Lee, Y.
(2017),
Least Squares Moving-Window Spectral Analysis, Applied Spectroscopy, [online], https://doi.org/10.1177/0003702816685336
(Accessed January 17, 2026)
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Least Squares Moving-Window Spectral Analysis
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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 SavitzkyGolay 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
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Journals
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Keywords
least-squares regression, moving-window, Savitzky-Golay, numerical differentiation, two- dimensional correlation spectroscopy
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Created January 20, 2017, Updated November 10, 2018