Winemaking techniques to manage tannins of Cabernet sauvignon and Pinot noir wines made in Ontario, Canada
Bibliographic record
Abstract
Seed tannins are responsible for the bitter and astringent mouthfeel in red wines (Smith et al., 2015; Arnold et al., 1980). Skin tannins are mostly responsible for colour stability of red wines (Kennedy, 2008). Tannins are present in different amount in the grape berries, and they vary in concentration from one variety to another (Bautista-Ortin, 2005; Kennedy, 2008). This project, which is part of a bigger project called TanninAlert: Improve Ontario red wine quality and consumer acceptance through winemaking techniques by grape variety and tannin level, aims to define winemaking techniques that improve the skin tannin extraction while minimizing seed tannin extraction in order to improve the color and mouthfeel of red wine produced in Ontario. Winemaking techniques were selected based on the concentration of skin and seed tannins in the grapes at harvest. Pinot noir and Cabernet sauvignon have been the focus of this study for two vintages (2019 and 2020). The data collected through out ripening contributed to the categorization of the tannin in Cabernet Sauvignon and Pinot Noir on a scale from low to high. Specific winemaking techniques were applied to each of the varieties. For Pinot noir wines, the focus was put on the addition of skin tannins and two kinds of macerating enzymes: LAFASE He Grand Cru (Laffort, France) and ULTRASI DarkBerry (A.O. Wilson, Canada). Results showed no significant impact on total extractable tannin extraction in the wine when skin tannin were added. On the other hand, a significant increase in total extractable tannins was observed when pectolytic enzymes were added during fermentation. The addition of the DarkBerry enzyme showed the best tannin retention over time in the wines. For Cabernet Sauvignon, juice removal (Saignée 14%), pre-fermentation pressing of berries and addition of DarkBerry enzyme were studied. Results from the Saignée treatment were inconclusive; it increased the extraction in 2019, but not in 2020. The pre-fermentation pressing of the grapes showed no impact on total extractable tannins. When both treatments (Saignée + addition of DarkBerry enzyme) were applied to the wines, the total extractable tannin concentration was significantly higher than in the control wines.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".