On Parameter Redundancy in Curve Fitting of Kinetic Data

Parameter redundancy arises when the proposed kinetic model is too detailed for the actual information content of the measurable data. Then an enormous set of totally different, but interdependent parameter values is able to explain the data, and parameter estimation becomes impossible. The paper shows that the defect may be studied before experiments are available. Numerical criteria are outlined which can be used to estimate redundancy and redundant parameter combinations, to predict standard deviations of estimated parameters, to optimize the experimental design for reducing redundancy.