Flavour Development in the Vineyard: Impact of Viticultural Practices on Grape Monoterpenes and their Relationship to Wine Sensory Response
Bibliographic record
Abstract
Monoterpenes are responsible for the distinctive flavour of grape cultivars such as Gewiirztraminer, Riesling and several muscat cultivars. These components are present as odour-active free volatile terpenes (FVT) and as potentially volatile terpenes (PVT), i.e. glycosides and polyols capable of releasing FVT by temperature-, pH-, or enzyme induced hydrolysis. Our first work focused on the impact of fruit exposure on terpene concentrations in Gewiirztraminer. Fully-exposed fruit consistently displayed higher FVT and PVT than partially-exposed and fully-shaded fruit. This knowledge was utilised to investigate effects of cultural practices. Hedging and basal leaf removal (BLR) increased FVT and PVT levels, while multi-site experiments also indicated that hedging and BLR could increase FVT and PVT in berries and musts of early-season cultivars such as Bacchus, Pearl of Csaba, Gewiirztraminer, Schonburger and Siegerrebe. Canopy division, BLR and increased vine spacing also increased FVT and PVT concentrations in Riesling fruit. Low-heat unit sites appear to promote accumulation of monoterpenes in Vitis vinifera more than warmer sites, when compared at equal growing degree days. Prefermentation practices such as delayed harvest, prolonged pressing and skin contact were also shown to increase must terpene content. In many cases, these differences in terpene concentrations in the berries and musts were organoleptically detectable in wines. Our conclusions to date are: (1) PVT are more responsive to viticultural and enological practices than FVT; (2) FVT and PVT are rarely correlated with soluble solids, titratable acidity or pH, and thus cannot be predicted by standard harvest indices; (3) Losses in FVT and PVT can occur between the berry and juice stages, hence the desirability of skin contact; (4) FVT and PVT concentrations can, in some cases, be related to wine-tasting results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".