Application to grapevine leaves of different doses of urea at two phenology stage: effect on the aromatic composition of red wine

Beihong and Beimei were bred as winemaking cultivars released by Institute of Botany, the Chinese Academy of Sciences in 2008. The cultivars are selected from the population of ‘Muscat Hamburg’ (Vitis vinifera) ×V. amurensis. They are extended to most provinces in North of China because they have strong resistance to cold and disease and need not be buried in soil in winter. To better understand the effect of cropload on volatile compounds during wine-making, we surveyed volatiles composition and content of different cropload level in 3-years-old ‘Beihong’ and ‘Beimei’ vines which planted in east foot of Helan mountain of Ningxia (EHN).

Elemental composition, measured as the concentrations of different elements present in a given tissue at a given time point, is a key indicator of vine health and development. While elemental composition and other high-throughput phenotyping approaches yield tremendous insight into the growth, physiology, and health of vines, costs and labor associated with repeated measures over time can be cost-prohibitive. Recent advances in handheld sensors that measure hyperspectral reflectance patterns of leaf tissue may serve as an affordable proxy for other types of phenotypic data, including elemental composition. Here, we ask if reflectance patterns of dried Chambourcin leaf tissue from an experimental grafting vineyard can predict the known elemental composition of those leaves.

Viticulture is exposed to a range of new stressors, that are challenging its sustainability and disrupting famous and well-established production regions. Steady increase of average temperature, recurring heat waves, altered rainfall seasonal distribution, drought spells, increased pathogens pressure, they all mix up with increased frequency, making every growing season a special challenge and calling for new approaches to cope with worrying scenarios.

While SO2 is one of the oldest and widest additive used in enology for its well-known antioxidant, anti-laccase and antimicrobial properties, it can cause health problems in some individuals.

Grape harvest time is one of the most fundamental aspects that affect grape quality and thus wine quality. Many factors influence the decision of harvest; among them technological and phenolic maturity of grape. Technological ripeness is mainly related to sugar concentration, titratable acidity and pH. Conventional methods for chemical analysis of grapes are normally sample-destructive, time-consuming, include laborious sample preparation steps, and generate chemical waste, thereby limiting their utility in online/in-line quality monitoring. Moreover, destructive analyses can be performed only on a limited number of fruit pieces and, thus, their statistical relevance could be limited. This study evaluated the ability of a lab-scale hyperspectral imaging (HYP-IM) technique to predict titratable acidity, organic acid and sugar content of grapes. Samples of Cabernet franc and Chenin blanc grapes were consecutively collected six times at weekly intervals after veraison. The images were recorded thanks to the hyperspectral imaging camera Pica L (Resonon) in a spectral range from 400 to 1000 nm. Statistics were performed using Microsoft Xlstat software. Successively, the berries were analyzed for their sugar (glucose and fructose) and organic acid (malic and tartaric acid) content and titratable acidity according to usual methods.