A recursive spline-based algorithm for spectrophotometric data correction

The authors present a method of spectrometric data correction based on the smoothing approximation of the undistorted spectrum as it might be recorded by a perfectly resolving spectrophotometer with the cubic spline whose parameters are determined using the Kalman filter, under a nonnegativity constraint reflecting a-priori knowledge of the real spectra. There are three main advantages of this approach which are significant: flexibility of the spline approximation; computational simplicity of the Kalman filter, and easy incorporation of the nonnegativity constraint. The proposed method of correction is developed. The essential results of the study obtained using synthetic data are presented. Some real-data-based experiments are reported