Phenolic Compounds and Antioxidant Activities of Skins and Seeds of Foreign and Iranian Grapes
Bibliographic record
Abstract
Grape skins and seeds are sources of phenolic compounds that contribute to the sensory characteristics and beneficial bioactivity of wines and other processed foods. Grape seed and skin extracts from foreign, wild and Iranian cultivars were assayed for their antioxidant properties and phenolic compositions. Finally, the results were compared with those of Vitis vinifera cv. Muscat of Alexandria and V.labrusca. Among the skins of grape cultivars analyzed, those of Lalsiyah contained the highest amount of total phenolics (1067.5 mg 100g-1 gallic acid equivalent of fresh weight) and antiradical activities (0.79 m mol g-1 trolox equivalent of fresh weight). In contrast, Dedeskiramfi contained highest amount of seed total phenolics (2277.3 mg 100 g-1 GAE of fresh weight). The phenolic content of different grapes depends mainly on the grape skin color. The total phenolic content of W8 and W11 with white skins was significantly different from grapes with dark skins. Lalsiyah skin contained the highest amount of total flavonoid, total anthocyanins content, total procyanidin monomers and antiradical activity. Since, total phenolic content is an index of potent antioxidant capability; Lalsiyah will be good resource of antioxidant in food and pharmaceutical industries.
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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) | 0.000 | 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".