Enhancement of antioxidant properties of Eisenia bicyclis extracts through combination of green extractions: Parameter optimization and comparison
Bibliographic record
Abstract
We compared and optimized combined green extraction (CGE) technologies to maximize the antioxidant activity of Eisenia bicyclis through response surface methodology (RSM) . Firstly, conventional extraction (CE), microwave assisted extraction (MAE), and supercritical fluid extraction by carbon dioxide (SFE-CO 2 ) were applied to extract and compare antioxidant activities of E. bicyclis . The antioxidant activity results indicated that MAE and SFE-CO 2 were superior to CE alone. Based on the optimum conditions for MAE and SFE-CO 2 , ultrasound-assisted extraction (UAE) and high-pressure processing (HPP) were combining to increase antioxidant activity. SFE-CO 2 +UAE was selected as the optimum CGE technology. The DPPH-radical scavenging activity and the total phenolic content of E. bicyclis extracts using SFE-CO 2 +UAE were approximately 2 and 5-fold higher than those of extracts using CE with almost 9-fold higher dieckol content. We demonstrated that E. bicyclis has substantial potential as a natural antioxidant and that CGE is more effective than CE and GE in enhancing antioxidant yields. • Parameters of CGE for E. bicyclis was optimized using response surface methodology. • SFE-CO 2 + UAE was the most effective CGE technology for extraction of E. bicyclis. • CGE achieved 2, 5 and, 9-folds higher DPPH, TPC, and dieckol than these of CE. • CGE was more effective extraction technology for E. bicyclis compared to CE and GE. • E. bicyclis is a potential natural antioxidant as its high antioxidant properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".