Characterization of Brown Seaweed (Ascophyllum nodosum) and Sugar Kelp (Saccharina latissima) Extracts Using Temporal Check-All-That-Apply
Bibliographic record
Abstract
Seaweed is a sustainable ingredient that has been suggested to improve the nutritional aspects as well as the sensory properties of different food products. The objective of this study was to evaluate the flavor properties of extracts from brown seaweed (Ascophyllum nodosum) and sugar kelp (Saccharina latissimi) obtained at different temperatures. These varieties commonly grow in the Atlantic Ocean. The seaweed samples were extracted using water at three different temperatures (50 °C, 70 °C, and 90 °C). The volatile fraction of the extracts was extracted with headspace solid-phase microextraction and analyzed by gas chromatography–mass spectrometry. The headspace chemical composition varies significantly among seaweed extracts and at different extraction temperatures. Major classes of identified compounds were aldehydes, ketones, alcohols, hydrocarbons, and halogenated compounds. Extracts were also evaluated using temporal check-all-that-apply (with 84 untrained participants). The different temperatures had minimal impact on the flavour properties of the brown seaweed samples, but the extraction temperature did influence the properties of the sugar kelp samples. Increasing the extraction temperature seemed to lead to an increase in bitterness, savouriness, and earthy flavor, but future studies are needed to confirm this finding. This study continues the exploration of the flavor properties of seaweeds and identifies the dynamic flavor profile of brown seaweed and sugar kelp under different extraction conditions.
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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) | 0.001 | 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".