Marker Based Standardization of Herbal Sunscreen Formulation by Using Validated High-Performance Thin Layer Chromatography Method
Bibliographic record
Abstract
Plant based products are worldwide used and trusted for the care and cure of the health. Herbal sunscreen formulations are gaining popularity for their effectiveness and are devoid of side effects. The present investigation is an effort made to develop herbal sunscreen cream containing methanolic extracts of different plants and its standardization for the presence of bioactive compounds. The leaves of Cymbopogon citratus (Stapf), fruit peel of Punica granatum (Linn), flowers of Butea monosperma (Lam.) and leaves of Neolamarckia cadamba (Roxb.) were selected for the preparation of sunscreen cream. The standardization of herbal formulations is very important to determine its quality based on the concentration of their active phytoconstituents. The herbal cream F-5 (2 % w/w) imparted its sun-protective and antioxidant property. It shows free radicals scavenging activity due the presence of flavonoids and phenolic compounds. High performance thin layer chromatography method is used to determine the quality and quantity of the sun protective phytoconstituents present in the product. The method was validated according to ICH guidelines for the estimation butrin (BT), isobutrin (IBT), quercetin (QC), apigenin (API), chlorogenic acid (CA) and gallic acid (GA) using the optimized solvent systems. The estimation of bioactive markers was carried out on silica gel precoated thin layer chromatography plates with 60F254 as the stationary phase and Camag TC scanner III for densitometric scanning. The average Rf values for the markers were found to be 0.46 for BT, 0.57 for IBT, 0.50 for QC, 0.57 for API, 0.66 for CA and 0.42 for GA. The developed HPTLC method was linear with correlation coefficient 0.999 for BT, 0.998 for IBT, QC, API and 0.9966 for CA and 0.9989 for GA. Limit of detection (LOD) and limit of quantification (LOQ) were recorded. The developed analytical method for quantitative determination of phytoconstituents was found efficient, simple, accurate, and validated.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".