Alkaline subcritical water extraction of bioactive compounds and antioxidants from beach-cast brown algae (Ascophyllum Nodosum)
Bibliographic record
Abstract
Beach-cast brown algae Ascophyllum nodosum (rockweed) is a seasonal phenomenon where rockweed accumulates on beaches, resulting in environmental impacts. However, rockweed is a valuable source of bioactive compounds and nutrients for use in biomaterials, food, cosmetic, and pharmaceutical industries. This study explores the utilization of alkaline subcritical water extraction in an accelerated solvent extractor (ASE) for extracting valuable compounds from beach-cast rockweed. The bioactive compounds and nutrients (alginate, fucoidan, phenolics, minerals, and vitamins) were first characterized. The yields of major components (alginate, fucoidan, and phenolics) and antioxidant properties of crude extract were further optimized in the ASE system as a function of extraction conditions (temperature, extraction time, and solid mass). The maximum yields of crude extract (86.52 dry wt%), alginate (38.25 dry wt%), and fucoidan (39.38 dry wt%) were achieved at 160 °C, 18 min, and 0.1 g mass loading. A higher temperature of 200 °C increased phenolic yield and antioxidant activities (ABTS and FRAP assays) but led to alginate/fucoidan degradation. The extraction rates for extract, alginate, and fucoidan were modeled using Fick’s and Peleg’s models. Both models fit the experimental data well, but Fick’s model showed a better overall fit. The effective diffusion coefficients for the extract (7.82 × 10−12 m2/s), fucoidan (7.56 × 10−12 m2/s), and alginate (8.55 × 10−12 m2/s) were determined. Thus, the ASE using alkaline subcritical water can effectively be used to identify and quantify key value-added compounds by modifying process conditions while minimizing extraction time (maximizing rate) from beach-cast rockweed.
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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) | 0.000 | 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".