Comparative evaluation of preprocessing freeware on chromatography/mass spectrometry data for signature discovery

• Published in: Journal of Chromatography A 2014-09, Vol.1358, p.155-164
• Main Authors: Coble, Jamie B., Fraga, Carlos G.
• Format: Article
• Language: English
• Subjects: Analytical biochemistry: general aspects, technics, instrumentation; Analytical chemistry; Analytical, structural and metabolic biochemistry; Biological and medical sciences; Biomarkers; Chemical forensics; Chemistry; Chemometrics; Chromatographic methods and physical methods associated with chromatography; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; GC/MS; General aspects. Methods; Impurity profiling; LC/MS; Medical sciences; Metabolite profiling; Metabolomics; MetAlign; MZmine; Other chromatographic methods; SpectConnect; Toxicology; XCMS
• ISSN: 0021-9673
• DOI: 10.1016/j.chroma.2014.06.100

•Preprocessing tools used to detect impurity components in a nerve-agent precursor.•MetAlign performed the best of four tools in terms of the number of detected components.•∼15% more components were detected by combining the top two tool performers.•Extensive manual examinations needed to deal with high prevalence of false peaks. Preprocessing software, which converts large instrumental data sets into a manageable format for data analysis, is crucial for the discovery of chemical signatures in metabolomics, chemical forensics, and other signature-focused disciplines. Here, four freely available and published preprocessing tools known as MetAlign, MZmine, SpectConnect, and XCMS were evaluated for impurity profiling using nominal mass GC/MS data and accurate mass LC/MS data. Both data sets were previously collected from the analysis of replicate samples from multiple stocks of a nerve-agent precursor and method blanks. Parameters were optimized for each of the four tools for the untargeted detection, matching, and cataloging of chromatographic peaks from impurities present in the stock samples. The peak table generated by each preprocessing tool was analyzed to determine the number of impurity components detected in all replicate samples per stock and absent in the method blanks. A cumulative set of impurity components was then generated using all available peak tables and used as a reference to calculate the percent of component detections for each tool, in which 100% indicated the detection of every known component present in a stock. For the nominal mass GC/MS data, MetAlign had the most component detections followed by MZmine, SpectConnect, and XCMS with detection percentages of 83, 60, 47, and 41%, respectively. For the accurate mass LC/MS data, the order was MetAlign, XCMS, and MZmine with detection percentages of 80, 45, and 35%, respectively. SpectConnect did not function for the accurate mass LC/MS data. Larger detection percentages were obtained by combining the top performer with at least one of the other tools such as 96% by combining MetAlign with MZmine for the GC/MS data and 93% by combining MetAlign with XCMS for the LC/MS data. In terms of quantitative performance, the reported peak intensities from each tool had averaged absolute biases (relative to peak intensities obtained using instrument software) of 41, 4.4, 1.3 and 1.3% for SpectConnect, MetAlign, XCMS, and MZmine, respectively, for the GC/MS data. For the LC/MS data, the averaged abs