Breath analysis of smokers, non-smokers, and e-cigarette users

•Breath analysis can discriminate smokers, non-smokers, and e-cigarette (vape) users.•SPME-GC/MS followed by multivariate data analysis separated the three groups.•Compounds related mainly to e-liquid flavors separate vape users from smokers.•PCA and PLS-DA provided corroborating models with accurac...

Full description

Saved in:
Bibliographic Details
Published in:Journal of chromatography. B, Analytical technologies in the biomedical and life sciences Analytical technologies in the biomedical and life sciences, 2020-12, Vol.1160, p.122349-122349, Article 122349
Main Authors: Papaefstathiou, E., Stylianou, M., Andreou, C., Agapiou, A.
Format: Article
Language:English
Subjects:
Adult
Aged
Breath Tests - methods
Chemometrics
Cigarette Smoking - metabolism
Exhaled breath
Female
Gas Chromatography-Mass Spectrometry - methods
Humans
Male
Middle Aged
Non-Smokers
Smokers
Smoking
Solid Phase Microextraction - methods
SPME-GC/MS
Vape
Vaping - metabolism
VOCs
Volatile Organic Compounds - analysis
Young Adult
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Breath analysis can discriminate smokers, non-smokers, and e-cigarette (vape) users.•SPME-GC/MS followed by multivariate data analysis separated the three groups.•Compounds related mainly to e-liquid flavors separate vape users from smokers.•PCA and PLS-DA provided corroborating models with accuracy >80%•Other lifestyle factors may confound the analysis. Solid phase micro extraction-Gas Chromatography/Mass Spectrometry (SPME-GC/MS) analysis was performed in exhaled breath samples of 48 healthy volunteers: 20 non-smokers, 10 smokers and 18 e-cigarette (EC, vape) users. Each volunteer provided 1 L of exhaled breath in a pre-cleaned Tedlar bag, in which an SPME fiber was exposed to absorb the emitted breath volatile organic compounds (VOCs). The acquired data were processed using multivariate data analysis (MDA) methods in order to identify the characteristic chemicals of the three groups. The results revealed that the breath of non-smokers demonstrated inverse correlation with a variety of molecules related to the breath from smokers including furan, toluene, 2-butanone and other organic substances. Vapers were distinguished from smokers by the chemical speciation of the e-liquids, such as that of esters (e.g. ethyl acetate), terpenes (e.g. α-pinene, β-pinene, d-limonene, p-cymene, etc.) and oxygenated compounds (e.g. 3-hexen-1-ol, benzaldehyde, hexanal, decanal, etc). Two classification models were developed (a) using principal component analysis (PCA) with hierarchical cluster analysis (HCA) and (b) using partial least squares-discriminant analysis (PLS-DA). Both models were validated using 8 new samples (4 vapers and 4 smokers), collected in addition to the 48 samples of the calibration set. The combination of GC/MS breath analysis and MDA contributed successfully in classifying the volunteers into their respective groups and highlighted the relevant characteristic VOCs. The respective dynamic combination (SPME-GC/MS and MDA) provides a means for long term non-invasive monitoring of the population’s health status for early detection purposes.
ISSN:1570-0232
1873-376X
DOI:10.1016/j.jchromb.2020.122349