A data-challenge case study of analyte detection and identification with comprehensive two-dimensional gas chromatography with mass spectrometry (GC×GC-MS)

Stephen E. Reichenbach, Qingping Tao, Chiara Cordero, Carlo Bicchi

Research output: Contribution to journalArticle

Abstract

This case study describes data analysis of a chromatogram distributed for the 2019 GC×GC Data Challenge for the Tenth Multidimensional Chromatography Workshop (Liege, Belgium). The chromatogram resulted from chemical analysis of a terpene-standards sample by comprehensive two-dimensional chromatography with mass spectrometry (GC×GC-MS). First, several aspects of the data quality are assessed, including detector saturation and oscillation, and operations to prepare the data for analyte detection and identification are described, including phase roll for modulation-cycle alignment and baseline correction to account for the non-zero detector baseline. Then, the case study presents operations for analyte detection with filtering, a new method to flag false detections, interactive review to confirm detected peaks, and ion-peaks detection to reveal peaks that are obscured by noise or coelution. Finally, the case study describes analyte identification including mass-spectral library search with a new method for optimizing spectra extraction, retention-index calibration from preliminary identifications, and expression-based identification checks. Processing of the first 40 min of data detected 144 analytes, 21 of which have at least one percent response, plus an additional 20 trace and/or coeluted analytes.

Original languageEnglish (US)
Article number38
JournalSeparations
Volume6
Issue number3
DOIs
StatePublished - Sep 2019

Keywords

  • Cheminformatics
  • Chemometrics
  • Comprehensive two-dimensional gas chromatography (GC×GC)

ASJC Scopus subject areas

  • Analytical Chemistry
  • Filtration and Separation

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