Impact of Commercial Inactive Yeast Derivatives on Antiradical Properties, Volatile and Sensorial Profiles of Grašac Wines
Bibliographic record
Abstract
This study shows the impact of three different commercial inactive yeast derivatives (IYDs) (Opti Less™, Noblesse™, Optimum White™, Lallemand, Canada and Oenolees MP™ Lafort, USA) during the 6-month aging period on the volatile profile, sensory attributes and antiradical activity, including polyphenols and the total free sulfhydryl (-SH groups) content, of Grašac wines made in sequential fermentation with native Hanseniaspora uvarum S-2 and Saccharomyces cerevisiae QA23. The addition of IYDs helped in maintaining the constant values of antiradical activity during aging by increasing polyphenolic values and mitigating the decrease in -SH groups. HS-SPME-GC-MS analysis showed that esters were the major volatile compounds, with ethyl-acetate and 2-phenyl-ethyl-acetate being the most abundant among all the samples, followed by ethyl-dodecaonate, ethyl-decanoate and 3-methyl-butyl-octanoate, all of them contributing to fruity and floral aromas in wine. As the concentration of IYDs increased, a corresponding rise in the levels of certain volatiles, such as 2-methyl-1-propanol, phenyl-ethyl-alcohol and ethyl-octanoate, was observed. Sensory analysis showed that the addition of IYDs generally improved the taste and odor profile of the wine by reducing astringency and increasing fullness and complexity, regardless of the IYD type. The results demonstrated that different IYDs may have varying effects on wine, with each product having its specific purposes, providing the tools for winemakers to carefully regulate and obtain the desired sensory profile of the wine.
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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 | 0.000 |
| 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".