Hans F. M. Boelens

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MOTIVATION Metabolomics datasets are generally large and complex. Using principal component analysis (PCA), a simplified view of the variation in the data is obtained. The PCA model can be interpreted and the processes underlying the variation in the data can be analysed. In metabolomics, often a priori information is present about the data. Various forms(More)
This study illustrates how retention models can be used to accurately predict the retention behaviour of polydisperse macromolecules in LC separations. It highlights that the number of experiments required can be drastically reduced when the relationship between the model parameters and molecular structure parameters (e.g. molar mass) can be incorporated(More)
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