Comprehensive Characterization of Green Leaf Tobacco Extracts Using GC-HRT

Importance of the Topic

Understanding the complex chemical composition of tobacco leaf extracts is critical for assessing flavor attributes, health risks, and metabolic pathways. High-resolution mass spectrometry coupled with advanced data deconvolution methods enables confident identification of a wide range of metabolites and trace compounds in plant matrices.

Objectives and Study Overview

• Evaluate the performance of a Pegasus GC–High Resolution Time-of-Flight Mass Spectrometer (GC-HRT) for non-targeted profiling of green leaf tobacco extracts.
• Demonstrate the use of High Resolution Deconvolution™ (HRD™) to separate coeluting analytes and match spectra against EI-MS libraries.
• Leverage accurate mass measurements in both electron ionization (EI) and chemical ionization (CI) modes to confirm molecular formulas of known and unknown compounds.

Methodology and Instrumentation

Leaf samples were extracted with toluene and analyzed in splitless injection mode (1 µL) for EI and 2 µL for CI. Key instrument settings included:

• Gas chromatograph: Agilent 7890 with 7693 autosampler
• Column: Rxi-5Sil MS, 30 m×0.25 mm×0.25 µm
• Temperature program: 50 °C (1 min) to 300 °C @ 10 °C/min (10 min)
• Carrier gas: He at 1.0 mL/min
• Mass spectrometer: LECO Pegasus GC-HRT, resolution 25 000 FWHM, acquisition rate 6 spectra/s
• Ionization modes: EI at 250 °C source; CI with CH₄ reagent gas at 200 °C source
• Mass ranges: m/z 28–510 (EI) and 45–800 (CI)

Main Results and Discussion

Non-targeted profiling revealed key compound classes including alkaloids, terpenes, terpenoids, sterols, organic acids, amino acids, and volatile aromatics. HRD effectively separated coeluting sterols such as vitamin E and cholesterol, yielding high-quality deconvoluted spectra. Mass accuracies for representative compounds averaged 1.01 ppm, with spectral similarity scores between 758 and 895/1000. Plant sterols campesterol, stigmasterol, sitosterol, and α-amyrin were identified with library similarities above 822 and mass errors below 2 ppm. Complementary CI-HRT spectra provided protonated molecular ions that, when combined with EI data, corrected potential misassignments and improved confidence in formula determination.

Benefits and Practical Applications

• High confidence in metabolite identification by combining spectral deconvolution with accurate mass measurements.
• Simultaneous monitoring of flavor compounds, metabolic intermediates, and known toxicants in tobacco products.
• Enhanced non-targeted analysis workflow for natural product research, QA/QC in agricultural and tobacco industries.

Future Trends and Opportunities

Advancements in spectral libraries and integration of machine-learning-based deconvolution will further improve annotation of unknown metabolites. Expanding this approach to other plant matrices and coupling with tandem MS techniques will enable more detailed structural elucidation. Quantitative workflows leveraging high-resolution full-scan data and data mining tools will enhance large-scale metabolomics and regulatory compliance studies.

Conclusion

The GC-HRT platform with HRD and accurate mass EI/CI data provides a robust solution for comprehensive characterization of tobacco leaf extracts. The combined workflow delivers high-quality spectral deconvolution, precise mass measurement, and confident identification of diverse compound classes relevant to flavor profiling, metabolic research, and health risk assessment.

References

• LECO Corporation. Comprehensive Characterization of Green Leaf Tobacco Extracts Using GC-HRT. Application Note Form No. 203-821-481; 2014.