Microbrewidics: A Microfluidic Platform to Investigate What Stabilizes Hop Oil Emulsions in Beer
Bibliographic record
Abstract
Hop oils form microscopic emulsions in aqueous beer, but little is known about which molecules in beer stabilize these emulsions. Here we use a microfluidic platform as a tool to enable the creation of assays to explore the role of proteins in the stabilization of hop oil emulsions in beer. The terpenes linalool and α-pinene were used to form emulsions with a Kölsch-style ale on a microfluidic device (oil-in-beer emulsions). Gluten was added to these emulsions on-chip to investigate how this protein, which is present in beer, affects the stability of the emulsions. Then Brewers Clarex, an enzyme commonly used in brewing to degrade proteins, was added to digest the oil-in-beer emulsions. Our data suggest that the type and amount of proteins present in beer may affect the stability of the hop oil emulsions, which could have an impact on the shelf life and sensory quality of the beer.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".