Effects of β-Glucans and Environmental Factors on the Viscosities of Wort and Beer
Bibliographic record
Abstract
This paper reports on the influence of molecular weight and concentration of barley β-glucans on the rheological properties of wort and beer. Environmental conditions such as pH, maltose level in wort, ethanol content of beer, shearing and shearing temperature were also examined for their effects on wort and beer viscosities. In the range of 50–1000 mg/L, β-glucans increased solution viscosity linearly with both molecular weights (MW) of 31, 137, 250, 327, and 443 kDa and concentration. The influence of MW on the intrinsic viscosity of β-glucans followed the Mark-Houwink relationship. Shearing wort and beer at approximately 13,000 s−1for 35 s was found to increase the wort viscosity but reduce beer viscosity. Shearing wort at 20°C influenced β-glucan viscosity more than shearing at 48°C and 76°C whereas the shearing temperature (0, 5 and 10°C) did not effect the viscosity of beer. At lower pHs, shearing was found to reduce the viscosity caused by β-glucans in wort but had no effect in beer. Higher concentrations of maltose in wort and ethanol in beer also increased the viscosity of β-glucan polymers. It was found that β-glucans had higher intrinsic viscosities in beer than in wort (5°C), and lower critical overlap concentrations (C*) in beer than in wort.
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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 | 0.001 |
| 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 | 0.001 | 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".