Component correlation between related samples by using comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry with chemometric tools

A chemometric strategy has been developed to discover component difference and similarity between two chromatograms (correlation) by using comprehensive two-dimensional (2D) gas chromatography with time-of-flight mass spectrometry (GC×GC-TOFMS). It allows for rapid determination of the presence or absence of analytes of interest in both pure and overlapping peak clusters, and then locates elution windows of target components. First, representative elution windows of analytes are extracted from the 2D GC×GC map to characterize the spectral space and further construct an orthogonal projection matrix for analysis. Next, multi-component spectral correlative chromatography (MSCC) is employed to scan the whole or pre-selected GC×GC-TOFMS data range to obtain component features. An auto-correlative projection curve is proposed to assess the projection residual from MSCC by defining a new evaluation index as reference, based on fixed-size moving window evolving factor analysis. In principle, the method can also be utilized to locate specific compounds whose known spectra are available. It is not restricted by data with high homoscedastic and heteroscedastic noise. Simulated GC-MS data and an extremely complicated herbal product mixture comprising 9 herbs demonstrates that the two-dimensional correlative distribution graph is effective for chemical interpretation between GC×GC-TOFMS data. It allows discovery of information buried in this type of highly complex dataset, especially for rapid and effective data comparison, where specific molecular identity might otherwise be hidden. ⺠A novel chemometric tool allows component correlation studies for GC×GC-TOFMS datasets. ⺠An evaluation index can semi-automatically locate buried correlation information from the auto-correlative projection curve. ⺠A 2D correlative distribution graph allows visual comparison of complex 2D separation patterns. ⺠Complete deconvolution of overlapping peak clusters is not required for fast comparison of multi-components. ⺠A complex Chinese medicinal formula comprising 9 single herbs using three extraction techniques is compared.