Dynamic system multivariate calibration

In the first part of the paper, the optimal estimator for normally nonmeasu red primary outputs from a linear, time invariant and stable dynamic system is developed. The optimal estimator is based on all available information in known inputs and measured secondary outputs. Assuming sufficient experim ental data, the optimal estimator can be identified by standard system iden tification (SI) methods, utilizing an output error (OE) model. It is then s hown that least squares estimation (LSE) and multivariate calibration by me ans of principal component regression (PCR) or partial least squares regres sion (PLSR) can be seen as special static cases of such a dynamic SI. Final ly, it is shown that dynamic system PCR and PLSR solutions can be developed as special cases of the general estimator for dynamic systems. (C) 1998 El sevier Science B.V. All rights reserved.