Sequential Extraction of Bioactive Saponins from Cucumaria frondosa Viscera: Supercritical CO2–Ethanol Synergy for Enhanced Yields and Antioxidant Performance
Bibliographic record
Abstract
This study investigates the sequential extraction of lipids and saponins from C. frondosa viscera. Lipids were extracted using supercritical carbon dioxide (scCO2) in the presence of ethanol (EtOH) as a co-solvent. Subsequently, the lipid-extracted viscera underwent three saponin extraction approaches, scCO2-scCO2, scCO2-EtOH, and scCO2-hot water, resulting in saponin-rich extracts. Process parameter investigation for saponin extraction from scCO2-defatted viscera revealed minimal effects of temperature, pressure, extraction time, static extraction, and EtOH concentration on saponin yields, allowing for milder operational conditions (35 °C, 20 MPa, 30 min dynamic extraction, 75% EtOH at 0.5 mL/min) to achieve energy-efficient recovery. Continuous EtOH feeding predominates the scCO2 extraction of saponins. The sequential scCO2 extraction of lipid and saponins yielded saponins at 9.13 mg OAE/g, while scCO2 extraction of lipid followed by a 24 h 70% EtOH extraction of saponins achieved 16.26 mg OAE/g, closely matching the optimized ultrasonic-assisted extraction of saponins (17.31 mg OAE/g) from hexane-defatted samples. Antioxidant activities of saponin-rich extracts obtained in the sequential scCO2-EtOH extraction (17.12 ± 4.20% DPPH scavenging) and the sequential scCO2-scCO2 extraction (16.14 ± 1.98%) were comparable to BHT (20.39 ± 0.68%), surpassing that of hexane-defatted ultrasonic extracts (8.11 ± 1.16%). The optimized scCO2-EtOH method offers a sustainable alternative, eliminating toxic solvents while maintaining high saponin yields and bioactivity.
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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 | 0.000 |
| 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".