Chemical analysis and hazard identification of the most common electronic cigarette liquids in nine European countries
Bibliographic record
Abstract
Background We aimed to detect the composition and reported chemical health hazards of the most common electronic cigarette liquids (e-liquids) in nine European Union (EU) Member States (MS) prior to adoption of the Tobacco Product Directive (TPD). Material and Methods Within the Horizon2020, EUREST-PLUS study, 122 of the most commonly used e-liquids were purchased from 9 EU MS. Chromatography - mass spectrometry and liquid chromatography - mass spectrometry methods were used to analyze the samples. Among the most frequently detected compounds (detected ≥4 times), Danger Globally Harmonized System of Classification and Labelling of Chemicals (GHS) and Warning GHS codes were identified. Results During the samples analysis, several discrepancies in nicotine concentration were detected among the samples from the 9 EU MS. French samples contained an average of 21.9% more nicotine than labelled, while Romanian samples contained an average of 22.5% less nicotine than labelled. In addition, in the 9.8% of the samples, the nicotine concentration exceeded the limit of 20 mg/ml. With regards to the samples’ composition, 171 different compounds were identified and detected 750 times in total while we did not identify samples positive for PAHs or nitrosamines. Finally from the 171 substances, only 5 (10.4%), (Oxime-, methoxy-phenyl, +/-.-.alpha.-Methylbenzyl acetate, 1,3-Dioxolane, 2-butyl-4-methyl-, Melonal and l-Menthyl acetate) were not associated with a Danger GHS and Warning GHS codes. Conclusions As large number of potential harmful compounds was identified, the systematic monitoring and chemical evaluation of e-liquids is necessary in order to protect the consumers’ health. Acknowledgements EUREST-PLUS is a Horizon2020 project conducted by researchers throughout Europe from both the six participating countries as well as other institution partners within Europe and abroad. Partnering organizations include the European Network on Smoking Prevention (Belgium), Kings College London (United Kingdom), German Cancer Research Centre (Germany), University of Maastricht (The Netherlands), University of Athens (Greece), Aer Pur Romania (Romania), European Respiratory Society (Switzerland), the University of Waterloo (Canada), the Catalan Institute of Oncology (Catalonia, Spain), Smoking or Health Hungarian Foundation (Hungary), Health Promotion Foundation (Poland), University of Crete (Greece), and Kantar Public Brussels (Belgium). Funding The EUREST-PLUS Project takes place with the financial support of the European Commission, Horizon 2020 HCO-6-2015 program (EUREST-PLUS: 681109; C. Vardavas) and the University of Waterloo (GT. Fong). Additional support was provided to the University of Waterloo by the Canadian Institutes of Health Research (FDN-148477). GT. Fong was supported by a Senior Investigator Grant from the Ontario Institute for Cancer Research. E. Fernández is partly supported by Ministry of Universities and Research, Government of Catalonia (2017SGR139) and by the Instituto Carlos III and co-funded by the European Regional Development Fund (FEDER) (INT16/00211 and INT17/00103), Government of Spain.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".