Odor Potency of Aroma Compounds in Riesling and Vidal blanc Table Wines and Icewines by Gas Chromatography–Olfactometry–Mass Spectrometry
Bibliographic record
Abstract
This study aimed to elucidate the odor potency of aroma compounds in Riesling and Vidal blanc (syn. Vidal) table wines and icewines from the Niagara Peninsula using stir bar sorptive extraction-gas chromatography-olfactometry-mass spectrometry. Dilution analysis determined the most odor-potent compounds in Vidal and Riesling icewines (n = 2) and table wines (n = 2) from a commercial producer. The top 15 odor-potent compounds in each wine were identified and quantified, resulting in 23 and 24 compounds for Riesling and Vidal, respectively. The most odor-potent compounds were β-damascenone, decanal, 1-hexanol, 1-octen-3-ol, 4-vinylguaiacol, ethyl hexanoate, and ethyl 3-methylbutyrate. In general, icewines had higher concentrations of most aroma compounds compared to table wines. Through computation of odor activity values, the compounds with the highest odor activity for the icewines were β-damascenone, 1-octen-3-ol, ethyl octanoate, cis-rose oxide, and ethyl hexanoate. In table wines the highest odor activity values were found for ethyl octanoate, β-damascenone, ethyl hexanoate, cis-rose oxide, ethyl 3-methylbutyrate, and 4-vinylguaiacol. These findings provide a foundation to determine impact odorants in icewines and the effects of viticultural and enological practices on wine aroma volatile 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".