Erik J Nilsson | PubMed Health

PubMed2004

Algorithm for locating analytes of interest based on mass spectral similarity in GC x GC-TOF-MS data: analysis of metabolites in human infant urine.

The developed algorithm reported herein, referred to as "DotMap," addresses the need to rapidly identify analyte peak locations in gas chromatography x gas chromatography-time of flight mass spectrometry (GC x GC-TOF-MS) data. The third-order structure of GC x GC-TOF-MS data is such that at each point in the GC x GC chromatogram, a complete mass spectrum is measured. DotMap utilizes this third-order structure to search for the location of a given spectrum of interest in a complete data set, or in a user selected portion of the complete data set. The algorithm returns a contour plot indicating the location of signal(s) with the most similar mass spectra to the analyte of interest. A spectrum from the region indicated is then subjected to an automated mass spectral search to give immediate feedback on the accuracy of the analysis. This algorithm was investigated with a trimethylsilyl (TMS) derivatized human infant urine sample that contained organic acid metabolites. One hundred percent of 12 selected TMS derivatized organic acid metabolites in human infant urine were located with the DotMap algorithm. A typical automated DotMap analysis takes 30 s on a 1.6 GHz PC with 1024 MB of RAM. Vanillic acid (TMS) was located by DotMap, but also exhibited overlap with other organic acids. The presence of vanillic acid (TMS) was confirmed by subjecting the appropriate GC x GC region to chemometric signal deconvolution by PARAFAC to yield pure component information suitable for subsequent quantification.

Algorithms↗

PubMed2004

Multivariate selectivity as a metric for evaluating comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry subjected to chemometric peak deconvolution.

Two-dimensional gas chromatography (GC x GC) coupled to time-of-flight mass spectrometry (TOFMS) [GC x GC-TOFMS)] is a highly selective technique well suited to analyzing complex mixtures. The data generated is information-rich, making it applicable to multivariate quantitative analysis and pattern recognition. One separation on a GC x GC-TOFMS provides retention times on two chromatographic columns and a complete mass spectrum for each component within the mixture. In this report, we demonstrate how GC x GC-TOFMS combined with trilinear chemometric techniques, specifically parallel factor analysis (PARAFAC) initiated by trilinear decomposition (TLD), results in a powerful analytical methodology for multivariate deconvolution. Using PARAFAC, partially resolved components in complex mixtures can be deconvoluted and identified without requiring a standard data set, signal shape assumptions or any fully selective mass signals. A set of four isomers (iso-butyl, sec-butyl, tert-butyl, and n-butyl benzenes) is used to investigate the practical limitations of PARAFAC for the deconvolution of isomers at varying degrees of chromatographic resolution and mass spectral selectivity. In this report, multivariate selectivity was tested as a metric for evaluating GC x GC-TOFMS data that is subjected to PARAFAC peak deconvolution. It was found that deconvolution results were best with multivariate selectivities over 0.18. Furthermore, the application of GC x GC-TOFMS followed by TLD/PARAFAC is demonstrated for a plant metabolite sample. A region of GC x GC-TOFMS data from a complex natural sample of a derivatized metabolic plant extract from Huilmo (Sisyrinchium striatum) was analyzed using TLD/PARAFAC, demonstrating the utility of this analytical technique on a natural sample containing overlapped analytes without selective ions or peak shape assumptions.

Evaluation Studies as Topic↗