Puff-by-puff Mainstream Smoke Analysis by Multiplex Gas Chromatography-Mass Spectrometry
Bibliographic record
Abstract
Abstract A puff-by-puff mainstream smoke procedure has been developed that provides the sensitivity and selectivity of a gas chromatography-mass spectrometry (GC-MS) system. The smoke analysis is based on automated sample collection and injection into the GC system. This development builds on, and complements, prior puff-by-puff procedures developed by Philip Morris USA, that utilized infrared (IR) analysis of gas-phase mainstream smoke. IR analysis of the gas-phase smoke for individual smoke constituents relies on the unique spectroscopic absorption patterns of each analyte. The new multiplex procedure relies on both chromatographic separation as well as spectroscopic separation. A significant feature of this method is that multiple injections are made prior to the complete elution of the first injected sample. The benefits of this methodology are that both sensitivity and the number of detected compounds are enhanced. While the multiplex method increases the complexity of the chromatographic data, the mass spectral analysis provides a means for data reduction to meaningful results. Many smoke constituents that are at concentrations below the Fourier transform infrared (FTIR) detection limit are observable with the multiplex analysis while maintaining the feature of puff-by-puff characterization of fresh smoke. The gas-phase mainstream smoke filtration performance of standard adsorption materials are discussed as a demonstration of the versatility and information content of this analytical procedure.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".