LECO PEGASUS® GC-HRT+
Brochures and specifications | 2020 | LECOInstrumentation
High-resolution and accurate mass GC-TOFMS analysis is essential for confidently identifying trace components in complex matrices encountered in environmental, petrochemical, food, forensic, and life science applications. Enhanced resolving power minimizes coelution interferences and reduces false positives in untargeted and targeted studies.
This whitepaper introduces the PEGASUS GC-HRT system, designed to combine ultra-high mass resolution with parts-per-million mass accuracy and advanced data processing to streamline compound identification and quantitation in difficult sample matrices.
Ongoing developments will integrate advanced spectral analysis tools, AI-driven deconvolution, and expanded multidimensional separations. Data-rich outputs will support machine learning for predictive compound annotation and real-time decision making.
The PEGASUS GC-HRT platform delivers exceptional mass resolution, accuracy, and sensitivity, empowering analysts to resolve coelution challenges and accelerate confident identification in the most complex samples.
GC/MSD, GC/HRMS, GC/TOF
IndustriesManufacturerLECO
Summary
Importance of the Topic
High-resolution and accurate mass GC-TOFMS analysis is essential for confidently identifying trace components in complex matrices encountered in environmental, petrochemical, food, forensic, and life science applications. Enhanced resolving power minimizes coelution interferences and reduces false positives in untargeted and targeted studies.
Study Objectives and Overview
This whitepaper introduces the PEGASUS GC-HRT system, designed to combine ultra-high mass resolution with parts-per-million mass accuracy and advanced data processing to streamline compound identification and quantitation in difficult sample matrices.
Methodology and Instrumentation
- Folded Flight Path (FFP) technology with selectable 20 m or 40 m flight paths for resolution up to 50 000 at m/z 200
- High Resolution Deconvolution (HRD) using statistical algorithms to separate coeluting analytes at resolutions of 25 000 and above
- KADAS data acquisition system based on ion statistics for noise reduction, accurate mass assignment, and efficient file sizes
- Encoded Frequent Pushing (EFP) to boost duty cycle and sensitivity by a factor of ten
- Interchangeable high-resolution Electron Impact (HR-EI) and optional Chemical Ionization (HR-CI) sources
- ChromaTOF software with ribbon-style interface for deconvolution, library search, mass defect plotting, and automated workflows
Main Results and Discussion
- Mass resolution of 50 000 and mass accuracy of 1 ppm at 200 spectra/s enables clear separation of isobaric ions such as cyanazine and metolachlor
- HRD effectively deconvolutes complex tobacco extracts at 25 000 resolution, resolving critical target analytes like Lindane and Neophytadiene
- Detection limits below 100 fg OFN with quantitative dynamic range spanning 0.10 pg to 10 000 pg (5 orders of magnitude) demonstrate unparalleled sensitivity
- Mass defect plots facilitate rapid screening of unknowns using scalable defect axes (Kendrick, halogen, etc.)
Benefits and Practical Applications
- Accelerated identification through combined accurate-mass fragment and molecular ion matches with library searches
- Automated tuning, front-end maintenance with pneumatic gate valve, and streamlined software tools enhance laboratory throughput
- Versatile configurations support single-dimensional GC, GC×GC, and multi-industry workflows from QA/QC to metabolomics
Future Trends and Opportunities
Ongoing developments will integrate advanced spectral analysis tools, AI-driven deconvolution, and expanded multidimensional separations. Data-rich outputs will support machine learning for predictive compound annotation and real-time decision making.
Conclusion
The PEGASUS GC-HRT platform delivers exceptional mass resolution, accuracy, and sensitivity, empowering analysts to resolve coelution challenges and accelerate confident identification in the most complex samples.
Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.
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