Kalman filtering approaches for solving problems in analytical chemistry

The application of the Kalman filter to the solution of a variety of problems in analytical chemistry is reviewed. Five examples are selected from the literature to illustrate the use of Kalman filtering techniques for obtaining least‐squares estimates fo several parameters of analytical importance. These examples include multicomponent curve resolution and concentration estimation, correction for variable background responses, calibration with drift compensation, and estimation of kinetic parameters for first‐order reactions and for heterogeneous charge‐transfer reactions. An adaptive Kalman filtering technique is required for the solution of the background correction problem, and the extended Kalman filter algorithm is required for the solution of the nonlinear kinetic problems. For each case, the results that were obtained are summarized, and some advantages of Kalman filtering over traditional least‐squares approaches are discussed.

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