Improving the interpretation of multivariate and rule induction models by using a peak parameter representation

This paper demonstrates that the interpretation of multivariate calibration and rule induction classification models can be significantly improved by adopting a new representation of data profiles (e.g., spectra and chromatograms) containing identifiable peaks. The new representation is based on estimating Gaussian or Lorentzian curve parameters of data profiles by non-linear curve fitting. All modelling is performed on these peak parameters rather than using the traditional approach where each variable is assigned a sampling point in the data profile. Loading weight plots from the multivariate methods and decision trees obtained from rule induction algorithms become more parsimonious and easier to interpret in terms of the new representation.