Optimization of ultrasound‐assisted extraction of bioactive compounds from <scp><i>B. forficata</i></scp> subsp. <scp><i>Pruinosa</i></scp>
Bibliographic record
Abstract
Abstract Extracts containing bioactive compounds were obtained from Bauhinia forficata leaves by ultrasound‐assisted extraction (UAE) with three different solvents (n‐hexane, ethyl acetate, and ethanol) and were compared with those obtained by a conventional method (maceration). Box‐Behnken experimental design was applied to examine and optimize the effect of the extraction temperature (40°C‐60°C), power (20%‐80%), and sample to solvent ratio (1:10 to 1:20 (w/v)) on the total phenolic content (TPC), total flavonoid content (TFC), and the ferric reducing antioxidant power (FRAP) of B. forficata leaf extracts. This experimental design generated second‐order polynomial models, which accurately describe the experimental data, allowing the prediction of optimal conditions for the investigated responses. Optimal extraction was achieved under the following conditions: 80% power, temperature of 41°C, and a 1:20 sample to solvent ratio. Under these conditions, the experimental yield was 8.33 ± 0.32%, total phenolic content was 59.47 ± 0.71 mg GAE · gextract−1, total flavonoid content was 62.30 ± 3.38 mg QE · gextract−1, and the ferric reducing antioxidant power was 726.7 ± 15.7 μmol Fe(II)EQ · gextract−1, which were close to the predicted values, which validated the models. The major compounds found in B. forficata extracts were tocopherols, phytol, heneicosane, and β‐Sitosterol.
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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 | 0.000 | 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 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".