Changes in the flavan-3-ol and polysaccharide content during the fermentation of <i>Vitis vinifera</i> Cabernet-Sauvignon and cold-hardy <i>Vitis</i> varieties Frontenac and Frontenac blanc
Bibliographic record
Abstract
Grape variety has a significant impact on wine flavan-3-ol and polysaccharide profile. The main objective of this work was to study differences in flavan-3-ol and polysaccharide diffusion from grape to wine during the fermentative alcoholic maceration of three Vitis sp. varieties: the cold-hardy hybrid varieties Frontenac and Frontenac blanc, and the V. vinifera variety Cabernet-Sauvignon. Polysaccharides from must and wine were precipitated by ethanol and quantified using the phenol-sulfuric method of Dubois. Flavan-3-ol concentration and profile were analysed by HPLC-FLD. Results showed that wines from Frontenac and Frontenac blanc had less oligomeric and polymeric flavan-3-ols than those from V. vinifera Cabernet-Sauvignon. Wines made from Frontenac also had a higher concentration in total polysaccharides. Preliminary results from GPC/SEC analyses suggested that Frontenac wine had a higher content in mannoproteins and rhamnogalacturonan-2 polysaccharides compared to the other studied varieties. Overall, wines of Frontenac showed the highest content in total polysaccharides, and the lowest content in condensed tannins. As polysaccharides are known to negatively impact wine perceived astringency, these results suggest that significant attention should be given to the polysaccharide composition of cold-hardy cultivars in the context of cold climate wine production. Such knowledge may help winemakers from cold climate areas to improve the winemaking processes and final wine composition when working with cold-hardy Vitis sp. varieties.Knowledge on interspecific hybrid polysaccharide and flavan-3-ol kinetic during the alcoholic fermentative maceration may help the winemakers from cold climate areas to improve winemaking processes and final wine composition.
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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) | 0.000 | 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 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".