The Effect of Pre-Veraison Smoke Exposure of Grapes on Phenolic Compounds and Smoky Flavour in Wine
Bibliographic record
Abstract
Background and Aims. Smoke exposure occurred in the Adelaide Hills region in December 2019 due to a wildfire, when wine grapes were peppercorn-size green berries. Previously, pre-veraison smoke exposure had been identified through model experiments as unlikely to affect grape composition, whereas smoke exposure after veraison can have a major effect on wine flavour. Hence the effects of pre-veraison smoke on grape and wine composition, and smoky sensory properties of wine were investigated. Methods and Results. Chardonnay, Pinot Noir and Shiraz were investigated and eight blocks with varied smoke exposure were selected for each cultivar. Berries were sampled initially four weeks after the fire and at harvest, and mature grapes were made into unoaked wines. Established smoke exposure markers, phenolic glycosides, were found in berries at pre-veraison and at harvest from the high smoke exposure sites, with concentrations well above those found in non-smoke exposed fruit. Volatile phenols were also elevated in grapes at harvest. The resulting red wines from some exposure vineyards were high in volatile phenols, glycosides and smoky flavours. However, most of the Chardonnay wines expressed much less smoky flavours, despite similar levels of smoke exposure of grapes. Conclusions. Pre-veraison smoke exposure can result in elevated concentrations of volatile phenols and their glycosidic metabolites in grape berries and wine and cause strong smoky flavour in wine. Significance. The wine sector and land management agencies responsible for controlled burns need to consider the effect of smoke from fires near vineyards even very early in the growing season.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".