Peak clustering in two-dimensional gas chromatography with mass spectrometric detection based on theoretical calculation of two-dimensional peak shapes: The 2DAid approach

A method is presented to facilitate the non-target analysis of data obtained in temperature-programmed comprehensive two-dimensional (2D) gas chromatography coupled to time-of-flight mass spectrometry (GC × GC–ToF-MS). One main difficulty of GC × GC data analysis is that each peak is usually modulated several times and therefore appears as a series of peaks (or peaklets) in the one-dimensionally recorded data. The proposed method, 2DAid, uses basic chromatographic laws to calculate the theoretical shape of a 2D peak (a cluster of peaklets originating from the same analyte) in order to define the area in which the peaklets of each individual compound can be expected to show up. Based on analyte-identity information obtained by means of mass spectral library searching, the individual peaklets are then combined into a single 2D peak. The method is applied, amongst others, to a complex mixture containing 362 analytes. It is demonstrated that the 2D peak shapes can be accurately predicted and that clustering and further processing can reduce the final peak list to a manageable size. ⺠Generally applicable data processing methods for two-dimensional gas chromatography data (GC × GC–MS) can still be improved or extended. ⺠This study describes a method to cluster the sub-peaks (peaklets) that comprise a two-dimensional peak to a single peak in order to reduce the data analysis results to a manageable size. ⺠The method was demonstrated using a complex mixture containing 362 analytes, and was shown to fit its purpose.