Abstract :
[en] Characterization of highly complex matrices implicates scientific challenges such as wide presence of “true” unknowns, concentration ranges of various compound classes and limited, available amounts of sample. Cutting-edge, discovery based separation techniques such as multidimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GCxGC-HRToF/MS), are commonly applied to such analytical challenges. Nevertheless, most studies focus on target analysis and tend to disregard important details of the sample composition.
The high separation capacity of GCxGC-ToF/MS 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 analyzes. Coupling GCxGC to high-resolution mass spectrometry (HRMS) further increases the amount of data and therefore requires advanced data reduction and mining techniques. Commonly, the main approach for the evaluation of dense data sets either focuses on the chromatographic separation for e.g. group type analysis, or utilizes exact mass data applying Kendrick Mass Defect (KMD) analysis or van Krevelen plots.
The presented approach integrated the accurate mass data into the chromatographic information by combining KMD information and knowledge-based rules. This combination allows for fast, visual data screening as well as first quantitative estimation of the sample's composition. Moreover, the resulted sample classification significantly reduces the number of variables, allowing distinct chemometric analysis in non-targeted studies such as detailed hydrocarbon analysis (DHA), environmental and forensic investigations.
