Correction for drift in multivariate systems using the Kalman filter

Many analytical instruments can now be used effectively to monitor industrial and environmental processes on-line. In the large majority of cases, however, the instrument must be first calibrated, and then recalibrated if the instrument characteristics change with time. Here an approach for detection and correction of time-dependent drift in multivariate calibration parameters is described. Kalman filters can be devised that correct for drift in the baseline and drift in sensitivities, either through purely random processes, or a combination of linear and random drift. Some examples of implementations of this filter are demonstrated using synthetic, near-infrared, and UV-visible spectrophotometric data. The application of the Kalman filter to the inverse calibration problem is also demonstrated.