Phenolic contents, <i>in vitro</i> antioxidant activities and biological properties, and HPLC profiles of free and conjugated phenolics extracted from onion, pomegranate, grape, and apple
Bibliographic record
Abstract
This study quantified the free and conjugated phenolic content of extracts from selected Liliaceae (white and red onion), Punicaceae (red and pink pomegranate), Vitaceae (zini, red globe, and baladi grape), and Rosaceae (yellow and red apple) cultivated family plants and evaluated their antioxidant activities and beneficial properties toward diabetes and hypertension. Free and bound phenolics were extracted from plants, and total phenolic contents were evaluated. Antioxidant activities were assessed, as well as the inhibitory effects of angiotensin I-converting enzyme (ACE) and α-amylase and α-glucosidase activities. Liquid chromatography–mass spectrometry/mass spectrometry (LC–MS/MS) measured the individual phenolic content in both free and bound extracts. The free phenolic contents in the selected plants were higher than bound phenolic contents, except for in zini and red globe grapes. In onion, the highest antioxidant activity was observed in the bound phenolic extract (49.6–56.9%). In pomegranates, grapes, and apples, the highest antioxidant activities were obtained in free phenolic extracts at 30°C with values of 65.3% to 74.2%, 39.6% to 47.6%, and 49.5% to 55.9%, respectively. ACE was 100% inhibited by the free phenolic extract at 30°C from white onion and by the bound phenolic extract after acid hydrolysis of red onion. The highest α-amylase enzyme inhibitory activity was obtained in free phenolic extracts at 60°C of both red pomegranates and zini grapes. The highest α-glucosidase inhibition activity (99.3–99.6%) was found in free phenolic extracts at 30°C from red and pink pomegranates. Reverse phase HPLC revealed two predominant groups of free and bound phenolics identified in onion, pomegranate, grape, and apple.
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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 | 0.001 | 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".