Effect of Health Information on Consumer Acceptability of Bread Fortified with β‐Glucan and Effect of Fortification on Bread Quality
Bibliographic record
Abstract
ABSTRACT Fortifying bread with β‐glucan reduces bread sensorial properties, though fortification using β‐glucan concentrates of low solubility under the conditions of dough preparation has not been investigated. This study investigated the consumer acceptability and purchase intent of bread fortified with a less soluble β‐glucan concentrate at levels corresponding to 0, 0.75, and 1.5 g β‐glucan/serving bread in relation to the provision of health information, gender, and whole wheat bread consumption. The effect of β‐glucan concentration on the physical properties of the bread produced under pilot plant settings was also investigated. β‐Glucan addition decreased (P < 0.05) loaf volume, increased firmness, and produced a darker, redder bread (P < 0.05), though fortification at 1.5 g β‐glucan/serving bread decreased height as well (P < 0.05). Consumer evaluation (n = 122) revealed that health information increased liking of appearance, flavor, and overall acceptability of the 1.5 g/serving bread to levels similar to or exceeding that of the control. Liking of the 1.5 g β‐glucan/serving bread appearance increased more in women than in men and for consumers who regularly consumed whole wheat bread for perceived health benefits when β‐glucan health information was provided. The provision of β‐glucan health information may be the key to increasing consumer acceptability of bread fortified with β‐glucan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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) | 0.003 | 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".