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PUBLICATIONS

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
Pub Type
Journals

Download Paper

https://doi.org/10.1177/0003702816685336

Keywords

least-squares regression, moving-window, Savitzky-Golay, numerical differentiation, two- dimensional correlation spectroscopy
Analytical chemistry and Spectroscopy

Citation

Lee, Y. (2017), Least Squares Moving-Window Spectral Analysis, Applied Spectroscopy, [online], https://doi.org/10.1177/0003702816685336 (Accessed October 8, 2025)

Issues

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Created January 20, 2017, Updated November 10, 2018
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