Food-grade argan oil supplementation in molasses enhances fermentative performance and antioxidant defenses of active dry wine yeast
Bibliographic record
Abstract
The tolerance of the yeast Saccharomyces cerevisiae to desiccation is important for the use of this microorganism in the wine industry, since active dry yeast (ADY) is routinely used as starter for must fermentations. Both biomass propagation and dehydration cause cellular oxidative stress, therefore negatively affecting yeast performance. Protective treatments against oxidative damage, such as natural antioxidants, may have important biotechnological implications. In this study we analysed the antioxidant capacity of pure chemical compounds (quercetin, ascorbic acid, caffeic acid, oleic acid, and glutathione) added to molasses during biomass propagation, and we determine several oxidative damage/response parameters (lipid peroxidation, protein carbonylation, protective metabolites and enzymatic activities) to assess their molecular effects. Supplementation with ascorbic, caffeic or oleic acids diminished the oxidative damage associated to ADY production. Based on these results, we tested supplementation of molasses with argan oil, a natural food-grade ingredient rich in these three antioxidants, and we showed that it improved both biomass yield and fermentative performance of ADY. Therefore, we propose the use of natural, food-grade antioxidant ingredients, such as argan oil, in industrial processes involving high cellular oxidative stress, such as the biotechnological production of the dry starter.
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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) | 0.000 | 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".