Viscosity and Solubility of β‐Glucan Extracted Under In Vitro Conditions from Barley β‐Glucan‐Fortified Bread and Evaluation of Loaf Characteristics
Bibliographic record
Abstract
ABSTRACT Fortifying bread with β‐glucan has been shown to reduce bread quality and the associated health benefits of barley β‐glucan. Fortification of bread using β‐glucan concentrates that are less soluble during bread preparation steps has not been investigated. The effects of β‐glucan concentration and gluten addition on the physicochemical properties of bread and β‐glucan solubility and viscosity were investigated using a less soluble β‐glucan concentrate, as were the effects of baking temperature and prior β‐glucan solubilization. Fortification of bread with β‐glucan decreased loaf volume and height (P ≤ 0.05) and increased firmness (P ≤ 0.05). Gluten addition to bread at the highest β‐glucan level increased height and volume (P ≤ 0.05) to values exceeding those for the control and decreased firmness (P ≤ 0.05). β‐Glucan addition increased (P ≤ 0.05) extract viscosity, as did gluten addition to the bread with the highest β‐glucan level. Baking at low temperature decreased (P ≤ 0.05) β‐glucan viscosity and solubility, as did solubilizing it prior to dough formulation. Utilization of β‐glucan that is less soluble during bread preparation may hold the key to effectively fortifying bread with β‐glucan without compromising its health benefits, although more research is required.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".