qNMR metabolomics a tool for wine authenticity and winemaking processes discrimination

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.

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.

Vineyard floors in warm, dry landscapes including those in South Australia, have traditionally been managed using intensive practices such as tillage and herbicides to control weeds and vegetation, thereby limiting competition with grapevines for water and nutrients in order to not compromise yields.

The content of 3-sulfanyl-1-hexanol and its acetate ester in wine is affected by a number of factors, including the concentration of its precursors S-3-(hexan-1-ol)-L-glutathione (G-3SH),

Over the past two years, the AWRI has received 69 wine samples suspected of being affected by mousy off-flavour. The character has been mostly observed in white wines.