Selection of Natural Extracts for their Antioxidant Capacity by Using a Combination of <i>In Vitro</i> Assays
Bibliographic record
Abstract
Abstract Numerous different in vitro assays, labeled as “antioxidant assays,” are used intensively to predict the antioxidant capacity of phenolic compounds. Most of these methods give valuable information in terms of chemical reactivities but also present some weaknesses. It may be difficult to use them to predict antioxidant capacity in real conditions. Indeed, lipid oxidation is a complex reaction, with numerous paths and components, and antioxidant action can occur via a multitude of mechanisms, especially when different phases coexist in the lipid‐based formulation. Yet, correctly combining selected in vitro methods to extract complementary information with respect to antioxidant behaviors would be much better and will help reducing the gap between prediction and efficacy in the finished product. Thus, we hereby propose a methodology to evaluate the antioxidant properties of 12 selected natural polyphenolic extracts based on the appropriate combination of in vitro assays. The scores obtained with the DPPH test, the measure of the chelation capacity, the evaluation of antioxidants efficiency in emulsions (CAT and VESICAT assays), or in bulk oils were submitted to a statistical treatment. This analysis allowed a ranking on their global antioxidant capacities and the creation of clusters depending on their mechanisms of action and the type of media where the tests were performed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".