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<p>One-dimensional gas chromatography coupled with time-of-flight mass spectrometry (1D GC-TOF/MS) and comprehensive two-dimensional gas chromatography coupled with TOF/MS (GC×GC-TOF/MS) were evaluated as complementary platforms for diesel-fuel compositional profiling. Across all samples, 1D GC-TOF/MS reported 1,850 library-annotated features (similarity score ≥ 700), whereas GC×GC-TOF/MS reported 27,146 deconvoluted features, comprising 8,108 annotated features and 19,038 unnamed Peak-ID features. Only 595 annotated features were common to both workflows, yet workflow-specific statistical evaluation and PCA of GC×GC feature-occurrence profiles consistently highlighted the same atypical samples. Broad chemical grouping of annotated and predicted GC×GC features showed that paraffinic and olefinic/naphthenic hydrocarbon families dominated most fuels. In contrast, sample 51 × 57, and to a lesser extent K8LZ1 depending on feature inclusion, exhibited redistributed class patterns and a higher proportion of oxygenated or FAME-associated features. Integrating chromatographic profiling with routine physicochemical descriptors indicated that meaningful compositional divergence may occur even within a narrow specification window. Overall, 1D GC-TOF/MS remains effective for robust comparative screening, while GC×GC-TOF/MS provides additional selectivity and peak capacity to interrogate compositionally congested diesel matrices and to support future chemometric modelling.</p>

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Complementary diesel-fuel compositional profiling by one-dimensional and comprehensive two-dimensional GC-TOF/MS

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月29日

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  • Tomasz Zieliński,
  • Joanna Szpotkowska,
  • Joanna Rudnicka,
  • Aleksandra Bogumiła Florkiewicz,
  • Edyta Szyszko,
  • Tomáš Kovalczuk,
  • Kamila Grygiel,
  • Paweł Pomastowski

摘要

One-dimensional gas chromatography coupled with time-of-flight mass spectrometry (1D GC-TOF/MS) and comprehensive two-dimensional gas chromatography coupled with TOF/MS (GC×GC-TOF/MS) were evaluated as complementary platforms for diesel-fuel compositional profiling. Across all samples, 1D GC-TOF/MS reported 1,850 library-annotated features (similarity score ≥ 700), whereas GC×GC-TOF/MS reported 27,146 deconvoluted features, comprising 8,108 annotated features and 19,038 unnamed Peak-ID features. Only 595 annotated features were common to both workflows, yet workflow-specific statistical evaluation and PCA of GC×GC feature-occurrence profiles consistently highlighted the same atypical samples. Broad chemical grouping of annotated and predicted GC×GC features showed that paraffinic and olefinic/naphthenic hydrocarbon families dominated most fuels. In contrast, sample 51 × 57, and to a lesser extent K8LZ1 depending on feature inclusion, exhibited redistributed class patterns and a higher proportion of oxygenated or FAME-associated features. Integrating chromatographic profiling with routine physicochemical descriptors indicated that meaningful compositional divergence may occur even within a narrow specification window. Overall, 1D GC-TOF/MS remains effective for robust comparative screening, while GC×GC-TOF/MS provides additional selectivity and peak capacity to interrogate compositionally congested diesel matrices and to support future chemometric modelling.