[en] Characterization of complex matrices commonly implicates scientific challenges such as wide concentration ranges of various compound classes and/or the limited, available sample volume. Applying cutting-edge, discovery based separation techniques such as multidimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GCxGC-HRToF/MS) facilitate such analytical challenges. Nevertheless, the majority of studies is still focused on targeted analysis, which tend to disregard important details of the sample of interest.
GCxGC-ToF/MS provides in-depth chemical insight in the molecular fingerprint of analyzed matrices. However, such analysis produces high amounts of data generally containing several thousands of compounds per experiment. The amount of data will further increase by coupling GCxGC to high-resolution mass spectrometry (HRT), which requires advanced data reduction and mining techniques. So far, GCxGC-HRToF/MS information is evaluated by focusing either on the chromatographic separation for e.g. group type analysis, or utilizing exact mass data applying Kendrick Mass Defect (KMD) analysis or van Krevelen.
This study integrates high-resolution mass information directly into the multidimensional separation space, combining KMD data and knowledge-based rules. Combining of these approaches allows for fast, visual data screening as well as a first quantitative estimation of the samples composition. Additionally the obtained classification drastically reduces the number of variables allowing a clear and distinct chemometric analysis in e.g. environmental and forensic studies such as for detailed hydrocarbon analysis (DHA).
