An Investigation Into Soup With the Addition of Brown Seaweed (<scp><i>Ascophyllum nodosum</i></scp>) and Red Seaweed (<scp><i>Chondrus crispus</i></scp>) Using Nonconsumers of Seaweed
Bibliographic record
Abstract
ABSTRACT Seaweed has been proposed as an ingredient that can increase the umami taste and saltiness of food items. However, seaweed is not regularly consumed in North America. This study aimed to evaluate how nonconsumers of seaweed (n = 103) perceive the sensory properties and acceptance of soup with brown seaweed (Ascophyllum nodosum) and red seaweed (Chondrus crispus) powder added. The samples include a control soup (without seaweed) and soup with 1.5% and 3% brown seaweed, as well as 1.5% and 3% red seaweed by weight. Furthermore, before evaluating the soup, they were asked to identify the flavors and textures they associate with seaweed. The brown and red seaweed increased the umami and saltiness intensity of the soup, but it also increased the bitterness and sourness. The red seaweed also decreased the sweetness, overall liking, and liking of the soup's flavor. The participants associated seaweed with fishy, salty, and umami flavors and undesirable textures (slimy, tough, chewy). Seaweed increased the umami and salty taste of soup when evaluated by nonconsumers, but it also introduced other tastes to the soup. This study also identified nonconsumers’ beliefs about seaweed and should help create novel food products using seaweed.
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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 | 0.001 |
| 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) | 0.002 | 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".