Macrowine 2021
IVES 9 IVES Conference Series 9 The future of DMS precursors during alcoholic fermentation: impact of yeast assimilable nitrogen levels on the contents of DMSp in young wines

The future of DMS precursors during alcoholic fermentation: impact of yeast assimilable nitrogen levels on the contents of DMSp in young wines

Abstract

Some red wines develop a “bouquet” during ageing. This complex aroma is linked to quality by wine tasters1. The presence of dimethylsulfide (DMS) in those wines is implicated in the expression of “bouquet typicity”2. DMS is a result of the hydrolysis of its precursors. Several molecules, including S-methylmethionine, could constitute the precursors of DMS3. DMS can be liberated by alkaline hydrolysis and quantified by SPME-GC-MS4. The releasable DMS is designated by “DMSp”. The DMSp levels in grapes are 20 to 30 times higher than those observed in young wines5. Our question is : “What happens during the stages of fermentation?”First, DMSp levels were studied during a small-scale winemaking process and were measured in musts, in wine after alcoholic fermentation (AF) and after malolactic fermentation (MLF). Then, to understand the mechanism of the DMSp degradation, synthetic must was used with various levels of YAN and different pools of inorganic and organic nitrogen such as amino acids. Synthetic musts were supplemented by one of the known DMS precursor (S-methylmethionine), inoculated with S. cerevisiae and the fermentations were monitored by evaluating CO2 evolution.During AF, around 90% of DMSp is degraded by the action of yeast. The MLF consumed a little DMSp but it is negligible compared to AF. The link between DMSp and nitrogen would generate a variable consumption of DMSp during AF. Then, DMSp is consumed at the beginning of alcoholic fermentation during the yeast growth step and the level of consumption depends of the constitution of YAN. The several pools of nitrogen substances of YAN tested shows various results about the consumption or conservation of DMSp during AF.Finally, the assays in laboratory to try to control DMSp levels in young wine will help the winemakers to keep the ageing potential of red wine and maintain a high quality of wine.

DOI:

Publication date: September 14, 2021

Issue: Macrowine 2021

Type: Article

Authors

Justine Laboyrie

University of Bordeaux, ISVV, EA 4577, INRA, USC 1366 OENOLOGIE, 33140 Villenave d’Ornon, France,Marina Bely, University of Bordeaux, ISVV, EA 4577, INRA, USC 1366 OENOLOGIE, 33140 Villenave d’Ornon, France Nicolas Le Menn, University of Bordeaux, ISVV, EA 4577, INRA, USC 1366 OENOLOGIE, 33140 Villenave d’Ornon, France Stéphanie Marchand, University of Bordeaux, ISVV, EA 4577, INRA, USC 1366 OENOLOGIE, 33140 Villenave d’Ornon, France

Contact the author

Keywords

wine ageing potential, dimethylsulfide, s-methylmethionine, alcoholic fermentation, yeast assimilable nitrogen

Citation

Related articles…

Effects of graft quality on growth and grapevine-water relations

Climate change is challenging viticulture worldwide compromising its sustainability due to warmer temperatures and the increased frequency of extreme events. Grafting Vitis vinifera L.

The plantation frame as a measure of adaptation to climate change

The mechanization of vineyard work originally led to a reduction in planting densities due to the lack of machinery adapted to the vineyard. The current availability of specific machinery makes it possible to establish higher planting densities. In this work, three planting densities (1.40×0.80 m, 1.80×1 m and 2.20×1.20 m, corresponding to 8928, 5555 and 3787 plants/ha respectively) were studied with four varieties autochthonous of Galicia (northwestern Spain): Albariño and Treixadura (white), Sousón and Mencía (red). The vines were trained in a vertical shoot positioning system using a single Royat cordon, and pruned to spurs with two buds each. Agronomic data (yield, pruning wood weight, Ravaz index) and oenological data in must were collected. The higher planting density (1.40×0.80 m) had no significant effect on grape yield per vine in white varieties, although production per hectare was much higher due to the greater number of plants. In red varieties, this planting density resulted in a significantly lower production per vine, compensated by the greater number of plants. In addition, it significantly reduced the Brix degree in the must of the Albariño, Treixadura and Sousón varieties, and increased the total acidity in the latter two and Mencía. It also caused an increase in extractable and total anthocyanins and IPT in red grapes. The effects of high planting density on grapes are of great interest for the adaptation of varieties in the context of climate change. In the future, it could be advisable to modify the limits imposed by the appellations of origin on the planting density of these varieties in order to obtain more balanced wines.

Use of a new, miniaturized, low-cost spectral sensor to estimate and map the vineyard water status from a mobile 

Optimizing the use of water and improving irrigation strategies has become increasingly important in most winegrowing countries due to the consequences of climate change, which are leading to more frequent droughts, heat waves, or alteration of precipitation patterns. Optimized irrigation scheduling can only be based on a reliable knowledge of the vineyard water status.

