2D autocovariance function for comprehensive analysis of two-way GC–MS data matrix: Application to environmental samples

This paper describes a signal processing method for comprehensive analysis of the large data set generated by hyphenated GC–MS technique. It is based on the study of the 2D autocovariance function (2D-EACVF) computed on the raw GC–MS data matrix, extending the procedure previously developed for 1D to 2D signals. It appears specifically promising for GC–MS investigation, in particular to single out ordered patterns in complex data: such patterns can be simply identified by visual inspection from deterministic peaks in the 2D-EACVF plot. of order along the retention time axis (x = tR) is represented by a horizontal sequence of peaks, located at the same interdistance ΔtR = bx, e.g., bx is the CH2 retention time increment between subsequent terms of an homologous series. The order along the fragment mass axis (y = m/z) contains information on analyte fragmentation patterns. Deterministic peaks appear in the 2D-EACVF plot at Δm/z values corresponding to the most abundant ion fragments – dominating fragments in MS spectrum – or to ions generated by repetitive loss of the same ion fragment, i.e., Δm/z = 14 amu produced by the [CH2] group loss in n-alkanes. applicability was tested by processing GC–MS data of organic extracts of atmospheric aerosol samples: attention is focused on identifying and characterizing homologous series of organics, i.e., n-alkanes and n-alkanoic acids, since they are considered molecular tracers able to track the origin and fate of different organics in the environment.