Effect of Formulation and Processing Treatments on Viscosity and Solubility of Extractable Barley β‐Glucan in Bread Dough Evaluated Under In Vitro Conditions
Bibliographic record
Abstract
ABSTRACT β‐Glucan shows great potential for incorporation into bread due to its cholesterol lowering and blood glucose regulating effects, which are related to its viscosity. The effects of β‐glucan concentration, gluten addition, premixing, yeast addition, fermentation time, and inactivation of the flour enzymes on the viscosity of extractable β‐glucan following incorporation into a white bread dough were studied under physiological conditions, as well as, β‐glucan solubility in fermented and unfermented dough. β‐Glucan was extracted using an in vitro protocol designed to approximate human digestion and hot water extraction. The viscosity of extractable β‐glucan was not affected by gluten addition, the presence of yeast, or premixing. Fermentation produced lower (P ≤ 0.05) extract viscosity for the doughs with added β‐glucan, while inactivating the flour enzymes and increasing β‐glucan concentration in the absence of fermentation increased (P ≤ 0.05) viscosity. The physiological solubility of the β‐glucan concentrate (18.1%) and the β‐glucan in the unfermented dough (20.5%) were similar (P > 0.05), while fermentation substantially decreased (P ≤ 0.05) solubility to 8.7%, indicating that the reduction in viscosity due to fermentation may be highly dependent on solubility in addition to β‐glucan degradation. The results emphasize the importance of analyzing β‐glucan fortified foods under physiological conditions to identify the conditions in the dough system that decrease β‐glucan viscosity so that products with maximum functionality can be developed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".