Determination of 4(5)-methylimidazole in carbonated beverages by isotope-dilution liquid chromatography-tandem mass spectrometry
Bibliographic record
Abstract
The purpose of this study was to develop a method to quantify 4(5)-methylimidazole (4-MEI), a suspected carcinogen, in carbonated beverages by simple sample dilution and isotope-dilution reverse-phase LC-MS/MS. Isotope dilution using hexa-deuterated methylimidazole (d6-4-MEI) was used to quantify native 4-MEI and to assess matrix effects quantitatively. The accuracy of the method was assessed by intentionally fortifying a negative control sample at three doses: low, medium and high (replicates of n = 5 each) with a known amount of 4-MEI. The respective absolute error in each case was 18.7 ± 0.7%, 14.6 ± 2.8% and 21.1 ± 9.7%. Within-day (intra-) and day-to-day (inter-) repeatability, determined as the relative standard deviation by fortifying a negative control sample (n = 5), were 9.5% and 15.4%, respectively. Average ion suppression of d6-4-MEI in beer was 63.9 ± 3.2%, while no suppression or enhancement was seen in non-alcoholic samples. The instrument and method limit of detection were calculated as 0.6 and 5.8 ng ml(-1), respectively. 4(5)-Methylimidazole was quantified in a variety of store-bought consumer beverages and it was found that in many of the samples tested consuming a single can of beer would result in intake levels of 4-MEI that exceed the no significant risk guideline of 29 µg day(-1). Conversely, 4-MEI in the samples was orders of magnitude smaller than the European Food Safety Authority acceptable daily intake threshold value of 100 mg kg(-1) bw day(-1).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".