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Least Squares Moving-Window Spectral Analysis

Published

Author(s)

Young J. Lee

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.
Citation
Applied Spectroscopy

Keywords

least-squares regression, moving-window, Savitzky-Golay, numerical differentiation, two- dimensional correlation spectroscopy

Citation

Lee, Y. (2017), Least Squares Moving-Window Spectral Analysis, Applied Spectroscopy, [online], https://doi.org/10.1177/0003702816685336 (Accessed April 14, 2024)
Created January 20, 2017, Updated November 10, 2018