Optimization of Extraction Condition and Characterization of Malva Verticillata Root Bark Mucilage
Bibliographic record
Abstract
The study aimed at optimizing the extraction conditions and establishing the physicochemical properties of the mucilage obtained from root bark of Malva verticillata. Effects of temperature, time, particle size and amount of water as a solvent on the yield and composition of mucilage were established. Micrometric, physical, chemical and structural properties were used to characterise the extracted mucilage. Result showed significant correlation between extraction conditions and mucilage yield. Low temperature and short extraction time results into pharmaceutically valuable mucilage with low protein content, high viscosity and good dispersion in aqueous solvent. Large and small size particles of the plant material results into low mucilage yield without significant difference (p=0.054). The mucilage shows mixed cohesiveness with less tendencies to flow. It exhibits vibration bands in FTIR that are associated with the presence of polysaccharides and proteins. The mucilage contains 2.65% protein, 13.83% inorganics, 1.85% fats, and the rest carbohydrates. High extractive value ˃80% and moisture content ˃12% implies that mucilage has high tendency to microbial attack. Optimized extraction conditions results into high yield of the mucilage with the same physicochemical properties and mineral composition regardless of the plant material sampling sites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".