Non‐traditional yeasts from cool‐climate vineyards for novel low‐alcohol wines
Bibliographic record
Abstract
Societal Impact Statement Climate warming and demand for fuller‐bodied wines has resulted in increased grape sugar contents and, therefore, increased alcohol in wines. Concurrently, there are increased warnings about the health risks of alcohol, and consumers seek low‐alcohol options as part of a healthy lifestyle. High alcohol levels can also impact the flavor balance of wines, resulting in economic losses. However, exploration of native vineyard yeasts shows that fermentation with novel species can result in both alcohol reduction and improved wine flavor. Our findings show that native yeasts have the potential to bring the North American cool‐climate wine industry into a healthier future. Summary There is recent interest in developing wines fermented with non‐traditional yeasts that reflect the microbial terroir of the grape‐growing region. These native yeasts inhabit grape skins and can produce wines with distinctive flavor profiles, and when used in concert with traditional wine yeast, the risk of incomplete fermentation is minimized. Non‐traditional yeasts can also produce wines with relatively low alcohol content, an attractive characteristic given the health and societal issues associated with overconsumption. Yeasts isolated from L'Acadie and Pinot Noir grapes grown at Nova Scotia vineyards were identified by DNA sequencing and characterized regarding fermentative properties. Promising isolates of Saccharomyces uvarum , Hanseniaspora uvarum , Wickerhamomyces anomalus , and Zygotorulaspora florentina were selected for mixed and sequential fermentations with Saccharomyces cerevisiae based on their ethanol and SO 2 tolerance, β‐glucosidase activity, and low H 2 S production. The resulting wines were evaluated for sensory properties by 15 experienced panelists. Alcohol levels of many of the fermentations that included non‐traditional yeasts were significantly lower than those made with traditional yeast alone, while fructose was higher, and total phenolic contents were similar. Experienced panelists also found that these fermentations resulted in wines with novel fruit and floral attributes. Our results demonstrate that native yeasts from cool‐climate wine regions can be used in concert with traditional wine yeast to produce novel, lower alcohol wines with a low risk of fermentation failure. This unlocks the potential of naturally occurring yeasts from these vineyards to create wines with flavor profiles that reflect the local wine‐making regions.
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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) | 0.003 | 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".