Red wine oxidation: oxygen consumption kinetics and high resolution uplc-ms analysis

The chemical composition of grape berries at harvest is one of the most important factors that should be considered to produce high quality wines. Among the different chemical classes which characterize the grape juice, the polyphenolic compound, such as flavonoids, contribute to the final taste and color of wines. Recently, an innovative non-destructive method, based on chlorophyll fluorescence, was developed to estimate the phenolic maturity of red grape varieties through the evaluation of anthocyanins accumulated in the berry skin. To date, only few data are available about the application of this method on white grape varieties.

Grignolino, is a native Piedmont grape variety which well represents the historical and
enological identity of Monferrato, a territory between Asti and Casale Monferrato, included in the World Heritage List designated by UNESCO (1).

Grapevine yield is a key indicator to assess the impacts of climate change and the relevance of adaptation strategies in a vineyard landscape. At this scale, a yield model should use a number of parameters and input data in relation to the information available and be able to reproduce vineyard management decisions (e.g. soil and canopy management, irrigation). In this study, we used data from six experimental sites in Southern France (cv. Syrah) to calibrate a model of grapevine yield limited by water constraint (GraY). Each yield component (bud fertility, number of berries per bunch, berry weight) was calculated as a function of the soil water availability simulated by the WaLIS water balance model at critical phenological phases. The model was then evaluated in 10 grapegrowers’ plots, covering a diversity of biophysical and technical contexts (soil type, canopy size, irrigation, cover crop). We identified three critical periods for yield formation: after flowering on the previous year for the number of bunches and berries, around pre-veraison and post-veraison of the same year for mean berry weight. Yields were simulated with a model efficiency (EF) of 0.62 (NRMSE = 0.28). Bud fertility and number of berries per bunch were more accurately simulated (EF = 0.90 and 0.77, NRMSE = 0.06 and 0.10, respectively) than berry weight (EF = -0.31, NRMSE = 0.17). Model efficiency on the on-farm plots reached 0.71 (NRMSE = 0.37) simulating yields from 1 to 8 kg/plant. The GraY model is an original model estimating grapevine yield evolution on the basis of water availability under future climatic conditions.  It allows to evaluate the effects of various adaptation levers such as planting density, cover crop management, fruit/leaf ratio, shading and irrigation, in various production contexts.

The purpose of this study is to compare the skin resistance (SR) of the grapes with physico-chemical propertiess using a stong dataset and multidimentional statistical analysis .
A recent study has shown the role skin resistance plays against pest invasion but skin resistance could be a useful agronomic parameter, for example in the choice of the type of winemaking, by influencing the quantity of juice during crushing and maceration.

Context and purpose of the study. The seasons immediately following planting are key growth stages where young vines are particularly susceptible to various forms of damage.