Nutrient Addition to Low pH Base Wines (L. cv. Riesling) during Yeast Acclimatization for Sparkling Wine: Its Influence on Yeast Cell Growth, Sugar Consumption and Nitrogen Usage
Bibliographic record
Abstract
In traditional method sparkling wine production, to carry out a successful second alcoholic fermentation, yeast are acclimatized to stressful base wine conditions. Base wines typically have low pH, low nutrient concentrations, high acid concentrations, contain sulfur dioxide (SO2), and high ethanol concentrations. Supplementing yeast during the acclimatization stages prior to second alcoholic fermentation with different nutrient sources was assessed to determine the impact on yeast growth, sugar consumption and nitrogen usage. Four treatments were tested with Saccharomyces cerevisiae strain EC1118: the control (T1) with no additives; addition of diammonium phosphate (DAP) during acclimatization, (T2); Go-Ferm® inclusion during yeast rehydration (GF), (T3); and DAP + GF (T4). Results (n = 4) indicated that supplementing with DAP, GF or DAP + GF increased both the rate of sugar consumption and the concentration of viable cells during the yeast acclimatization phase in comparison to the control. Treatments supplemented with DAP + GF or DAP alone resulted in yeast consuming 228 and 220 mg N/L during the acclimatization phase, respectively. Yeast treated only with GF consumed 94 mg N/L in comparison to the control, which consumed 23 mg N/L. The time required to reach the target specific gravity (1.010) during acclimatization was significantly reduced to 57 h for yeast treated with DAP and GF, 69 h for yeast treated with DAP only and 81 h for yeast rehydrated with GF in comparison to 105 h for the control. Our results suggest that nutrients used during yeast acclimatization could have an important impact on the kinetics of second alcoholic fermentation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".