Parallel Kalman filters for peak purity analysis: extensions to non-ideal detector response

A mathematical method for detecting spectral impurities in liquid chromatographic peaks measured with a diode array detector (LC-DAD) is described. The new method compensates for certain measurement non-idealities, minimizing the likelihood of a false indication of an impurity. The algorithm, evolving projection analysis, is based on a previously reported method in which points in an n-dimensional absorbance space (n = number of wavelengths) are projected onto two-dimensional subspaces and modeled using the Kalman filter. Model deviations can be indicative of the presence of spectral impurities, or may result from measurement artifacts such as heteroscedastic noise or non-linear detector response. The modified algorithm incorporates model variants that mask the effects of heteroscedastic noise and non-linearities so that impurities can be detected even in the presence of these artifacts. A noise model is developed for the DAD and simulations are used to evaluate the modified algorithm. Simulations are validated through the use of experimental data. It is demonstrated that, except in cases where the effects of non-linearities become severe, the modified model successfully compensates for these measurement non-idealities.