The effect of inactivated yeast-based products on the process of wine aging, phenolic compounds and sensory characteristics of red wine Prokupac
Bibliographic record
Abstract
Keeping of red wine in bottles is very important for its maturation and quality. However, there are numerous changes that happen during that period, usually caused by oxidative processes and changes in structure and content of polyphenolic compounds. The goal of this study was to determine the effects of Inactivated dry yeast (IDY) products on aging, phenolics content, colour stability and sensory characteristics of red wine Prokupac (Serbian autochthonous variety). The treatment of wine was done by 3 different IDYP (Lallemand, Canada): Optimum White, Opti Less and Noblesse, applied as 0.2 g/L and 0.4 g /L during 15 days. Subsequently, wine clarification was done, followed by filtration and bottling. Untreated wines were used for comparison. Wine was subjected to accelerated aging (10 days at 55 ° C) and also to normal aging conditions during one year. To determine the influence of IDYP following parameters were monitored: dissolved O2, free and total SO2, sugar free extract, content of total phenolics, flavonoids, flavan-3-ols and anthocyanins, percent of polymeric color, color tint, color intensity and sensory characteristics (Panel method). The obtained results clearly show that IDY products are good scavengers of oxygen and have a positive impact on wine quality preservation and its organoleptic characteristics. However, a slight decrease of polyphenols content was detected.
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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".