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Thermo Scientific Orbitrap GC-MS HRAM Flavor and Fragrances Library

Brochures and specifications | 2024 | Thermo Fisher ScientificInstrumentation
GC/MSD, GC/MS/MS, GC/HRMS, GC/Orbitrap
Industries
Other
Manufacturer
Thermo Fisher Scientific

Summary

Importance of the Topic


Flavor and fragrance profiling is a cornerstone of quality control and product development in food, beverage, cosmetics and perfumery industries. Reliable characterization of the chemical composition ensures consistent sensory properties, traces the origin of raw materials and supports compliance with regulatory standards. High-resolution accurate mass (HRAM) gas chromatography-mass spectrometry (GC-MS) libraries accelerate compound identification, reduce analysis time and increase confidence in results.

Objectives and Study Overview


This application note presents the Thermo Scientific Orbitrap GC-MS Flavor and Fragrances Library, designed to facilitate comprehensive screening of flavor and fragrance compounds. Key goals include:
  • Compiling HRAM electron ionization spectra for over 400 compounds across eleven chemical classes
  • Enabling rapid method setup and data processing for both targeted and untargeted analyses
  • Demonstrating sub-ppm mass accuracy and retrospective data mining capabilities

Methodology and Instrumentation


The library was generated using an Orbitrap Exploris GC mass spectrometer operated in full-scan mode at 60 000 resolving power (FWHM at m/z 200). Electron ionization spectra were acquired and each fragment’s elemental composition verified. The resulting HRAM spectra and corresponding Kovats retention indices were curated and formatted for use in Thermo Scientific TraceFinder and Compound Discoverer software. Chromatographic deconvolution workflows combined with accurate-mass spectral matching enable both targeted quantitation and untargeted screenings.

Used Instrumentation


  • Thermo Scientific Orbitrap Exploris GC-MS
  • Thermo Scientific TraceFinder software for full-scan screening and quantitation
  • Thermo Scientific Compound Discoverer software for deconvolution, statistical analysis and library matching

Main Results and Discussion


The library encompasses 411 compounds sorted into eleven categories, including:
  • Esters (116) and alcohols (60)
  • Phenols (51), ketones (41) and aldehydes (42)
  • Terpenes (34), heterocyclics (41) and ethers (22)
  • Plus minor classes such as alkanes, BTEX and nitriles

Sub-ppm mass accuracy was maintained across varying peak intensities, enabling narrow mass tolerance windows and confident identifications. The HRAM library supports unlimited analyte targeting down to triple quadrupole detection limits and retrospective screening of compounds not initially targeted.

Benefits and Practical Applications


  • Rapid method development: Pre-built spectral entries and retention indices simplify screening setup
  • High confidence identifications: Sub-ppm mass errors and full-scan data provide robust confirmation
  • Retrospective analyses: Full-scan acquisition enables post-run searches for new targets
  • Software integration: Seamless workflows in TraceFinder and Compound Discoverer streamline data processing

Future Trends and Potential Applications


Advances in HRAM GC-MS coupled with machine learning will further accelerate unknown compound discovery and pattern recognition in complex matrices. Expansion of spectral libraries to include novel natural and synthetic flavor ingredients will support emerging food authenticity challenges. Real-time data processing and cloud-based library sharing are poised to enhance collaborative quality control across global supply chains.

Conclusion


The Thermo Scientific Orbitrap GC-MS Flavor and Fragrances Library offers a comprehensive HRAM spectral resource for rapid, reliable analysis of flavor and fragrance compounds. Its integration with powerful software tools and sub-ppm accuracy ensures high throughput, robust identifications and the ability to revisit historical data for new insights.

Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.

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