ArticleFrontiers in chemistry2021
Non-Targeted Analysis Using Gas Chromatography-Mass Spectrometry for Evaluation of Chemical Composition of E-Vapor Products.
Article in Frontiers in chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 18 citations in OpenAlex.
- Quantitative method comparison in non-targeted analysis using cascade classification.Analytical and bioanalytical chemistry · 2026Article
- 3D Printing Filament Composition, Emissions, and Induced Proinflammatory Responses.Chemical research in toxicology · 2026Article
- Artificial intelligence to investigate metabolomics data for precision medicine.Metabolomics : Official journal of the Metabolomic Society · 2026Review
- Pitfalls in the Detection of Volatiles Associated with Heated Tobacco and e-Vapor Products When Using PTR-TOF-MS.Journal of the American Society for Mass Spectrometry · 2024Article
- A literature review addressing midwakh and e-cigarette use in the Gulf region.The Journal of the Egyptian Public Health Association · 2023Review
- Nontargeted Analysis in Tobacco Research: Challenges and Opportunities.Chemical research in toxicology · 2023Review
- Uncertainty estimation strategies for quantitative non-targeted analysis.Analytical and bioanalytical chemistry · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Premarket Tobacco Product Applications (PMTA) guidance issued by the Food and Drug Administration for electronic nicotine delivery systems (ENDSs) recommends that in addition to reporting harmful and potentially harmful constituents (HPHCs), manufacturers should evaluate these products for other chemicals that could form during use and over time. Although e-vapor product aerosols are considerably less complex than mainstream smoke from cigarettes and heated tobacco product (HTP) aerosols, there are challenges with performing a comprehensive chemical characterization. Some of these challenges include the complexity of the e-liquid chemical compositions, the variety of flavors used, and the aerosol collection efficiency of volatile and semi-volatile compounds generated from aerosols. In this study, a non-targeted analysis method was developed using gas chromatography-mass spectrometry (GC-MS) that allows evaluation of volatile and semi-volatile compounds in e-liquids and aerosols of e-vapor products. The method employed an automated data analysis workflow using Agilent MassHunter Unknowns Analysis software for mass spectral deconvolution, peak detection, and library searching and reporting. The automated process ensured data integrity and consistency of compound identification with >99% of known compounds being identified using an in-house custom mass spectral library. The custom library was created to aid in compound identifications and includes over 1,100 unique mass spectral entries, of which 600 have been confirmed from reference standard comparisons. The method validation included accuracy, precision, repeatability, limit of detection (LOD), and selectivity. The validation also demonstrated that this semi-quantitative method provides estimated concentrations with an accuracy ranging between 0.5- and 2.0-fold as compared to the actual values. The LOD threshold of 0.7 ppm was established based on instrument sensitivity and accuracy of the compounds identified. To demonstrate the application of this method, we share results from the comprehensive chemical profile of e-liquids and aerosols collected from a marketed e-vapor product. Applying the data processing workflow developed here, 46 compounds were detected in the e-liquid formulation and 55 compounds in the aerosol sample. More than 50% of compounds reported have been confirmed with reference standards. The profiling approach described in this publication is applicable to evaluating volatile and semi-volatile compounds in e-vapor products.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.