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Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
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Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas

Meriem Gaida1, Pierre-Hugues Stefanuto1, Jean-François Focant1

  • 1Organic and Biological Analytical Chemistry Group (OBiAChem), MolSys Research Unit, Liège University, Belgium.

Journal of Chromatography. A
|October 23, 2023
PubMed
Summary
This summary is machine-generated.

Comprehensive two-dimensional gas chromatography (GC×GC) offers superior separation for complex samples. Optimizing methods with theoretical modeling and advanced data processing, including machine learning, is crucial for extracting meaningful insights.

Area of Science:

Keywords:
Comprehensive two-dimensional gas chromatographyData processingMachine LearningMethod developmentModeling

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  • Analytical Chemistry
  • Chromatography

Background:

  • Comprehensive two-dimensional gas chromatography (GC×GC) provides enhanced separation capabilities over conventional GC.
  • Effective utilization of GC×GC requires meticulous method development and data processing strategies.

Conclusions:

  • A holistic approach combining optimized separation and advanced data processing is essential for maximizing GC×GC utility.
  • Theoretical modeling and ML algorithms are key components for successful and informative GC×GC analyses.