Comparing various techniques for maximising the yield of bioactive compounds and antioxidants from Chaetomorpha linum (O.F. Müller) Kützing (1845)
Bibliographic record
Abstract
Seaweeds are a rich source of bioactive compounds and antioxidants. However, the extraction of these compounds from Australian seaweed species including Chaetomorpha linum remain unknown to date. No study has reported the impact of different techniques on the yield of bioactive compounds. The current study aimed to compare the yield of bioactive compounds using four extraction techniques, such as microwave-assisted extraction (MAE), ultrasound-assisted extraction (UAE), enzyme-assisted extraction (EAE), and conventional solvent extraction (CSE), for determining total polysaccharide content (TPS), total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activity of C. linum extracts. Antioxidant activity was determined using DPPH, ABTS, and ORAC assays. Result revealed that EAE yielded the highest overall extraction yield (28.9%), followed by UAE (26.1%), MAE (23.5%), and CSE (17.5%). EAE also resulted in a significantly (p < 0.05) higher total polysaccharide content than the other techniques, whereas no significant difference was observed in TPS between UAE and MAE. Further, both UAE and MAE extracts recorded significantly higher total phenolic content and total flavonoid content than other extraction methods. UAE and MAE exhibited significantly higher DPPH scavenging activity and ORAC values. The ABTS radical scavenging activity was significantly higher in the UAE extracts than in other methods. These findings compared the yield of the applied extraction methods in enhancing the recovery of bioactive compounds and antioxidants from C. linum. In conclusion, UAE and MAE were identified as the most suitable methods for maximising the phenolic and flavonoid compounds, whereas EAE was more effective for polysaccharides recovery.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".