Towards Accelerated Autolysis? Dynamics of Phenolics, Proteins, Amino Acids and Lipids in Response to Novel Treatments and during Ageing of Sparkling Wine
Bibliographic record
Abstract
Premium sparkling wine produced by the traditional method (analogous to the French méthode champenoise) is characterised by the development of aged wine character as a result of a second fermentation in the bottle with lees contact and lengthy ageing. Treatments (microwave, ultrasound, or β-glucanase enzymes) were applied to disrupt the cell wall of Saccharomyces cerevisiae and added to the tirage liquor for the second fermentation of Chardonnay-Pinot Noir base wine cuvée and compared to a control, to assess effects on the release of phenolics, proteins, amino acids, and lipids at 6, 12 and 18 months post-tirage. General responses to wine ageing included a 60% increase in the total phenolic content of older sparkling wines relative to younger wines and an increase in protein concentration from 6 to 12 months bottle age. Microwave and β-glucanase enzyme treatments of yeast during tirage preparation were associated with a 10% increase in total free amino acid concentration and a 10% increase in proline concentration at 18 months bottle age, compared to control and ultrasound treatment. Furthermore, microwave treatment was associated with elevated asparagine content in wine at 18 months bottle age, relative to the control and the other wines. The β-glucanase enzyme and ultrasound treatments were associated with significant accumulation of total lipids, which were driven by 2-fold increases in the phospholipid and monoacylglycerol components in wine at 18 months bottle age and, furthermore, the microwave treatment was associated with elevated triacylglycerol at 18 months bottle age. This study demonstrates that the use of yeast treatments at the tirage stage of sparkling wine production presents an opportunity to manipulate wine composition.
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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 | 0.000 | 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".