and sensory properties they provide to the wines. However, the number of studies focused on the impact that these additives might induce on wine flavor perception during wine tasting is still quite scarce. The aim of this work was to evaluate the effect of three different types of common oenological additives: two oenotannins (ellagitannin and gallotannin) and a commercial preparation of yeast mannoproteins on the long-lasting flavor perception (aroma and astringency).

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).