Optimization of extraction of antioxidants from aromatic herbs and their synergistic effects in a lipid model system
Bibliographic record
Abstract
Response surface methodology was applied to improve the polyphenol extraction process of rosemary, oregano, sage, and thyme. Aqueous ethanol (EtOH 50%) rendered the highest polyphenol extraction yield for all tested samples. Based on their total phenolic contents, rosemary, oregano, and thyme were selected for evaluation of their scavenging activities towards DPPH radical and ABTS radical cation and application in an oil model system. All extracts decreased the production of primary oxidation compounds during Schaall oven test storage. The induction period, as evaluated by the Rancimat test, was also reduced. There was an agreement between both oil model system assays, and rosemary extract showed the highest antioxidant capacity, followed by thyme and oregano. A centroid simplex design was used to evaluate the synergistic effect among the samples. Rosemary was able to play a synergistic effect when combined with thyme and oregano, or when used in binary mixtures.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".