Network Formation by Pilot Plant and Laboratory‐Extracted Barley β‐Glucan and Its Rheological Properties in Aqueous Solutions
Bibliographic record
Abstract
ABSTRACT Barley and oat β‐glucans of low viscosity form reversible gels when prepared in sufficiently high concentrations. Solutions of three barley β‐glucan gums differing in molecular weight and thus in viscosity were prepared at 1.0, 2.5, or 5.0% (w/w) concentration levels. Medium‐ and high‐viscosity gums were prepared in a pilot plant (PP) and laboratory (LAB), respectively. Low‐viscosity (LV) gum was extracted in the laboratory at pH 7, which allowed for native enzymatic activity and decreased molecular weight. Network formation was monitored overnight through changes in storage (G′) and loss (G″) moduli. The strength of the formed network was determined from oscillatory rheological measurements by increasing the strain from 2 to 100%. Findings demonstrate that gelation of β‐glucan is molecular weight dependent and practically an instantaneous process for low‐viscosity gum solutions at concentrations of ≤5% gum (or ≤4% β‐glucan), levels lower than previously anticipated. The purity of β‐glucan also seems to affect gelation rate. Better understanding of the β‐glucan gelation behavior is important for its functionality in both food product applications and physiological mechanisms of its health benefits.
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".