Rheological characterisation of polysaccharides extracted from brown seaweeds
Bibliographic record
Abstract
Abstract Hydrocolloids from seaweeds have interesting functional properties, such as thickening or gelling ability. Structural characteristics of polysaccharides extracted from Québec seaweeds have not yet been established. Thus, the determination of the relationship between their structure and rheological behaviour is limited. Alginate and fucoidan were extracted using selective solvents from three species: Saccharina longicruris, Ascophyllum nodosum and Fucus vesiculosus. Structural analysis (total sugars, uronic acids, sulfates and molecular weight) and rheological characterisation were performed at different polysaccharide concentrations with and without the addition of NaCl. The results showed important variation between species. Fucoidan and alginate exhibited Newtonian behaviour. Fucoidan extracted from F. vesiculosus had the highest viscosity level, which might be explained by the degree of branching of the molecules. For alginate, the one extracted from S. longicruris showed a higher apparent viscosity. This result can partially be explained by the block proportion of alginate. The gelation profile of alginate was also determined for each species. The final storage modulus, G′, was variable for each species. Differences between species were observed for both polysaccharides as a result of structural variation. Copyright © 2007 Society of Chemical Industry
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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 | 0.001 | 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) | 0.001 | 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".