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<p>The use of e-cigarettes has increased globally, including in Malaysia, and is accompanied by the rapid expansion of flavors and e-liquid formulations. This growing trend raises concerns about chemical exposure, product safety, and the accuracy of labeling, particularly regarding nicotine content. To address these issues, this study investigated the metabolite composition of flavored e-liquids using headspace gas chromatography-mass spectrometry (HS-GC-MS), which was subjected to both supervised and unsupervised learning techniques to identify and understand the differences in metabolite composition. A total of 143 metabolites were detected across the four samples, with nicotine consistently present, even in products labeled as nicotine-free, suggesting potential mislabeling. Comparative analyses using volcano plots indicated that LC01 and IM02 exhibited the lowest dissimilarity, whereas LC02 and IM01 exhibited the highest similarity. Both hierarchical clustering (HCA) and k-means clustering classified the samples into four distinct groups. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) demonstrated consistent separation patterns, with PLS-DA achieving robust validation (5-fold cross-validation: R2, Q2, and accuracy ≥ 0.99; permutation test (<i>n</i> = 1000): <i>p</i> &lt; 0.001). Twenty-five metabolites were identified as key biomarkers, with piperonal, tetradecanoic acid ethyl ester, and alpha-hellandrene emerging as the most discriminative. These findings highlight the potential of metabolomic profiling to differentiate e-liquids by their chemical composition, reveal mislabeling of nicotine content, and emphasize the need for stricter regulatory oversight and consumer protection measures as e-cigarette use continues to rise.</p>

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Non-Targeted Metabolomic Profiling of E-Cigarette Liquids Via Headspace-Gas Chromatography-Mass Spectrometry

  • 期刊论文

  • 发表日期：

    2026年1月22日

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  • Ganapaty Manickavasagam,
  • Mohd Reduan Bin Selamat,
  • Alfiah Hayati,
  • Wen Nee Tan,
  • Vuanghao Lim

摘要

The use of e-cigarettes has increased globally, including in Malaysia, and is accompanied by the rapid expansion of flavors and e-liquid formulations. This growing trend raises concerns about chemical exposure, product safety, and the accuracy of labeling, particularly regarding nicotine content. To address these issues, this study investigated the metabolite composition of flavored e-liquids using headspace gas chromatography-mass spectrometry (HS-GC-MS), which was subjected to both supervised and unsupervised learning techniques to identify and understand the differences in metabolite composition. A total of 143 metabolites were detected across the four samples, with nicotine consistently present, even in products labeled as nicotine-free, suggesting potential mislabeling. Comparative analyses using volcano plots indicated that LC01 and IM02 exhibited the lowest dissimilarity, whereas LC02 and IM01 exhibited the highest similarity. Both hierarchical clustering (HCA) and k-means clustering classified the samples into four distinct groups. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) demonstrated consistent separation patterns, with PLS-DA achieving robust validation (5-fold cross-validation: R2, Q2, and accuracy ≥ 0.99; permutation test (n = 1000): p < 0.001). Twenty-five metabolites were identified as key biomarkers, with piperonal, tetradecanoic acid ethyl ester, and alpha-hellandrene emerging as the most discriminative. These findings highlight the potential of metabolomic profiling to differentiate e-liquids by their chemical composition, reveal mislabeling of nicotine content, and emphasize the need for stricter regulatory oversight and consumer protection measures as e-cigarette use continues to rise.