Targeted and untargeted 1H-NMR analysis for sparkling wine’s authenticity

The trial was conducted in variety/rootstock Riesling/Kober 5 BB in the vineyard district of Vrsac. The vineyard was planted in 1996 on a south-facing slope, with rectangular type pruning of 3×1 m. The training system is of symmetric cordon type and mixed type pruning is practiced.

Oak wood selection and maturation are essential steps in the course of barrel fabrication. Given the existence of many factors involved in the choice of raw material and in natural seasoning of oak wood, it is very difficult to determine the real impact of seasoning and selection factors on oak wood composition. A sampling was done to study the evolution of oak wood chemical composition during four seasoning steps: non matured, 12 months, 18 months and 24 months. For this sampling, three selection factors were taken into account: age, grain type and the Polyphenolic Index measured by Oakscan®. Besides extractables
(~10%), three polymers constitute the main part of oak wood: cellulose, hemicelluloses and lignins.

Favoring the formation of volatile compounds and their precursors in must and wine represent one of the principal goals during winemaking technology. In recent years, most attention has been placed on using glycosidases to enlarge the aroma profile of white wines. The effect of enzymes makes odorless glycosidically-bound precursors be converted into aromatic compounds. This paper focuses to study the influence of enzymes (pectolytic and β-glycosides) administered before alcoholic fermentation, even if most studies analyze their use in different winemaking stages. Two semi-aromatic varieties such as Fetească regală and Sauvignon blanc were chosen.

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.

This work began as an intellectual game, in order to discuss the notion of wine quality in terms of terroir and territory spatial structure. Vine and wine quality has long been questioned by scientists. Each discipline approaching it with his own tools.