Using design of experiments to select optimum calibration model parameters

Abstract

A new approach to choosing the right calibration model is introduced. The basis is the well known DoE (Design of Experiments) methodology. It is shown that by identifying variables suspected to have impact on the model quality and using these as input variables in an experimental design, the significant effects and possible interactions can be determined. The chosen design has six variables: type of regression method, scaling, Box–Cox transformation, OSC pre-treatment, differentiation, and number of components. It is also shown that the approach is well suited for using more than one model evaluation criterion which is important in order to balance the fit and prediction trade-off. The feasibility of the approach is demonstrated on two different data sets. One contains visible spectra measurements of a series of metal solution standards, and the other is Raman spectra of the naphtha feed into a distillation column in a refinery. The same experimental design is used for both the laboratory and the process data in order to demonstrate the simplicity, flexibility and robustness of the proposed approach.