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.

在48名健康志愿者的呼出气样本中进行了固相微萃取-气相色谱/质谱（SPME-GC / MS）分析：20名不吸烟者，10名吸烟者和18名电子烟（EC，vape）用户。每个志愿者在预先清洁的Tedlar袋中提供1 L呼气，其中将SPME纤维暴露以吸收释放的呼吸中的挥发性有机化合物（VOC）。使用多变量数据分析（MDA）方法处理获取的数据，以识别三组的特征化学物质。结果表明，非吸烟者的呼吸与吸烟者的多种呼吸相关分子呈负相关，包括呋喃，甲苯，2-丁酮和其他有机物质。电子烟液体的化学形态将Vapers与吸烟者区分开，例如乙酸乙酯），萜烯（例如α-pine烯，β-pine烯，d-柠檬烯，对伞花烯等）和含氧化合物（例如3-己烯-1-醇，苯甲醛，己醛，癸醛等）。开发了两个分类模型（a）使用主成分分析（PCA）和层次聚类分析（HCA），以及（b）使用偏最小二乘判别分析（PLS-DA）。除了校准组的48个样本外，还使用8个新样本（4个vapers和4个吸烟者）对这两个模型进行了验证。GC / MS呼吸分析和MDA的结合成功地将志愿者分为各自的组，并突出了相关的特征性VOC。各自的动态组合（SPME-GC / MS和MDA）为长期无创监测人群的健康状况提供了一种手段，以便及早发现。