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Two different algorithms for time-alignment as a preprocessing step in linear factor models are studied. Correlation optimized warping and dynamic time warping are both presented in the literature as methods that can eliminate shift-related artifacts from measurements by correcting a sample vector towards a reference. In this study both the theoretical properties and the practical implications of using signal warping as preprocessing for chromatographic data are investigated. The connection between the two algorithms is also discussed. The findings are illustrated by means of a case study of principal component analysis on a real data set, including manifest retention time artifacts, of extracts from coffee samples stored under different packaging conditions for varying storage times. We concluded that for the data presented here dynamic time warping with rigid slope constraints and correlation optimized warping are superior to unconstrained dynamic time warping; both considerably simplify interpretation of the factor model results. Unconstrained dynamic time warping was found to be too flexible for this chromatographic data set, resulting in an overcompensation of the observed shifts and suggesting the unsuitability of this preprocessing method for this type of signals.

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This paper focuses on the practical aspects and implications of preprocessing chromatographic data to correct for undesirable time-shifts. An approach to automate the alignment of chromatographic data based on peak alignment or warping is proposed. This approach deals with selection of the required parameters including selection of reference sample to warp towards, and chooses warping settings based on a new evaluation criterion for goodness of correction. The new criterion aims at quantifying goodness of alignment while at the same time penalising significant shape or areachanges in the warped peaks. The entire selection procedure is automated using a discretecoordinates simplex-like optimisation routine. Examples with simulated chromatographic data, GC-FID and HPLC-Fluorescence measurement series illustrate the potential of using this automated alignment tool.

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