Mathematical deconvolution methods can separate co-eluting peaks in samples for which (chromatographic) separation fail. However, these methods often heavily rely on manual user-input and interpretation. This is not only time-consuming but also error-prone and automation is needed if such methods are to be applied in a routine manner. One major hurdle when automating deconvolution methods is the selection of the correct number of components used for building the model. We propose a new method for the automatic determination of the optimum number of components when applying multivariate curve resolution (MCR) to comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC-MS) data. It is based on a two-fold cross-validation scheme. The obtained overall cross-validation error decreases when adding components and increases again once over-fitting of the data starts to occur. The turning point indicates that the optimum number of components has been reached. Overall, the method is at least as good as and sometimes superior to the inspection of the eigenvalues when performing singular-value decomposition. However, its strong point is that it can be fully automated and it is thus more efficient and less prone to subjective interpretation. The developed method has been applied to two different-sized regions in a GC×GC-MS chromatogram. In both regions, the cross-validation scheme resulted in selecting the correct number of components for applying MCR. The pure concentration and mass spectral profiles obtained can then be used for identification and/or quantification of the compounds. While the method has been developed for applying MCR to GC×GC-MS data, a transfer to other deconvolution methods and other analytical systems should only require minor modifications.
Keywords: Cross-validation; Deconvolution; Multivariate curve resolution; Two-dimensional chromatography.
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