Ultrasound-assisted extraction of bioactive compounds from green tea leaves and clarification with natural coagulants (chitosan and Moringa oleífera seeds)
Bibliographic record
Abstract
In this study, a sequential process of extraction and clarification by natural coagulants and centrifugation successfully produced green tea extracts clear and enriched in bioactive compounds. Desirability function methodology was applied to optimize ultrasound-assisted extraction (UAE) of total polyphenols and flavonoids by the evaluation of the parameters temperature (22–83 °C), tea-to-water ratio (12–73 g L−1) and amplitude (23–77%). Green tea clarification was carried out using the extract at UAE optimized conditions (77, 73 g L−1, 77%). The effect of centrifugation and the concentrations of chitosan (25–2000 mg L−1) and Moringa oleífera seeds (MOS) (100–10000 mg L−1) were evaluated. The best clarification conditions were at the concentrations of chitosan 500 mg L−1 and MOS 8000 mg L−1 followed by centrifugation. These optimized clarification conditions provided turbidity, solids and polyphenols reduction of 95% and 96%, 16 and 16%, 18 and 19% for chitosan and MOS treatments, respectively. The main individual compounds identified by high liquid chromatography (HPLC) were epigallocatechin gallate, epicatechin, epigallocatechin and catechin, gallic acid and caffeine.
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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 | 0.000 |
| 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".