Ethyl Carbamate Analysis in German Fruit Spirits and Brazilian Sugarcane Spirits (Cachaça): Improved Sample Cleanup with Automated Parallel Evaporation
Bibliographic record
Abstract
The analysis of alcoholic beverages for the important carcinogenic contaminant ethyl carbamate is very time-consuming and expensive. Due to possible matrix interferences, sample cleanup using diatomaceous earth (Extrelut) column is required prior to gas chromatographic and mass spectrometric measurement. A limiting step in this process is the rotary evaporation of the eluate containing the analyte in organic solvents, which is currently conducted manually and requires approximately 20-30 min per sample. This paper introduces the use of a parallel evaporation device for ethyl carbamate analysis, which allows for the simultaneous evaporation of 12 samples to a specified residual volume without manual intervention. A more efficient and, less expensive analysis is therefore possible. The method validation showed no differences between the fully-automated parallel evaporation and the manual operation. The applicability was proven by analyzing authentic spirit samples from Germany, Canada and Brazil. It is interesting to note that Brazilian cachacas had a relatively high incidence for ethyl carbamate contamination (55% of all samples were above 0.15 mg/l), which may be of public health relevance and requires further evaluation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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 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".