Atlantic Canadians’ Sensory Perception of Couscous Made with Sugar Kelp (Saccharina latissma)
Bibliographic record
Abstract
has many nutritional benefits and has been identified as a rich source of fibre, vitamins, and antioxidants. However, it is not regularly consumed in the Western world, and the sensory perception of foods containing sugar kelp must be investigated to increase acceptance in North America. This study evaluated consumers' (n = 99) sensory perception of couscous with increasing amounts of sugar kelp (0% (control), 4%, 6%, 8%, and 10% wt/wt). Furthermore, consumers' purchase intent, liking, and emotional response to couscous with added sugar kelp was evaluated with and without nutritional information. Sugar kelp at 6% incorporation did not impact the consumers' liking scores ("Like Slightly" on the hedonic scale), but at 8% the consumers' liking significantly decreased ("Neither Like nor Dislike"). The 8% and 10% levels of sugar kelp addition led to astringency, bitter, hard, brackish, fishy, and chewy attributes being perceived by the consumers. The consumers identified they preferred samples that had soft, savoury, salty, and bland flavours and disliked samples that were brackish and gritty. The nutritional information did not increase overall liking scores, purchase intent, or emotional response. However, the inclusion of sugar kelp in the couscous did lead to an increased selection of positive emotions like happy, joyful, pleasant, and enthusiastic. Overall, the consumers were interested in foods containing seaweed and believed they were nutritious. The results indicated that sugar kelp could be added to couscous up to 6% wt/wt without impacting overall liking.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".