Application of accelerated solvent extraction coupled with online two‐dimensional countercurrent chromatography for continuous extraction and separation of bioactive compounds from <i>Citrus limon</i> peel
Bibliographic record
Abstract
Drug discovery from complex mixtures, like Chinese herbs, is challenging and extensive false positives make it difficult to obtain compounds with anti-Alzheimer's activity. In this study, a continuous method comprised of accelerated solvent extraction coupled with online two-dimensional countercurrent chromatography was developed for the efficient, scaled-up extraction and separation of six bioactive compounds from Citrus limon peels: neoeriocitrin, isonaringin, naringin, hesperidin, neohesperidin, and limonin. These active compounds were isolated and purified from the raw plant materials by two-dimensional countercurrent chromatography separation via two sets of an n-hexane/n-butanol/methanol/water solvent system: 0.23:1.00:0.25:1.13 and 0.47:1.00:0.38:1.46, v/v/v/v. The compounds were collected in yields of 0.22, 0.25, 0.10, 0.31, 0.29, and 0.28 mg/g, respectively, with purities of 95.79, 96.47, 97.69, 97.22, 98.11, and 98.82%, respectively. Subsequently, a simple and efficient in vitro method was developed for rapidly evaluating the acetylcholinesterase inhibitory activities of six bioactive components. Furthermore, the PC12 cell model and the in vitro metabolism of cytochromes P450 were employed to verify the monomers obtained from the continuous method. The results demonstrated that these six bioactive extracts from the C. limon peels were strong acetylcholinesterase inhibitors.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".