Optimization of Extraction Conditions and Phytochemical Screening of Root Extract of Synadenium glaucescens Pax
Bibliographic record
Abstract
Optimization of extraction conditions and phytochemical screening of the root bark of Synadenium glaucescens were carried out in a stepwise manner in order to obtain the highest yields and the constituents of the extracts. Sequential extraction using Soxhlet method was performed using dichloromethane, hexane and petroleum ether, respectively, each followed by ethanol. Extraction conditions included: running time of 2 to 6 hours, temperature at 25 oC to 95 oC and particle size ranging from 0.4mm to >3mm diameter. Phytochemical screening was done using derivatisation techniques, gas chromatography-mass spectrometry and high performance liquid chromatography. Extraction with dichloromethane followed by ethanol resulted in a higher yield by 25%, within 4 hrs of extraction, particle size of 1mm, at temperatures of 30 oC for dichloromethane and 75 oC for ethanol. Fatty acid analysis indicated absence of free fatty acids in both Dichloromethane and ethanolic extracts. Silylation and Thin Layer Chromatography indicated the presence of non hindered and hindered functionality and the presence of triterpenoids in the dichloromethane extract. Phytochemical screening of the dichloromethane extracts indicated that it is composed of two main triterpenoids that best matched with Lanosterol (42%) and Cycloartenol (31%). Other minor compounds identified through chromatographic analysis were phytol, ergostadiol, hentriacontane, sitastirol aceate, lupeol and hopenone. The ethanolic extracts indicated the presence of polyphenolic compounds.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".