Fourier Deconvolution in Non-self-deconvolving Conditions. Effective Narrowing, Signal-to-Noise Degradation, and Curve Fitting

Authors: Lórenz-Fonfría, Víctor A.; Villaverde, Joaquim; Padrós, Esteve

Source: Applied Spectroscopy, Volume 56, Issue 2, Pages 50A-69A and 145-274 (February 2002) , pp. 232-242(11)

Publisher: Society for Applied Spectroscopy

Buy & download fulltext article:

OR

Price: $29.00 plus tax (Refund Policy)

Abstract:

The effect of Fourier deconvolution on band narrowing and on the decrease of signal-to-noise ratio has been studied for a generalized case in which the width used in the deconvolution does not match the actual bandwidth. For the identification of underlying component bands, our results show that application of infra-deconvolution (i.e., the bandwidth used for deconvolution is lower than the actual bandwidth) produces a high degradation of the signal-to-noise ratio and poor band narrowing. On the contrary, self- or over-deconvolution, with lower signal-to-noise degradation and higher band narrowing, seem more suitable for this purpose. Relative to quantitative analysis, we rely on both theoretical and practical aspects to propose the generalized use of Voigt band shapes as a fairly correct general model to be used in the curve fitting of deconvoluted bands. With its use, the curve fitting of noise-free deconvoluted bands retrieved the original band parameters with high accuracy. The noise effect on the parameter precision obtained by curve fitting a deconvoluted noisy Lorentzian band was also studied. Finally, the existence of optimum deconvolution parameters for curve fitting complex spectra is considered, and a general recommendation for approaching this optimum is given.
More about this publication?
Related content

Tools

Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content

Text size:

A | A | A | A
Share this item with others: These icons link to social bookmarking sites where readers can share and discover new web pages. print icon Print this page