Natural and Artificial Oxidation of Grape Seed Phenolics are Influenced by Extractability and Galloylation Pattern
Bibliographic record
Abstract
Abstract In cool-climate viticulture, the short growing season can influence grape seed maturation by reducing the apparent oxidation of flavan-3-ols and associated increase in seed browning. A reduction in seed maturation increases the potential extraction of flavan-3-ols into wine during maceration operations, heightening bitterness. Here, we carried out a 2x2 factorial experiment to test the ability of freezing and heating treatments to artificially “ripen” seeds (decrease flavan-3-ols, improve browning) of (Vitis vinifera L.) Pinot noir and Cabernet Sauvignon over a 24-hour incubation period. Only freezing significantly increased seed browning in both cultivars. Subsequent correlations with seed flavan-3-ols concentrations suggest that freezing enhanced the oxidation of these compounds. Interestingly, natural ripening and freezing reduced galloylated flavan-3-ols to a greater extent than non-galloylated ones. This study provides new information regarding the susceptibility of flavan-3-ols to freezing and heating, and also suggests that freezing can artificially ripen the seeds of under-ripe red vinifera grapes.
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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".