Analysis of partially resolved liquid chromatographic peaks using dynamic modeling with the kalman filter

Establishing a calibration model is an important part of any mathematical method for multi-component determination. Use of a calibration model based on single spectra is subject to error, because the model spectrum chosen may not be representative of the response over the full range of the calibration. Alternative calibration models require more time to establish calibration, an these may not be convenient for real-time determinations. A novel calibration method is reported for use with Kalman filters. The method, dynamic modeling, is based on the use of libraries of calibration spectra. The set of used to describe the model at any time is based on component concentrations, estimated for the multi-component mixture, as determined from the Kalman filter, so that several spectra can be used to best describe a varying response. Through application of the dynamic modelingt to simulated and real chromatograms, it is demonstrated that use of the method decreases estimation errors cause by model data mismatches, and that full benefit can be obtained from relatively small libraries.