Different Temperature and UV Patterns Modulate Berry Maturation and Volatile Compounds Accumulation in Vitis sp.
Bibliographic record
Abstract
Volatile compounds (VCs) in grapevine berries play an important role in wine quality; however, such compounds and vine development can be sensitive to environmental conditions. Due to this sensitivity, changes in temperature patterns due to global warming are likely to further impact grape production and berry composition. The aim of this study was to determine the possible effects of different growing-degree day accumulation patterns on berry ripening and composition at harvest. An experimental field was conducted using Vitis sp. L'Acadie blanc, in Nova Scotia, Canada. Using on-the-row mini-greenhouses, moderate temperature increase and reduced ultraviolet (UV) exposure were triggered in grapevines during pre-veraison (inflorescence to the beginning of berry softening), post-veraison (berry softening to full maturity), and whole season (inflorescence to full maturity), while controls were left without treatment. Free and bound VCs were extracted from berries sampled at three different phenological stages between veraison and maturity before analysis by gas chromatography–mass spectrometry (GC-MS). Berries from grapevines exposed to higher temperatures during early berry development (pre-veraison and whole) accumulated significantly higher concentrations of benzene derivatives 2-phenylethanol and benzyl alcohol at harvest, but lower concentrations of hydroxy-methoxy-substituted volatile phenols, terpenes, and C 13 -norisoprenoids than the control berries. These results illustrate the importance of different environmental interactions in berry composition and suggest that temperature could potentially modulate phenylpropanoid and mevalonate metabolism in developing berries. This study provides insights into the relationships between abiotic conditions and secondary metabolism in grapevine and highlights the significance of early developmental stages on berry quality at harvest.
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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".