Nicotine, Humectants, and Tobacco-Specific Nitrosamines (TSNAs) in IQOS Heated Tobacco Products (HTPs): A Cross-Country Study
Bibliographic record
Abstract
Heated Tobacco Products (HTPs) purport to reduce exposure to tobacco-related toxicants compared to combustible cigarettes. This cross-sectional study examined the content of nicotine, two humectants (propylene glycol (PG) and vegetable glycerin (VG)), and four tobacco-specific nitrosamines (TSNAs: NNN, NNK, NAT, and NAB) in the tobacco filler of a popular HTP brand (IQOS). Non-menthol and menthol IQOS sticks were purchased from nine countries between 2017 and 2020 and were classified into two versions ("Bold" and "Light") using Philip Morris's flavor descriptors. The average nicotine concentration was 4.7 ± 0.5 mg/stick, and the highest nicotine concentration was found in products from Japan (5.1 ± 0.2 mg/stick). VG was the dominant humectant found in all sticks, with an average concentration of (31.5 ± 2.3 mg/stick). NNN, NNK, and NAT were substantially higher in the "Bold" sticks than the "Light" sticks. Significant differences between countries for TSNAs were also observed: the NAT and NAB contents were the highest in the "Light" products from Canada (192.5 ± 24.1 and 22.9 ± 1.0 ng/stick, respectively); the NNK concentration was the highest in the "Bold" products from Poland (64.8 ± 7.9 ng/stick); and the highest NNN concentrations were observed in the "Bold" products from South Africa (488.9 ± 26.7 ng/stick). As NNN and NNK are known human carcinogens, and as humectants like PG and VG can degrade into toxic carbonyl compounds upon heating, monitoring the concentration of these chemicals in HTPs is important for protecting users' health and ensuring compliance with regulations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".