A Novel Standardized Oxygen Radical Absorbance Assay for Evaluating Antioxidant Natural Products
Bibliographic record
Abstract
Functional and quantitative evaluation of analytes in crude extracts or semipure mixtures has been considered challenging due to variation in generated results. Some of the drawbacks have been linked to the texture, form, and content of the samples. The crude extracts contain multiple components and, in oxygen radical absorbance capacity (ORAC) assays, have exhibited complex reaction kinetics due to interference or matrix effect. The conventional ORAC assay utilized a single dilution factor that brought about a wide CV difference (60-110%) in antioxidant activity for each extract compared to other dilution factors applied. Due to precision errors introduced from these extremely high CV% differences, determination of antioxidant capacity based on a single dilution may introduce discrepant results. A novel standardized ORAC (SORAC) assay for the determination of antioxidant capacity of dietary plant extracts is described in this study. Using the SORAC assay, positive linear correlations (R2 = 0.95-0.99) were obtained for the sample extracts tested when a multiple dilution strategy was applied. Thus, on the strength of our observations, SORAC has been proposed as an effective tool for assessing antioxidant analytes in a biological matrix. This technique could also be modified for use in other bioanalytical platforms concerned with the analyte matrix effect.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".