Multivariate optimization of method for analysis of emissions from heated tobacco by HS-SPME GC×GC-TOFMS
Bibliographic record
Abstract
Cigarette smoke is a highly complex dynamic aerosol system generated by distillation, pyrolysis and combustion reactions when the tobacco is burnt. As the burning tip of a cigarette reaches temperatures up to 1000oC, more than 6800 compounds have been identified in mainstream smoke. Heating tobacco to temperatures lower than 300oC simplifies the composition of emissions by lowering the production of chemicals. The study focused on developing and optimising an analytical strategy for the characterisation of heated tobacco. Emissions were generated using an A14 smoking engine from Borgwaldt. Sampling was performed according to the Health Canada Intense applying 12 bell shaped puffs of 55ml volume, 2s puff duration and 30s interval between the puffs. Emissions were captured on glass fiber filter for Head Space Solid-Phase Micro Extraction (HS-SPME) analysis. Experimental design was applied for the optimization of the HS-SPME extraction parameters. The emmisions of heated tobacco have been analyzed by means of comprehensive two-dimennsional gas chromatography coupled to time of flight mass spectrometry (GCxGC-TOFMS). Based on initial results, the complexity of heated tobacco emissions appeared to be quite complex. The peak table-based processing software used for the study revealed up to 7000 hits (S/N > 100) depending on the SPME fiber used. Unsupervised library search results of studied emissions revealed up to 2500 unique and acceptably identified compounds (library matching higher than 75%). The range of identified compounds was in similar order of magnitude compared to combustible tobacco products studied in details earlier.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".