The Impact of Lactic and Acetic Acid on Primary Beer Fermentation Performance and Secondary Re-Fermentation during Bottle-Conditioning with Active Dry Yeast
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The presence of high concentrations of organic acids is known to adversely affect the efficiency and quality of ethanol fermentation. The growing popularity of sour beers warranted the exploration of strain-specific performance under optimal and suboptimal conditions similar to those found in sour beer production. The focus of this study was on the performance of select active dried yeast strains under artificially acidified conditions. Nine common brewing strains of active dried yeast were assessed based upon overall fermentation performance and their ability to metabolize maltotriose and maltose between 0.0% w/w − 1.0% w/w lactic acid and 0.0% w/w − 0.5% w/w acetic acid. A single strain of active dried yeast specifically selected and bred for bottle conditioning environments was assessed based upon its ability to metabolize glucose, and carbonate artificially acidified finished beer between 0.0%−1.6% w/w lactic acid and 0.0%−1.0% w/w acetic acid. This study confirmed the suitability of active dry brewing yeast for sour beer fermentations that meet or exceed the typical organic acid concentrations encountered in sour wort. The majority of the selected strains performed well in sour wort containing < 0.4% w/w lactic acid or < 0.1% w/w acetic acid. The importance of strain selection became apparent at concentrations exceeding these reported values, with two strains displaying almost no change in fermentation capabilities across the range of organic acid concentrations. Bottle conditioning remained unhindered by lactic acid up to 1.6% w/w, while acetic acid concentrations at and above 0.4% w/w significantly hindered bottle conditioning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it