Dynamic system multivariate calibration based on multirate sampling data

Abstract

The statistical principal component regression (PCR) and chemometric partial least squares regression (PLSR) algorithms based on latent variables (LV) modeling are effective tools for handling ill-conditioned regression data. In many process related cases the data form time series, and it may then be possible to improve the prediction/estimation results by utilizing the autocorrelation in the observations. This can be done by use of estimators found from experimental data by use of a combination of statistical/chemometric and system identification methods. In important industrial cases, the response variables are product qualities which also in the experimental data are sampled at a low and possibly irregular rate, while the regressor variables are sampled at a higher rate. After a discussion of the options available, the paper shows how the autocorrelation of the regressor variables in such multirate sampling cases may be utilized by identification of latent variables based output error (LV + OE) estimators. An example using acoustic power spectrum regressor data is finally presented.