Can wine composition predict quality? A metabolomics approach to assessing Pinot noir wine quality as rated by experts

The quality of bottled white wines is highly influenced by their storage conditions, mainly temperature, and exposure to light and oxygen (1, 2).

This paper focuses on how taste in wine (and other foods) changes and the implications of this process
for producers and merchants.
It draws primarily on the changing taste of and taste for champagne in Britain in the 19th century. Between 1850 and 1880 champagne went from a dosage level of around 20% (20 grams sugar / litre) to 0%. Champagne became the ‘dinner wine of the elite – drunk with roast meat and savoury dishes.
Contemporaries accepted that while most people could distinguish the taste of good champagne from that of bad, very few could distinguish very good from good.

The climate has warmed over the past century or more bringing about changes in numerous aspects in both earth and human systems

Riesling is the iconic grape variety of Germany and accounts for 23% of the German viticulture acreage, which comprises 45% of the worldwide Riesling plantings.

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.