EVALUATION OF ANTIOXIDANT ACTIVITY OF PHENOLIC COMPOUNDS PRESENT IN LIPPIA SIDOIDES CHAM LEAVES
Bibliographic record
Abstract
Several chemical, pharmacological and clinical studies have enabled phytochemical area developed a many drugs used today, such as aspirin, digitoxin, morphine, quinine and pilocarpine. In addition, this area has enriched and expanded numerous species of plants with medicinal potential, resulting in the isolation of substances with diverse biological activities that serve as prototypes for future drugs. This study aims to explore the potential of the species Lippia sidoides, quantifying the content of phenolic compounds from the methanol extract of the leaves, as well as evaluating the antioxidant activity. For chemical research, we used the spectrophotometric method of Folin-Ciocalteau using gallic acid as standard, which allowed determining a level of 126.89 ± 23.08 mg of gallic acid/g extract for total phenolic compounds by the Folin-Ciocalteau’ methods, a value within the patterns found in the literature. When evaluated the antioxidant activity by methods of inhibiting free radical DPPH (2,2- diphenyl-1-picrylhydrazyl), oxidation of linoleic β-carotene/linoleic acid system and reducing power, the results showed 90% inhibition for the first two methods and a good ability to donate electron to the latter method. This results show the potential that this plant has as a source of antioxidant compounds and the need to further explore the phytochemical studies on this species.
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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".