In this context, this work aims at the development of a novel methodology, using a contactless, miniaturized, low-cost NIR spectral tool to monitor (on-the-go) the vineyard water status variability. On-the-go spectral measurements were acquired in the vineyard using a NIR micro spectrometer, operating in the 900–1900 nm spectral range, from a ground vehicle moving at 3 km/h. Spectral measurements were collected on the northeast side of the canopy across four different dates (July 8th, 14th, 21st and August 12th) during 2021 season in a commercial vineyard (3 ha). Grapevines of Vitis vinifera L. Graciano planted on a VSP trellis were monitored at solar noon using stem water potential (Ψs) as reference indicators of plant water status. In total, 108 measurements of Ψs were taken (27 vines per date).

Calibration and prediction models were performed using Partial Least Squares (PLS) regression. The best prediction models for grapevine water status yielded a determination coefficient of cross-validation (r2cv) of 0.67 and a root mean square error of cross-validation (RMSEcv) of 0.131 MPa. This predictive model was employed to map the spatial variability of the vineyard water status and provided useful, practical information towards the implementation of appropriate irrigation strategies. The outcomes presented in this work show the great potential of this low-cost methodology to assess the vineyard stem water potential and its spatial variability in a commercial vineyard.

Delaying irrigation initiation linearly reduces yield with little impact on maturity in Pinot noir

When to initiate irrigation is a critical annual management decision that has cascading effects on grapevine productivity and wine quality in the context of climate change. A multi-site trial was begun in 2021 to optimize irrigation initiation timing using midday stem water potential (ψstem) thresholds characterized as departures from non-stressed baseline ψstemvalues (Δψstem). Plant material, vine and row spacing, and trellising systems were concomitant among sites, while vine age, soil type, and pruning systems varied. Five target Δψstem thresholds were arranged in an RCBD and replicated eight times at each site: 0.2, 0.4, 0.6, 0.8, and 1.0 MPa (T1, T2, T3, T4, and T5, respectively). When thresholds were reached, plots were irrigated weekly at 70% ETc. Yield components and berry composition were quantified at harvest. To better generalize inferences across sites, data were analyzed by ANOVA using a mixed model including site as a random factor. Across sites, irrigation was initiated at Δψstem = 0.24, 0.50, 0.65, 0.93, and 0.98 MPa for T1, T2, T3, T4, and T5, respectively. Consistent significant negative linear trends were found for several key yield and berry composition variables. Yield decreased by 12.9, 15.9, 19.5, and 27.4% for T2, T3, T4, and T5, respectively, compared to T1 (p < 0.0001) across sites that were driven by similarly linear reductions in berry weight (p < 0.0001). Comparatively, berry composition varied little among treatments. Juice total soluble solids decreased linearly from T1 to T5 – though only ranged 0.9 Brix (p = 0.012). Because producers are paid by the ton, and contracts simply stipulate a target maturity level, first-year results suggest that there is no economic incentive to induce moderate water deficits before irrigation initiation, regardless of vineyard site. Subsequent years will further elucidate the carryover effects of delaying irrigation initiation on productivity over the long term.

A better understanding of the climate effect on anthocyanin accumulation in grapes using a machine learning approach

The current climate changes are directly threatening the balance of the vineyard at harvest time. The maturation period of the grapes is shifted to the middle of the summer, at a time when radiation and air temperature are at their maximum. In this context, the implementation of corrective practices becomes problematic. Unfortunately, our knowledge of the climate effect on the quality of different grape varieties remains very incomplete to guide these choices. During the Innovine project, original experiments were carried out on Syrah to study the combined effects of normal or high air temperature and varying degrees of exposure of the berries to the sun. Berries subjected to these different conditions were sampled and analyzed throughout the maturation period. Several quality characteristics were determined, including anthocyanin content. The objective of the experiments was to investigate which climatic determinants were most important for anthocyanin accumulation in the berries. Temperature and irradiance data, observed over time with a very thin discretization step, are called functional data in statistics. We developed the procedure SpiceFP (Sparse and Structured Procedure to Identify Combined Effects of Functional Predictors) to explain the variations of a scalar response variable (a grape berry quality variable for example) by two or three functional predictors (as temperature and irradiance) in a context of joint influence of these predictors. Particular attention was paid to the interpretability of the results. Analysis of the data using SpiceFP identified a negative impact of morning combinations of low irradiance (lower than about 100 μmol m−2 s−1 or 45 μmol m−2 s−1 depending on the advanced-delayed state of the berries) and high temperature (higher than 25oC). A slight difference associated with overnight temperature occurred between these effects identified in the morning.