Volatile Compounds from Grape Skin, Juice and Wine from Five Interspecific Hybrid Grape Cultivars Grown in Québec (Canada) for Wine Production
Bibliographic record
Abstract
Developed from crosses between Vitis vinifera and North American Vitis species, interspecific hybrid grape varieties are becoming economically significant in northern areas, where they are now extensively grown for wine production. However, the varietal differences between interspecific hybrids are not well defined, nor are the relationships between hybrid grape and wine composition, which causes significant drawbacks in the development of viticulture and winemaking of northern wines. In an effort to increase our understanding of interspecific hybrids, we have characterized the free volatile compounds profiles of berries (juice and skin) and wines of five red hybrid varieties (Frontenac, Marquette, Maréchal Foch, Sabrevois and St. Croix) grown in Québec (Canada), using GC-MS(TOF)-SPME. In grapes and wines, significantly higher levels of C6 and other fatty acid degradation products (FADP) were found in Frontenac, Maréchal Foch and Marquette. Terpenes were primarily located in the skin, with Marquette showing the highest level for these compounds. Both the level of terpenes and the level of FADP in grape were strongly correlated with their respective levels in wine, as demonstrated by the redundancy analyses. Nonanal, (E,Z)-2,6-nonadienal, β-damascenone, ethyl octanoate and isoamyl acetate showed the highest OAVs in the wines of the studied varieties.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".