Impact of Bourbon Continuous Column Operations on Ethyl Carbamate Levels
Bibliographic record
Abstract
European and Canadian regulators enforce strict limits on the ethyl carbamate content of alcoholic beverages. North American distillers’ malt have high glycosidic nitrile levels which amplify the ethyl carbamate level of whiskey. Guidelines are required to produce low ethyl carbamate whiskey from North American distillers’ malts. This work challenges several strategies for reducing ethyl carbamate levels in bourbon and American whiskey products. New make spirit was produced using a bourbon mash bill on a continuous beer still with doubler. The low wines alcohol content and beer feed tray were systematically varied while the caustic cleaning schedules were monitored to generate 113 unique high wines distillate samples. Ethyl carbamate levels in each sample were determined by gas chromatography-mass spectrometry. Ethyl carbamate levels are reduced by feeding beer into a lower tray on the distillation column. Ethyl carbamate levels are reduced in distillates collected shortly after caustic cleaning over those collected longer after a caustic cleaning. There was no significant effect (p > 0.05) on ethyl carbamate levels as the low wines alcohol content was changed.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".