title: Least Squares Moving-Window Spectral Analysis.
abstract: Least squares regression is proposed as a moving-windows method for analysis of a series of spectra acquired as a function of external perturbation. The least squares moving-window (LSMW) method can be considered an extended form of the Savitzky-Golay differentiation for nonuniform perturbation spacing. LSMW is characterized in terms of moving-window size, perturbation spacing type, and intensity noise. Simulation results from LSMW are compared with results from other numerical differentiation methods, such as single-interval differentiation, autocorrelation moving-window, and perturbation correlation moving-window methods. It is demonstrated that this simple LSMW method can be useful for quantitative analysis of nonuniformly spaced spectral data with high frequency noise.
authors.affiliation: Biosystems & Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD, USA.
authors.forename: Young Jong
authors.initials: YJ
authors.lastname: Lee
keywords: Least squares moving-window regression; Savitzky–Golay numerical differentiation; two-dimensional correlation spectroscopy