Variation of the Nutritional Composition and Bioactive Potential in Edible Macroalga Saccharina latissima Cultivated from Atlantic Canada Subjected to Different Growth and Processing Conditions
Bibliographic record
Abstract
Macroalgae are a new food source in the Western world. The purpose of this study was to evaluate the impact of harvest months and food processing on cultivated Saccharina latissima (S. latissima) from Quebec. Seaweeds were harvested in May and June 2019 and processed by blanching, steaming, and drying with a frozen control condition. The chemical (lipids, proteins, ash, carbohydrates, fibers) and mineral (I, K, Na, Ca, Mg, Fe) compositions, the potential bioactive compounds (alginates, fucoidans, laminarans, carotenoids, polyphenols) and in vitro antioxidant potential were investigated. The results showed that May specimens were significantly the richest in proteins, ash, I, Fe, and carotenoids, while June macroalgae contained more carbohydrates. The antioxidant potential of water-soluble extracts (Oxygen Radical Absorbance Capacity [ORAC] analysis–625 µg/mL) showed the highest potential in June samples. Interactions between harvested months and processing were demonstrated. The drying process applied in May specimens appeared to preserve more S. latissima quality, whereas blanching and steaming resulted in a leaching of minerals. Losses of carotenoids and polyphenols were observed with heating treatments. Water-soluble extracts of dried May samples showed the highest antioxidant potential (ORAC analysis) compared to other methods. Thus, the drying process used to treat S. latissima harvested in May seems to be the best that should be selected.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".