An Examination of Substrate and Product Kinetics during Brewing Fermentations
Bibliographic record
Abstract
Brewers monitor density attenuation during fermentation to detect process deviations and to approximate other fermentation parameters. The most common relationship used to monitor fermentations is Balling's formula. It has been argued by many researchers that this relationship no longer adequately describes fermentation within modern breweries. This study monitored conversion of sugars into fermentation products in an attempt to assess the accuracy of historical and current theories. Experiments were conducted at laboratory scale using a standard assay with both sugar consumption and major product generation assessed at a high sampling frequency. The results were used to assess the accuracy of product formation models (such as Balling's) and to examine how product and substrate ratios changed over the fermentation. It was found that the majority of yeast and glycerol formation occurred within the first half of fermentation. Additionally, yeast cells held in suspension were fairly consistent in sugar consumption rate throughout the fermentation. Finally, it was found that while the consumption of each brewing sugar was highly ordered, there was a great deal of overlap between sugar utilization during the fermentation. This information will hopefully allow brewers to make informed decisions and to enable the use of accurate estimations of alcohol, yeast, and sugar contents for their final product.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".