Rapid Screening of Complex Matrices: Utilizing Kendrick Mass Defect to Enhance Knowledge-Based Group Type Evaluation of Multidimensional Gas Chromatography-High-Resolution Time-of-Flight Mass Spectrometry Data

Benedikt A. Weggler, Beate Gruber, Frank L. Dorman

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Organic compound characterization of highly complex matrices involves scientific challenges, such as the diversity of "true" unknowns, the concentration ranges of various compound classes, and limited available amounts of sample. Therefore, discovery-based multidimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC×GC-HRToFMS) is increasingly applied. Nevertheless, most studies focus on target analysis and tend to disregard important details of the sample composition. The increased peak or separation capacity of GC×GC-ToFMS allows for in-depth chemical analysis of the molecular composition. However, high amounts of data, containing several thousands of compounds per experiment, are generally acquired during such analyses. Coupling GC×GC to high-resolution mass spectrometry further increases the amount of data and therefore requires advanced data reduction and mining techniques. Commonly, the main approach for the evaluation of GC×GC-HRToFMS data sets either focuses on the chromatographic separation (e.g., group type analysis), or utilizes exact mass data applying Kendrick mass defect analysis or van Krevelen plots. The presented approach integrates the accurate mass data and the chromatographic information by combining Kendrick mass defect information and knowledge-based rules. This combination allows for fast, visual data screening as well as quantitative estimation of the sample's composition. Moreover, the resulting sample classification significantly reduces the number of variables, allowing distinct chemometric analysis in nontargeted studies, such as detailed hydrocarbon analyses and environmental and forensic investigations.

Original languageEnglish (US)
Pages (from-to)10949-10954
Number of pages6
JournalAnalytical chemistry
Volume91
Issue number17
DOIs
StatePublished - Sep 3 2019

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry

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