A Sensitive and Quantitative Isotope-Dilution LC-MS/MS Method for Analysis of Hydrazine in Tobacco Smoke
Bibliographic record
Abstract
A new isotope dilution liquid chromatography/tandem mass spectrometric method was developed for the analysis of potential hydrazine present in tobacco smoke. The sample preparation was performed via an optimized derivatization method using an aqueous buffer:methanol solution of 2-nitrobenzaldehyde (10 g/L) used as a derivatizing agent. The mainstream smoke of cigarettes was passed through a glass fiber filter pad followed by a trapping solution containing an isotopically labeled 15N2-hydrazine used as internal standard. After smoking, the filter pad was extracted with the trapping solution and then incubated for 30 minutes at 35°C. An aliquot of the extract was centrifuged and the resultant hydrazone was quantified by liquid chromatography tandem mass spectrometry (LC-MS/MS). The isotope dilution standard calibration curve demonstrated good linearity (R2 > 0.999) from 0.079 to 248 ng/mL, with limits of quantification in mainstream smoke of 0.2 and 0.4 ng/cig for ISO and Canadian Intense smoking regimens, respectively. The method recovery was assessed using samples spiked with solutions of known amounts of hydrazine. The results showed good accuracy with recoveries ranging from 98 to 111%. Although there were no detectable levels of hydrazine in the reference cigarettes used in the validation (KR3R4F), the method precision was estimated to be ~10% based on the variability observed in the spiked samples. Trapping efficiencies were assessed using a hydrazine permeation tube providing a known amount of hydrazine vapor such that the distribution between the vapor phase and particulate phase of mainstream smoke could be determined.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".