Reducing Beta‐Glucan Solubility in Oat Bran Muffins by Freeze‐Thaw Treatment Attenuates Its Hypoglycemic Effect
Bibliographic record
Abstract
ABSTRACT The viscosity of soluble fibers such as β‐glucan depends on their concentration in solution and molecular weight (MW) distribution. We investigated whether freezing treatment of oat bran muffins affected the physicochemical properties of β‐glucan, and its physiological effectiveness in lowering postprandial blood glucose response. A controlled range of β‐glucan solubility was achieved by subjecting oat bran muffins containing two levels of β‐glucan to repeated freeze‐thaw temperature cycling. β‐Glucan solubilized by in vitro digestion extraction was measured by flow‐injection analysis. MW distributions of β‐glucan were analyzed using size‐exclusion chromatography. β‐Glucan solubility decreased as the number of freeze‐thaw cycles increased, while MW distribution of β‐glucan decreased slightly. Peak blood glucose rise (PBGR) after fresh muffins (8 and 12 g of β‐glucan/serving) was significantly lower than that after muffins (8 and 12 g of β‐glucan/serving) treated with four freeze‐thaw (FT) cycles (1.84 ± 0.2 vs. 2.31 ± 0.1 mmol/L, P = 0.007). Compared with the control whole wheat muffins, the reduction in incremental area under the glucose response curve (AUC) after fresh muffins (8 and 12 g of β‐glucan/serving) was nearly twice that after 4 FT cycles (43.3 ± 4.4% vs. 27.0 ± 5.4%, P = 0.016). A significant inverse linear relationship was found between the log [concentration] of extractable β‐glucan and PBGR (r2 = 0.85, P = 0.01), and AUC (r2 = 0.71, P = 0.03). The results show that reduction of β‐glucan solubility in foods attenuates its physiological effectiveness in lowering postprandial glycemia.
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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".