Tobacco-Specific Nitrosamines in the Tobacco and Mainstream Smoke of U.S. Commercial Cigarettes
Bibliographic record
Abstract
Tobacco-specific nitrosamines (TSNAs) are N -nitroso-derivatives of pyridine-alkaloids (e.g., nicotine) present in tobacco and cigarette smoke. Two TSNAs, N ′-nitrosonornicotine (NNN) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), are included on the Food and Drug Administration’s list of harmful and potentially harmful constituents (HPHCs) in tobacco products and tobacco. The amounts of four TSNAs (NNK, NNN, N -nitrosoanabasine (NAB), and N ′-nitrosoanatabine (NAT)) in the tobacco and mainstream smoke from 50 U.S. commercial cigarette brands were measured from November 15, 2011 to January 4, 2012 using a validated HPLC/MS/MS method. Smoke samples were generated using the International Organization of Standardization (ISO) and Canadian Intense (CI) machine-smoking regimens. NNN and NAT were the most abundant TSNAs in tobacco filler and smoke across all cigarette brands, whereas NNK and NAB were present in lesser amounts. The average ratios for each TSNA in mainstream smoke to filler content is 29% by the CI smoking regimen and 13% for the ISO machine-smoking regimen. The reliability of individual TSNAs to predict total TSNA amounts in the filler and smoke was examined. NNN, NAT, and NAB have a moderate to high correlation ( R 2 = 0.61–0.98, p < 0.0001), and all three TSNAs individually predict total TSNAs with minimal difference between measured and predicted total TSNA amounts (error < 7.4%). NNK has weaker correlation ( R 2 = 0.56–0.82; p < 0.0001) and is a less reliable predictor of total TSNA quantities. Tobacco weight and levels of TSNAs in filler influence TSNA levels in smoke from the CI machine-smoking regimen. In contrast, filter ventilation is a major determinant of levels of TSNAs in smoke by the ISO machine-smoking regimen. Comparative analysis demonstrates substantial variability in TSNA amounts in tobacco filler and mainstream smoke yields under ISO and CI machine-smoking regimens among U.S. commercial cigarette brands.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".