Anion Exchange and Nebulizer-Enhanced Online UV-Chemiluminescence Analyzer Coupled with LC-MS for Total and Novel N-Nitrosamines in Cigarettes and e-Juices
Bibliographic record
Abstract
Quantification of total N-nitrosamines (TONO) is critical for assessing environmental carcinogens. UV-chemiluminescence (UV-CL) shows promise for TONO analysis; however, the data accuracy is limited by matrix interference and the analyte-specific method sensitivity. To overcome these challenges, we designed a new online UV-CL analyzer integrating anion exchange cartridges (AEC) and a homemade nebulizer. The AEC eliminates interference from a wide concentration range of NO 2 – . The nebulizer effectively introduces both volatile and less-volatile nitrosamines, achieving 98.4% photolysis while requiring only 14–53% of the UV dosage used in previous studies. Our analyzer achieved a universal calibration across structurally diverse N-nitrosamines, simplifying quantitative N-nitrosamine measurements. We further demonstrated its application by measuring TONO in authentic tobacco products. Compared to liquid chromatography mass spectrometry (LC-MS), the analyzer obtained consistent kinetic results when monitoring the reaction between NO 2 – and nornicotine, supporting its application in kinetic studies. Moreover, our preliminary analysis of flavored e-juices shows that they could contain up to 0.24 μmol/g of TONO, which could increase further when an environmental level of NO 2 – (6.7 mg/L) is present. Comparing results obtained using the analyzer and LC-MS, known tobacco-specific nitrosamines (TSNAs) account for less than 5.5% of TONO, indicating that most N-nitrosamines in tobacco are unknown. This led to the tentative identification of two new N-nitrosamines: pyridine-methylated N-nitrosoanatabine and pyridine-methylated N-nitrosoanabasine using high-resolution HPLC-MS/MS. Our results supported the feasibility of using the UV-CL analyzer for rapid TONO measurements and highlighted the need to monitor N-nitrosamine contamination in e-juice and tobacco products.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".