Flavan-3-ol and polysaccharide content and profile during the fermentation of Vitis vinifera Cabernet Sauvignon and cold-hardy Vitis sp. Frontenac
Bibliographic record
Abstract
Aim: The main objective of this study was to determine the effect of grape variety on skin and seed flavan-3-ol and polysaccharide diffusion from grapes to wine during the alcoholic fermentative maceration of Vitis vinifera Cabernet Sauvignon and Vitis sp. Frontenac and Frontenac blanc, and to determine the final composition of these wines at the end of the process. Methods and results: Must and wine polysaccharides were precipitated by ethanol and quantified using the phenol-sulfuric method of Dubois. Flavan-3-ol concentration and profile were analysed by HPLC-FLD. Cold-hardy cultivars Frontenac and Frontenac blanc had less oligomeric and polymeric flavan-3-ols than those from Vitis vinifera Cabernet Sauvignon. Wines made from the cold-hardy hybrid cultivar Frontenac had a higher concentration in total polysaccharide. 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. Conclusion: Grape variety is critical to wine flavan-3-ol and polysaccharide profile. The highest values of total polysaccharides and the lowest levels of condensed tannins were observed in the wines of Frontenac. 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. Significance of the study: 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) | 0.001 | 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".