A Data-Challenge Case Study of Analyte Detection and Identification with Comprehensive Two-Dimensional Gas Chromatography with Mass Spectrometry (GC-GC-MS)

Authors

Stephen E. Reichenbach, University of Nebraska - LincolnFollow
Qingping Tao, GC Image, LLC
Chiara Cordero, Università degli Studi di Torino
Carlo Bicchi, Università degli Studi di Torino

ORCID IDs

https://orcid.org/0000-0001-8615-0795

https://orcid.org/0000-0003-3201-0775

Document Type

Article

Date of this Version

8-2-2019

Citation

2019 by the authors.

Comments

Separations 2019, 6, 38; doi:10.3390/separations6030038 www.mdpi.com/journal/separations

Abstract

This case study describes data analysis of a chromatogram distributed for the 2019 GC-GC Data Challenge for the Tenth Multidimensional ChromatographyWorkshop (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.