Unfilterable Beer Haze Part II: Identifying Suspect Cell Wall Proteins
Bibliographic record
Abstract
The use of various diagnostic techniques has been previously utilized in the assessment of a commercially available India Pale Ale with cases of sporadically occurring unfilterable haze. The results from Part 1 suggested that β-glucans and proteins were the cause of the unfilterable haze and it was postulated that cell wall mannoproteins may also be a culprit of the unfilterable beer haze. In this follow-up study, proteins from high haze and low haze beer samples were precipitated and assessed using SDS-PAGE. Polyphenol interferences observed on the SDS-PAGE indicated that protein purification and targeted analysis was required. Proteins from high haze and low haze samples were fractionated and qualitatively identified via LC-MS. A library was built from FASTA sequences of targeted yeast proteins to qualitatively analyze the high haze and low haze samples. The protein fractionation was successful at purifying and isolating proteins from high and low haze samples. Two protein peaks were observed in the high haze sample, while one protein peak was observed in the low haze sample. The targeted LC/MS analysis discovered the presence of yeast cell wall mannoproteins and flocculation proteins, particularly Flo1 and Flo9. Understanding the source of these hazes can provide an opportunity for brewers to mitigate against their formation by adjusting brewing and yeast management practices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".