The characteristics of strong territorial brands: the case of Champagne

The analysis of structurally diverse proanthocyanidins in grapes and wine is challenging. Comprehensive two-dimensional liquid chromatography (LC×LC) and ion mobility spectrometry-mass spectrometry (IMS-MS) are increasingly used to address the challenges associated with the analysis of highly complex samples such as wine and grapes

The production of grapes with a balanced composition is one of the main goals that agronomists and oenologists pursue to produce premium quality wines.

The quality of wine is assessed, among other things, by its color, which is mainly due to its anthocyanin content. These pigments are polyphenols that give red, purple and blue hues depending on the relative proportion of anthocyanins produced by the action of flavonoid 3’5′ hydroxylase (delphinidin-3-glucoside, petunidin-3-glucoside, malvidin-3-glucoside) or flavonoid 3′ hydroxylase (cyanidin-3-glucoside, peonidin-3-glucoside). To study the genes involved in this biosynthetic pathway, we focused on Vitis vinifera cv. Tannat, known for producing wines with higher anthocyanin content and darker purple color compared to most red grape varieties. In this work, we have performed RNA-Seq analysis of skins during berry development, taking green and red berries at 50% veraison as separate samples, as an experimental strategy to focus on the differential expression of genes of interest.

In 2023–2024, the Republic of Moldova launched its first AI-powered wine pilot, integrating artificial intelligence into the vitivinicultural value chain.

Knowledge on the reflectance spectrum of soil is potentially useful since it carries information on soil chemical composition that can be used to the planning of agricultural practices. If compared with analytical methods such as conventional chemical analysis, reflectance measurement provides non-destructive, economic, near real-time data. This paper reports results from reflectance measurements performed by spectroradiometry on soils from two vineyards in south Brazil. The vineyards are close to each other, are on different geological formations, but were subjected to the same management. The objective was to detect spectral differences between the two areas, correlating these differences to variations in their chemical composition, to assess the technique’s potential to predict soil attributes from reflectance data.To that end, soil samples were collected from ten selected vine parcels. Chemical analysis yield data on concentration of twenty-one soil attributes, and spectroradiometry was performed on samples. Chemical differences significant to a 95% confidence level between the two studied areas were found for six soil attributes, and the average reflectance spectra were separated by this same level along most of the observed spectral domain. Correlations between soil reflectance and concentrations of soil attributes were looked for, and for ten soil traits it was possible to define wavelength domains were reflectance and concentrations are correlated to confidence levels from 95% to 99%. Partial Least Squares Regression (PLSR) analyses were performed comparing measured and predicted concentrations, and for fifteen out of 21 soil traits we found Pearson correlation coefficients r > 0.8. These preliminary results, which have to be validated, suggest that variations of concentration in the investigated soil attributes induce differences in reflectance that can be detected by spectroradiometry. Applications of these observations include the assessment of the chemical content of soils by spectroradiometry as a fast, low-cost alternative to chemical analytical methods.