Differential influence of timing and duration of bunch bagging on volatile organic compounds in Cabernet Sauvignon berries (<scp><i>Vitis vinifera</i></scp>L.)
Bibliographic record
Abstract
Background and Aims Bunch bagging is a grapevine canopy management strategy to protect bunches from light, heat stress and disease pressure. The aim of this study was to unveil the effects of timing and duration of bunch bagging on grape volatile organic compounds (VOCs) at the transcriptomic and metabolic levels. Methods and Results Seven bunch bagging treatments of varying timing and duration were implemented with Cabernet Sauvignon grapes. Regardless of timing and duration, bagging significantly reduced nerol, benzaldehyde, benzeneacetaldehyde and p-cymene, and increased the concentration of phenol and 3,5-dimethylbenzaldehyde. The decrease of nerol by bagging was associated with the down-regulation of the gene encoding glycosyltransferase14 at veraison. Bagging and re-exposing bunches at the preharvest stage inhibited and benefited the accumulation of C6 alcohols. Furthermore, we infer that the higher concentration of total benzenoids in all bagged berries was due to the down-regulated expression of phenylpropanoid biosynthesis genes. The concentration of the dominant norisoprenoid, β-damascenone, was not impacted by bunch bagging from veraison to harvest and was increased by bagging from veraison to post-veraison. All other treatments, however, reduced this compound, which was due to a lower concentration of β-carotene and lutein, and the down-regulation of VviCCD4a and VviCCD4b. Conclusions Both timing and duration of bunch bagging can affect biosynthesis and accumulation of VOCs. Significance of the Study Insights from this study provide new knowledge on bunch bagging, as well as information regarding the sensitivity of VOCs to light and the timing of light exposure during grape ripening.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".