Diacetyl : identification and characterisation of molecular mechanisms for reduction in yeast and their application in a novel enzyme based assay for quantification in fermentation systems
Bibliographic record
Abstract
Diacetyl (2,3-butanedione) is an important flavour active, oxidative compound that has significant impact on cellular health as well as financial impact in industrial fermentations. The presence of diacetyl in certain fermented beverages, such as beer, results in an unpleasant butterscotch-like flavour and its concentration needs to be reduced by yeast to below the taste threshold prior to filtration and packaging. This results in significant process inefficiency. Furthermore, diacetyl negatively impacts cellular health and has been associated with neurodegenerative diseases and general cell aging amongst others. The reduction of this compound is therefore essential for cellular health. Several yeast cell enzymatic mechanisms responsible for diacetyl reduction were identified and characterised, including Old Yellow Enzyme (OYE) isoforms and D-Arabinose Dehydrogenase (ARA1). OYE isoforms displayed different micromolar affinities and catalytic turnover rates for diacetyl and catalysed diacetyl reduction in a biphasic manner. ARA1 catalysed diacetyl reduction in a monophasic manner with a millimolar Michaelis constant. Knowledge gained in these studies was applied in investigations of diacetyl production and reduction in industrial brewing operations and the enzymatic systems further exploited for the development of a novel enzyme based assay to determine diacetyl concentrations in beer samples. Concentrations as low as 0.2 muM were detectable with high repeatability.
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How this classification was reachedexpand
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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".