Simultaneous Analytical Efficiency Evaluation Using an HPTLC Method for the Analysis of Syringic Acid and Vanillic Acid and Their Anti-Oxidant Capacity from Methanol Extract of <i>Ricinus communis</i> L. and <i>Euphorbia hirta</i> L
Bibliographic record
Abstract
BACKGROUND: The well-known medicinal plants Ricinus communis L. and Euphorbia hirta L. have been traditionally used to treat a variety of ailments in the India and other countries. OBJECTIVE: In the present research, a simple, rapid, reliable, and accessible HPTLC technique has been established for concurrent quantification of phenolic compounds (syringic acid and vanillic acid) and evaluation of their anti-oxidant capacity. METHOD: The chromatographic separation was conceded on pre-coated silica gel plates 60 F254 as the stationary phase. The appropriate mobile phase combination of toluene-ethyl acetate-formic acid (7:2.5:0.5, v/v/v) were developed to expand the plates which separated components according to the marker compounds. Further, the anti-oxidant potential of methanol extract of R. communis (MERC) and E. hirta (MERH) were also assessed with 1,1-diphenyl-2-picrylhydrazyl (DPPH) by using UV spectrophotometry. RESULTS: Densitometric scanning was performed with a Camag V scanner and measured at two different wave lengths, 272 and 318 nm. The marker compounds were practically resolved with RF 0.5 ± 0.04 for syringic acid and 0.6 ± 0.06 for vanillic acid. The results obtained in the study of anti-oxidant activity of MERC and MERH showed significant free radical scavenging capacity against DPPH-generated free radicals. CONCLUSIONS: The developed HPTLC method was validated for accuracy, linearity, precision, and specificity. Both the extracts revealed considerable antioxidant activity. The reported existing phenolic and flavonoids compounds are responsible for antioxidant activity of plant extracts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".