From grape berries to wines: drought impacts on key secondary metabolites
Bibliographic record
Abstract
Aim. We aimed to study the impact of water deficit on the concentration of key flavour and phenolic secondary metabolites of wines.Methods and results. A drought-stress field trial was conducted on Vitis vinifera cv. Merlot and Tocai Friulano for two seasons. Fully irrigated (C) and deficit irrigated (D) grapes were microvinified and the resulting wines were analysed to determine the concentrations of anthocyanins, tannins, and free and glycosidically-bound Volatile Organic Compounds (VOCs). A descriptive sensory test was undertaken on the same wines. Water stressed grapes produced wines with higher concentrations of anthocyanins in Merlot and of free and glycosidically-bound monoterpenes in Tocai Friulano. Both cultivars displayed higher amounts of glycosidically-bound C13-norisoprenoids.Conclusions. Previously observed drought-induced compositional changes to the grapes were transfered to the wines, with an increase in polyphenols and VOCs. However, the timing and the duration of the water stress in the field only heavily impacted the final wine composition with major metabolic modification when the severe water deficit started early (at approximately 40 days after anthesis) and lasted over the entire season until harvest.Significance and impact of the study. This study highlights the positive role of a controlled water deficit on the composition of the wines in terms of secondary metabolites.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".