Harvest date effects on aroma compounds in aged Riesling icewines
Bibliographic record
Abstract
BACKGROUND: Riesling icewine is an important product of the Ontario wine industry. The objective of this study was to characterize concentrations in aroma compounds in aged icewines associated with three harvest dates (H1, H2, H3) using stir bar sorptive extraction-gas chromatography-mass spectrometry and to make inferences, where appropriate, with respect to their roles in potential wine quality. RESULTS: Delaying harvest decreased concentrations of many odorants, but increased many critical odor-active compounds; e.g. 1-octen-3-ol, ethyl benzoate, ethyl octanoate, cis-rose oxide, and β-ionone. H1 wines had higher concentrations of four aldehydes, three alcohols, nine esters, seven terpenes, γ-nonalactone, p-vinylguaiacol, β-damascenone, and 2-furanmethanol. However, many of these compounds, with some exceptions, have relatively high odor thresholds. Fourteen compounds were above their odor thresholds, including decanal, 1-octen-3-ol, phenylethyl alcohol, four ethyl esters, cis-rose oxide, linalool, γ-nonalactone, p-vinylguaiacol, ethyl cinnamate, β-damascenone, and 1,1,6-trimethyl-1,2-dihydronaphthalene. H3 wines contained higher concentrations of highly odor-active compounds, e.g. 1-octen-3-ol, cis-rose oxide, and β-ionone. Only phenylethyl alcohol [H3 odor activity value (OAV) = 0.33 (honey, spice, rose)] and linalool [H3 OAV = 0.92 (floral, lavender)] had H3 OAVs < 1. CONCLUSIONS: Early harvest increased many esters and aliphatic compounds, but delayed harvest appeared to substantially increase concentrations of several highly odor-active compounds. © 2016 Society of Chemical Industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".