Solving chromatographic challenges in comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry using multivariate curve resolution–alternating least squares

Multivariate curve resolution–alternating least squares (MCR–ALS) analysis is proposed to solve chromatographic challenges during two-dimensional gas chromatography–time-of-flight mass spectrometry (GC × GC–TOFMS) analysis of complex samples, such as crude oil extract. In view of the fact that the MCR–ALS method is based on the fulfillment of the bilinear model assumption, three-way and four-way GC × GC–TOFMS data are preferably arranged in a column-wise superaugmented data matrix in which mass-to-charge ratios (m/z) are in its columns and the elution times in the second and first chromatographic columns are in its rows. Since m/z values are common for all measured spectra in all second-column modulations, unavoidable chromatographic challenges such as retention time shifts within and between GC × GC–TOFMS experiments are properly handled. In addition, baseline/background contributions can be modeled by adding extra components to the MCR–ALS model. Another outstanding aspect of MCR–ALS analysis is its extreme flexibility to consider all samples (standards, unknowns, and replicates) in a single superaugmented data matrix, allowing joint analysis. In this way, resolution, identification, and quantification results can be simultaneously obtained in a very fast and reliable way. The potential of MCR–ALS analysis is demonstrated in GC × GC–TOFMS analysis of a North Sea crude oil extract sample with relative errors in estimated concentrations of target compounds below 6.0 % and relative standard deviations lower than 7.0 %. The results obtained, along with reasonable values for the lack of fit of the MCR–ALS model and high values of the reversed match factor in mass spectra similarity searches, confirm the reliability of the proposed strategy for GC × GC–TOFMS data analysis.