Yeast-induced changes in the concentration and structure of oligomeric proanthocyanidins during simulated wine fermentation
Bibliographic record
Abstract
Background and Aims Proanthocyanidins (PAs), the predominant phenolic substances in wine, are responsible for the astringency of wine. The present work aims to understand the effect of yeast on the concentration and composition of the subunits of PAs during fermentation. Methods and Results The concentration and cleavage products (subunits) of PAs were measured during fermentation. Early in fermentation (within about 4 days), the concentration of PAs decreased significantly by around 78% for strain BH8 and 67% for strain AWRI R2; in addition, the mean degree of polymerisation and composition of PAs underwent considerable change. At the later phase of fermentation, no significant change was recorded in the concentration and mean degree of polymerisation of PAs, whereas the composition of PAs evolved progressively. Conclusions Yeast can exert a significant influence on the concentration and structure of PAs during wine fermentation in a strain- and time-dependent manner. Significance of the Study The results will enable a better understanding of the effect of yeast on the evolution of PAs in winemaking, as well as increase knowledge of the interaction between yeasts and phenolic substances to improve wine composition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".