Enhancing fermentation performance through the reutilisation of wine yeast lees
Bibliographic record
Abstract
Extensive research has been dedicated to elucidating the role of various nitrogen sources, nitrogen concentrations and the timing of addition when modulating grape must fermentation using yeast. The wine industry invests substantial resources in both the vineyard and the winery to provide adequate nitrogen concentrations for fermentation. This approach ensures optimal yeast performance during fermentation and minimises the risk of negative sensory attributes associated with poor ferment nutrition. In addition to wine, the winemaking process produces a substantial quantity of nutrient-rich biomass, a poorly explored resource that, if appropriately recycled, could be used to support the nutrient requirements of other winery fermentations. This study explored the feasibility of using processed yeast lees generated during alcoholic fermentation as a nutrient supplement in subsequent fermentations. Three lees treatment options were assessed: accelerated autolysis, enzymatic lysis and mechanical lysis. The ability of these treatments to achieve complete lysis of yeast cells and release amino acids and trace elements is reported. The addition of processed lysates into grape juice was shown to improve fermentation timeframes and influence the production of yeast-derived fermentation volatile compounds in a dose-dependent manner. This study demonstrates that recycling spent lees from winery waste is feasible and provides some strategies for extracting nutrients from winery waste.
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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 | 0.000 |
| 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".