Optimization of Soxhlet Extraction of Herba Leonuri Using Factorial Design of Experiment
Bibliographic record
Abstract
Soxhlet extraction technique is employed for the extraction and separation of chemical constituents in the medicinalplant, Herba Leonuri. The main goal of this analytical study was focused on extracted compounds and extractionconditions themselves. Soxhlet extractions were performed at three extraction time (6h, 9h and 12h) and with twosolvents (n-hexane and methanol). A general full factorial design with two factors (extraction time and types ofextractor solvents) was implemented. The Soxhlet extraction method presented a good yield of components in extract.The study shows that methanol extracted almost double yield than n-hexane. The highest yield obtained with methanolwas 14.18%; while the highest yield obtained by n-hexane was 7.25%.The results also indicated that, for methanolextraction, the mass yield percent extracted increased with increasing length of extraction period (up to 14.18%); forn-hexane extraction, the mass yield percent extracted was not consistent with increasing length of extraction period. Theextracted oil extracted was analyzed by GC-MS. The compounds identified were vitamin E, palmitic acid and syringol.General characteristics of the Herba leonuri oils obtained by different conditions were further compared, showing thatthe composition of the Herba leonuri oil extracted by different conditions is mostly similar, whereas relative concentration of the identified compounds is apparently different. This study can be considered as the first informationon the chemical compound of Herba leonuri.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".