Consumer perception of salt‐reduced bread with the addition of brown seaweed evaluated under blinded and informed conditions
Bibliographic record
Abstract
BACKGROUND: Many consumers have a high salt intake and bread is a primary source because of its high rate of consumption. The inclusion of seaweeds has been proposed as an ingredient that could help reduce the salt content of food products. As such, the present study aimed to evaluate whether the amount of salt in bread could be reduced and the change in sensory properties be mitigated by the inclusion of brown seaweed. There were two different sensory trials conducted. In the first trial, participants (n = 102) evaluated bread made with brown seaweed (4% substitution for flour) with reduced amounts of salt (10%, 20%, 30%, 40% and 50%). The second trial asked participants (n = 98) to evaluate the control bread and the 20% salt-reduced bread in blinded and informed conditions. In both sensory trials, the breads samples were assessed using hedonic scales, just-about-right scales, and check-all-that-apply. RESULTS: The results showed that the 10% and 20% salt-reduced breads were acceptable and associated with being soft, chewy and having no aftertaste. The other breads were associated with a dense, dry and strong aftertaste, along with not being salty enough for the consumers. When the breads were evaluated in informed conditions, the salt reduction label had a negative impact on the consumers' liking. CONCLUSION: The research emphasizes that salt-reduced labels influence consumers' sensory perception. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".