Characterization and optimization of hydrothermal extraction of quercetin from <scp> <i>Quercus</i> </scp> leaves using response surface methodology
Bibliographic record
Abstract
Abstract A hydrothermal process using subcritical water and ethanol as an extraction solvent has been studied to extract high‐value flavonoid compounds from Quercus leaves. The univariate analysis showed that the maximum quercetin yield was 34.6 mg/g obtained at the liquid–solid ratio of 30:1 ml/g, concentration of ethanol 60% (V/V), extraction temperature of 180°C, and extraction duration of 5 h. Based on the Box–Behnken experiment design, the response surface methodology (RSM) was used to optimize the liquid‐to‐solid ratio, extraction time, extraction temperature, ethanol concentration, and other operating conditions of hydrothermal extraction of quercetin. The regression equation showed that the maximum yield of quercetin was 36.1 mg/g under the conditions of solvent ethanol concentration of 65% (V/V), liquid–solid ratio of 40:1 ml/g, extraction temperature of 189°C, and extraction duration of 5.5 h. The quercetin yield of 36.1 mg/g obtained by the hydrothermal method was 5.6 times the 6.4 mg/g obtained by the traditional ethanol reflux extraction at the same concentration of ethanol 65% (V/V), liquid–solid ratio of 40:1 ml/g, and extraction duration of 5.5 h, but at the boiling point of the solvent.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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".