Crop Level and Harvest Date Impact on Four Ontario Wine Grape Cultivars. II. Wine Aroma Compounds and Sensory Analysis
Bibliographic record
Abstract
Pinot gris, Riesling, Cabernet franc and Cabernet Sauvignon from a vineyard in Virgil, Ontario, Canada were subjected by cluster thinning to two crop-level treatments (full crop, half crop), combined with threeharvest dates (commercial harvest (T0), three weeks after T0 (T1), six weeks after T0 (T2)) in a randomised experiment in 2011 and 2012, with wines produced from all treatment replicates. We hypothesised that afull crop with substantially delayed harvest date might have a greater impact on wine aroma than reducing crop level. Wine aroma volatile analysis was carried out by GC-MS, with quantification by calibration withanalytical standards prepared in model wine. Delayed harvest overcame the effects of crop reduction for almost all volatiles. Pinot gris and Riesling displayed increases in varietal aromas in the T1 and T2 wines, e.g. monoterpenes and norisoprenoids, and in esters, aldehydes and alcohols. Reduced concentrations of volatile acids and green odour compounds (e.g. 1-hexanol) with delayed harvest were also evident. Increases in ethanol were related to increased berry sugars, but higher alcohols did not necessarily increase with harvest date. Crop level had little impact on the sensory properties of these cultivars in both seasons. However, delayed harvest resulted in substantial sensorial changes. Due to these chemical and sensorial effects, delayed harvest is recommended, rather than crop-level reduction, to enhance wine quality in these cultivars. Extended harvest, e.g. T2 treatments, was associated with the production of benzaldehyde, diethyl acetal, and higher concentrations of higher alcohols, e.g. isoamyl alcohol and nonanol, which could be linked to pre-harvest desiccation.
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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 | 0.000 |
| 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) | 0.000 | 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".