Quantification of 16 polycyclic aromatic hydrocarbons in cigarette smoke condensate using stable isotope dilution liquid chromatography with atmospheric‐pressure photoionization tandem mass spectrometry
Bibliographic record
Abstract
A stable isotope dilution liquid chromatography tandem mass spectrometry method for the analysis of 16 polycyclic aromatic hydrocarbons in cigarette smoke condensate was developed and validated. Compared with previously reported methods, this method has lower limits of detection (0.04-1.35 ng/cig). Additionally, the proposed method saves time, reduces the number of separation steps, and reduces the quantity of solvent needed. The new method was applied to evaluate polycyclic aromatic hydrocarbon content in 213 commercially available cigarettes in China, under the International Standardization Organization smoking regime and the Health Canadian intense smoking regime. The results showed that the total polycyclic aromatic hydrocarbon content was more than two times higher in samples from the Health Canadian intense smoking regime than in samples from the International Standardization Organization smoking regime (1189.23 versus 2859.50 ng/cig, p < 0.05). Meanwhile, the concentration of individual polycyclic aromatic hydrocarbons (and total polycyclic aromatic hydrocarbons) increased with labeled tar content in both of the tested smoking regimes. There was a positive correlation between total polycyclic aromatic hydrocarbons under the International Standardization Organization smoking regime with that under the Health Canadian intense smoking regime. The proposed liquid chromatography tandem mass spectrometry method is satisfactory for the rapid, sensitive, and accurate quantitative evaluation of polycyclic aromatic hydrocarbon content in cigarette smoke condensate, and it can be applied to assess potential health risks from smoking.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".