Determination of Free Solanesol Levels in Cigarette Filters by Liquid Chromatography–Mass Spectrometry
Bibliographic record
Abstract
Solanesol, a naturally occurring constituent of tobacco, has been utilized as a good marker for environmental tobacco smoke particulate and as a noninvasive predictor of mainstream cigarette smoke tar and nicotine intake under naturalistic smoking conditions. A fast and accurate method for measuring free solanesol to assess tobacco smoke exposure is highly desirable. We have developed and validated a new environmentally friendly, high-throughput method for measuring solanesol content in discarded cigarette filter butts. The solanesol deposited in the used filters can be correlated with mainstream smoke deliveries of nicotine and total particle matter to estimate constituent delivery to smokers. A portion of filter material is removed from cigarette butts after machine smoking, spiked with internal standard solution, extracted and quantitatively analyzed using reverse-phase liquid chromatography coupled to a triple-quadrupole mass spectrometer. The new method incorporates a 48-well plate format for automated sample preparation that reduces sample preparation time and solvent use and increases sample throughput 10-fold compared to our previous method. Accuracy and precision were evaluated by spiking known amounts of solanesol on both clean and smoked cigarette butts. Recoveries exceeded 93% at both low and high spiking levels. Linear solanesol calibration curves ranged from 1.9 to 367 µg/butt with a 0.05 µg/butt limit of detection.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".