Sensorial and Volatile Analysis of Wines Made from Partially Dehydrated Grapes: An Ontario Case Study
Bibliographic record
Abstract
Winemaking in cool climate viticultural areas can pose challenges due to difficulties in achieving optimal ripeness from climatic conditions that tend to vary vintage-to-vintage. To stabilize quality, the use of partially dehydrated grapes has been indicated as beneficial to the production of high-quality wine (“appassimento” style) despite climatic variation. Postharvest wine grape dehydration is a complex process that involves the concentration or formation of sugars, aromas, and flavours. One of the quality challenges facing appassimento style winemaking is elevated levels of undesirable oxidation compounds. The aim of this study was to characterize wines made from a local yeast isolate, Saccharomyces uvarum CN1, which demonstrates limited osmotolerance and may have application to this wine style, as it is a known lower producer of such compounds. Wines made with CN1 were compared to wines made with the accepted commercial standard, S. cerevisiae, EC1118. Fermentations (n = 24) were established at three target starting sugar concentrations from dehydrated Cabernet franc grapes (24.5, 26.0, and 27.5°Brix) and a control (21.5°Brix) and were assessed for volatile organic compound (VOC) composition via gas chromatography-mass spectrometry (GC-MS). Wines also underwent quantitative descriptive analysis to identify and quantify sensory attributes by a trained panel (n = 11). Results show that the wines fermented with the yeast isolate contain significant differences in the concentrations of VOCs in the wines. Sensorially, the wines differed in intensity for a number of attributes, including red fruit aroma, black fruit flavour, and length of finish both within Brix treatments and amongst yeast strains. The most important differentiating factor amongst these wines was the combination of yeast strain at the highest starting sugar concentration (27.5°Brix). These findings may assist winemakers by informing the yeast strain choice for optimizing appassimento style wine quality in cool climates.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".