Analysis of Commercially Available Active Dry Yeast Used for Industrial Fuel Ethanol Production
Bibliographic record
Abstract
Seven industrial active dry yeasts (ADY) that are sold specifically for fuel alcohol production, but which may have brewing, winery, or bakery lineage (due to unavailability of specially selected strains for fuel alcohol use), were obtained from different suppliers. All were subjected to traditional screening methods to assess viability, contaminant levels, fermentation rates, generation times, optimal fermentation temperature, and fermentative performance in normal and very-high-gravity corn mashes. They were also compared by using two molecular techniques, karyotyping and mitochondrial DNA analysis. It was observed that of the seven samples, one had a significantly lower viability compared with the expected industrial norm of approximately 2.2 × 1010 cells/g. One sample had high levels of contaminants (0.1% anaerobic bacteria), suggesting that this ADY should not be used because of the risk of bacterial contamination and subsequent loss of ethanol yield during fermentation. More importantly, of the seven ADY, molecular examination revealed only four distinct karyotypic patterns. Mitochondrial DNA analysis confirmed the karyotypic results, but only three distinct patterns could be seen. These results demonstrated that of the seven ADY, only three unique yeast profiles were present. These results strongly suggest that many of the industrially available ADY are similar and indicate that yeasts may have been purchased by contract for resale or that some companies may have procured, grown, and sold the yeasts of their competitors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.000 |
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".