Bioextracting Polyphenols from the Brown Seaweed <i>Ascophyllum nodosum</i> from Québec's North Shore Coastline
Bibliographic record
Abstract
Developing innovative industries in rural communities requires researching valuable finished products using local natural resources and feasible equipment and technology. Resources like seaweed are popular in today's global cosmetic ingredient and biotechnology market and are commonly found growing in remote communities, making it an ideal opportunity for rural economic development. This study focuses on the antioxidant-rich polyphenol compounds found in the seaweed species Ascophyllum nodosum, local to Québec's North Shore coastline. Different processing technologies were compared to optimize polyphenol yields, including different preservation methods as well as bioextraction techniques that are applicable and accessible to remote regions. Analyses of extracts were performed using different colorimetric assays to measure total polyphenols and phlorotannins, as well as to estimate antioxidant activity. Results from the study found that the samples immediately frozen displayed higher polyphenol concentration and the highest antioxidant activity. Analysis also showed that a microwave-assisted extraction method improved polyphenol yield efficiency for water extractions. However, the conventional solvent extraction method using 75% (v/v aq.) 1,3-propanediol solvent resulted in the highest phenolic content, totalling 9.8% (w/w) of its dry weight, and the optimal antioxidant activity.
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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 | 0.001 | 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) | 0.001 | 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".