Metabolic fingerprinting of biofluids like urine is a useful technique for detecting differences between individuals. With this approach, it might be possible to classify samples according to their biological relevance. In Part I of this work a method for the comprehensive screening of metabolites was described [H. Idborg, L. Zamani, P-O. Edlund, I. Schuppe-Koistinen, S.P. Jacobsson, Part 1, J. Chromatogr. B 828 (2005) 9], using two different liquid chromatography (LC) column set-ups and detection by electrospray ionization mass spectrometry (ESI-MS). Data pretreatment of the resulting data described in [H. Idborg, L. Zamani, P-O. Edlund, 1. Schuppe-Koistinen, S.P. Jacobsson, Part 1, J. Chromatogr. B 828 (2005) 9] is needed to reduce the complexity of the data and to obtain useful metabolic fingerprints. Three different approaches, i.e., reduced dimensionality (RD), MarkerLynx (TM), and MS Resolver (TM), were compared for the extraction of information. The pretreated data were then subjected to multivariate data analysis by partial least squares discriminant analysis (PLS-DA) for classification. By combining two different chromatographic procedures and data analysis, the detection of metabolites was enhanced as well as the finding of metabolic fingerprints that govern classification. Additional potential biomarkers or xenobiotic metabolites were detected in the fraction containing highly polar compounds that are normally discarded when using reversed-phase liquid chromatography.
2005. Vol. 828, no 1-2, 14-20 p.