ANTIOXIDANT ACTIVITY OF ENGLISH WALNUT (<i>JUGLANS REGIA</i> L.)
Bibliographic record
Abstract
ABSTRACT Phenolic contents of different fractions (contents of whole nut, skin and kernel) of English walnut (Juglans regia L.) were determined and their antioxidative capacities investigated using a number of in vitro model systems. Phenolic compounds extracted from walnut skin into 95% ethanol contained the highest amount of total phenolics and exhibited the highest antioxidative capacity as evaluated by the trolox equivalent antioxidant capacity assay. Extracts of walnut phenolics showed a high ferrous ion chelating ability and effectively scavenged 2,2′‐diphenyl‐1‐picrylhydrazyl radicals, the latter considerably stronger for the skin with 50% inhibition concentration of 3.4 µg extract per mL. Further, inhibition of 2‐thiobarbituric acid reactive substances formation in a bulk corn oil model system was also significant (P < 0.05) for all three phenolic extracts and for pure gallic acid at 10 ppm gallic acid equivalents final assay concentration after four days of storage at 60C. PRACTICAL APPLICATIONS Walnut is a healthful nut that contains alpha‐linolenic acid in its lipid fraction, and its skin is rich in polyphenolics with strong antioxidant properties, as demonstrated in this study. Thus, walnut with skin and skin of walnuts serve as good free radical scavengers and could be effective in reducing oxidative stress among other beneficial health effects, which could be exploited for product development.
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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".