Effect of Extraction Solvents on Total Phenolic Contents and in vitro Antioxidant Activity of the Leaves of Lippia adoensis var. Koseret Sebsebe
Bibliographic record
Abstract
Lippia.adoensis var.koseret is an endemic herb to Ethiopia and is traditionally used as food flavoring and traditional medicine.This paper reported the total phenolic and flavonoid contents, and in vitro antioxidant activity of various extracts from the dried leaf of this herb.Aqueous: methanol (20:80, v/v) extract contained highest amount of total phenolic (67.61 ± 9.89 mg of gallic acid equivalent/g).Total flavonoid contents were highest in acetone extract (25.24 ± 0.43 mg of quercetin equivalent/g).An increase in the extracted concentration resulted in an increase of antioxidant power for all the extracts.The aqueous: methanol (20:80, v/v) extract showed highest DPPH radical scavenging (IC50 = 10.96 ± 0.42 g/ml) iron reducing power (IC50 = 123.97± 3.23 g/ml), total antioxidant activity (105.32 ± 10.67 mg ascorbic acid equivalent/g), and iron chelating activity (IC50 = 81.31± 15.94 g/ml) than other four solvents used.Total phenolics well correlated with DPPH (R 2 = 0.88, p < 0.05) and Ferric reducing power (R 2 = 0.77 p < 0.05).Whereas, total flavonoid content well correlated with total antioxidant (R 2 = 0.73 p < 0.05).The study showed the antioxidants activities of the crude extract were variable when extracted by different solvents indicating a high potential to be used as natural antioxidants in preventing various oxidative stresses and as food preservatives.
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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".