Advanced Data Visualization: The Many Dimensions of Petroleomics Using High Resolution Gas Chromatography and Time-of-Flight Mass Spectrometry

Significance of the topic

Two-dimensional gas chromatography coupled with high-resolution time-of-flight mass spectrometry (GC×GC-TOF MS) offers unparalleled separation and detection power for complex hydrocarbon mixtures. In petroleomics, this approach is critical for profiling thousands of molecular species in crude oil, enabling better understanding of feedstock composition, refinery optimization, and environmental monitoring.

Study Objectives and Overview

This work illustrates the advanced data visualization features of ChromaTOF-HRT software applied to two heavy Venezuelan crude oil samples (Sample A and Sample B). The key goals are:

• Automated classification of compound classes via deconvolution and spectral filtering.
• Multi-dimensional visualization of heteroatom distributions using Kendrick mass defect, van Krevelen, and C# vs. RDBE plots.
• Identification of unknown compounds through library matching and database searches.

Methodology and Instrumentation

Samples were analyzed on a LECO GC×GC-TOF MS system under the following conditions:

• Injection: split ratio 20:1, filament current 0.5 mA, inlet temperature 300 °C.
• Modulation: 120 s period with 1 s first-dimension and 3 s second-dimension sampling.
• Data Processing: ChromaTOF-HRT software for spectral deconvolution, mass defect series selection, formula calculation, and structured chromatogram visualization.

Main Results and Discussion

Comparative analysis revealed distinct compositional differences between samples:

• Compound Class Distribution: Sample A was enriched in paraffins and cycloparaffins, whereas Sample B exhibited higher proportions of diaromatics and heteroatom-containing species.
• Mass Defect Insights: Kendrick plots highlighted CH2 homologous series; van Krevelen plots mapped oxygenated compounds to assess thermal maturity.
• Structural Complexity: C# vs. RDBE plots demonstrated differences in unsaturation and molecular size across hydrocarbon classes.
• Unknown Compound Identification: A hopane-related peak (m/z 191.1795) was deconvoluted, matched to C31H54 with <0.5 ppm mass error, and tentatively assigned via ChemSpider as a 5-(11-henicosanyl)-1,2,3,4-tetrahydronaphthalene fragment.

Benefits and Practical Applications

The integrated visualization and analysis tools enable:

• Rapid, automated classification of complex mixtures containing thousands of compounds.
• Detailed heteroatom mapping for environmental, QA/QC, and feedstock evaluation workflows.
• High-confidence compound identification by correlating chromatographic retention with high-resolution mass spectral data.
• Efficient comparison of multiple samples to guide refining decisions and investigate compositional changes.

Future Trends and Potential Applications

Emerging directions include:

• Machine learning-driven pattern recognition for automated sample classification and anomaly detection.
• Online GC×GC-TOF MS for real-time process monitoring in refinery and petrochemical operations.
• Expansion to other complex matrices such as bio-oils, environmental samples, and pharmaceuticals.
• Cloud-based data sharing and collaborative annotation platforms integrated with spectral databases.

Conclusion

Combining GC×GC with high-resolution TOF MS and advanced data visualization in ChromaTOF-HRT provides a powerful workflow for petroleomic studies. Multi-dimensional plots and automated classification accelerate identification, deepen compositional insights, and support a wide range of industrial and research applications.