Effect of β-Glucans and Process Conditions on the Membrane Filtration Performance of Beer
Bibliographic record
Abstract
An extended filtration test with 0.45-μm membranes was employed in this study to investigate the influence of β-glucan polymers, shearing, pH, ethanol content, and storage time on beer filterability. Results indicated that the presence of β-glucans caused lower beer filterability. The maximum amount of beer filtered through a membrane filter (Vmax) and the initial filtration rate (Qinit) decreased with the addition of higher β-glucan molecular weight at higher concentrations. Shearing beer at 0–10°C resulted in lower Vmax and Qinit values. Higher pH values were found to improve beer filterability. Compared with nonalcohol beer samples, beers containing 5 and 10% (v/v) of ethanol showed lower Qinit and higher Vmax values. However, the addition of ethanol at 5 and 10% (v/v) decreased the relative Vmax (%) value of beer samples containing β-glucans compared with β-glucan-free beers. Filtration tests also suggested that a cold storage at 4°C for two weeks did not affect filterability in β-glucan treated beers.
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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 | 0.001 | 0.001 |
| 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".