Data preprocessing by wavelets and genetic algorithms for enhanced multivariate analysis of LC peptide mapping

Peptide mapping by means of liquid chromatography is a powerful technique used for the characterisation and analysis of the primary structure of proteins. Subtle changes in the covalent structure of the protein can be detected by means of the chromatographic profile (fingerprint). Chromatographic methods, however, display variations in the chromatographic profile even at identical instrumental settings and sample conditions. These variations may be due to changes of the chromatographic conditions, e.g. slight shifts in column temperature, and degradation or alterations of the stationary phase or small changes in the trifluoroacetic acid (TFA) concentration. Such variations may result in varying retention times and peak shapes of the analytes and differences in the chromatographic baseline, thereby having a detrimental impact on the results obtained on multivariate analysis of peptide maps. In order to reduce the non-sample-related variations and to be able to more fully extract the information in peptide mapping, approaches for achieving this objective are outlined in the present study. These methods are denoising and data compression of the chromatograms by wavelets, baseline corrections by linear interpolation, and peak shift alignments towards a target chromatogram by means of a genetic algorithm. Visual inspections of preprocessed chromatograms and principal component analysis (PCA) score plots demonstrate the efficiency of the methodology used. Furthermore, deliberately added changes. e.g. insertions of small Gaussian peaks (outliers), are more easily detected by the proposed methods than from the original chromatograms by multivariate analysis. (C) 2003 Elsevier B.V. All rights reserved.