UV chromatographic data in combination with multivariate data analysis (MVDA) has been extensively used for bioprocess monitoring. However, they are usually attributed to shifts along the retention time and require preprocessing. Misaligned UV chromatographic data result in inconsistent MVDA models. Numerous preprocessing techniques are available, each varying in the number of meta-parameters to optimize, complexity and computational time. Therefore, we aimed at developing a generic workflow to screen for preprocessing techniques. We chose four datasets with increasing complexity containing UV chromatographic data from reverse-phase and size exclusion chromatography HPLC. We aligned all four datasets using three preprocessing techniques, namely icoshift, PAFFT and RAFFT algorithms. We chose several statistical tools to validate the performance of the preprocessing techniques and to screen for meta-parameters. We validated the performance of the preprocessing techniques in terms of data preservation, complexity and computational time, and identified the optimal ranges of meta-parameters for each dataset. Finally, we established principal component analysis (PCA) models to evaluate the chosen alignment technique. Summarizing, in this study a generic workflow has been developed to validate alignment of chromatographic data using statistical tools.
Published Date: 2019-01-02; Received Date: 2018-12-03