Aktivitas antioksidan ekstrak bunga telang (Clitoria ternatea L.) dan aplikasinya dalam sediaan serum
Bibliographic record
Abstract
The unhealthy lifestyle of Indonesians can increase the amount of free radicals that have an impact on skin health. Free radicals can be prevented by increasing antioxidants derived from telang flower (Clitoria ternatea L.). The purpose of this study was to determine the antioxidant activity and phytochemical screening results of telang flower extract and to determine the effect of adding extract in serum gel preparation. Telang flower extract was obtained through maceration process using 96% ethanol solvent in a ratio of 1:10 for 2x24 hours. Antioxidant activity was tested using DPPH (1,1-diphenyl-2-picrylhydrazil) method. Data analysis was performed using One Way Anova test method followed by Duncan's Multiple Range Test (DMRT) with 95% confidence level. Telang flower extract is positive for flavonoids, saponins, triterpenoids, and tannins with an IC50 value of 53.546 ppm. The IC50 value of serum gel preparations with formulations F0, F1, F2, F3, F4, F5 consecutively amounted to 261.847 ppm, 91.294 ppm, 82.748 ppm, 74.487 ppm, 72.041 ppm, 66.985 ppm. The pH value of the serum gel preparation is in the range of 5.70 - 7.38 with a viscosity value of 581.33 - 1625 mPas and no irritation reaction on the skin. Based on the results of the study, it can be concluded that the higher the concentration of telang flower extract, the higher the antioxidant activity in serum gel preparations.
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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 | 0.000 | 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) | 0.002 | 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".