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 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 | 0.000 |
| 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) | 0.000 | 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".