Modeling Yeast in Suspension during Laboratory and Commercial Fermentations to Detect Aberrant Fermentation Processes
Bibliographic record
Abstract
Understanding yeast dynamics during fermentation is important for quality control, whether monitoring fermentation consistency or identifying aberrant events, such as premature yeast flocculation (PYF). Previous models of fermentation dynamics tend to be parameter rich and require large time series, which are rare in industry. This research investigates five simpler models to 1) describe fermentation dynamics, 2) refine quality control sampling regimes to improve model fit, and 3) identify PYF fermentations. The ability of these models to describe yeast dynamics was evaluated using model fitting with time series data and Akaike Information Criterion (AIC) model selection. Data simulated from large time series was used with this model fitting approach to improve sampling schedules without increasing sampling effort. Lastly, PYF was identified in fermentations of fungal-contaminated malt using linear discriminant analysis (LDA). For large data sets, a four-parameter extension of the normal curve performed best while smaller data sets were better described by the 2-parameter gamma model. Moving sampling effort nearer the population peak improved model fits. Lastly, all models detected PYF, however the two-parameter gamma model provided a simple metric for distinguishing PYF. This research provides guidelines on appropriate model use, improving sampling regimes, and identifying PYF.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".