Webinars

From Bottlenecks to Breakthroughs: Overcoming GC–MS Limitations in Non-Target Screening

Available On-Demand


Discover the advantages of GC×GC and how it surpasses traditional GC in analysing complex samples.

Event Overview

Non-target screening of complex samples is often hampered by challenges such as co-elutions and trace-level analytes, leading to low-confidence identifications or entirely missed compounds. Traditional one-dimensional GC–MS struggles to meet the demands of intricate petrochemical, food, and biological analyses, resulting in errors, inefficiencies, and lost insights.

GC×GC–TOF MS overcomes these challenges with enhanced separation, reliable compound identification, and advanced data analysis tools. This presentation demonstrates how GC×GC–TOF MS transforms non-target screening, with practical examples from sustainable fuel analysis, aroma profiling, and essential oil characterization.

By viewing this presentation, you will:

  • Discover the advantages of GC×GC and how it surpasses traditional GC in analysing complex samples.
  • Explore the latest enhancements to BenchTOF2 mass spectrometers, designed to support confident trace-level identification, intuitive workflows, and reliable performance.
  • Learn how intelligent data analysis tools can automatically identify key features that differentiate complex samples.
  • Gain insights from real-world applications, including sustainable fuel analysis, food and beverage aroma profiling, and essential oil comparisons.

What you need to know:

Format: On-demand

Duration: 57 minutes

Presenter:

Laura McGregor
Head of SepSolve Product Group, SepSolve Analytical

Laura McGregor completed a Ph.D. in Environmental Forensics at the University of Strathclyde in 2012. Her research interests included the chemical fingerprinting of coal tar contamination using GC×GC–TOF MS. In her current role at SepSolve Analytical, she oversees marketing activities for the full product portfolio and specializes in applying GC×GC and chemometrics to challenging samples.

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