OSA's Digital Library

Applied Spectroscopy

Applied Spectroscopy

| PUBLISHED BY SAS — AVAILABLE FROM SAS AND OSA

  • Vol. 65, Iss. 6 — Jun. 1, 2011
  • pp: 665–677

Automated Spectral Smoothing with Spatially Adaptive Penalized Least Squares

Aaron A. Urbas and Steven J. Choquette

Applied Spectroscopy, Vol. 65, Issue 6, pp. 665-677 (2011)


View Full Text Article

Acrobat PDF (981 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations
  • Export Citation/Save Click for help

Abstract

A variety of data smoothing techniques exist to address the issue of noise in spectroscopic data. The vast majority, however, require parameter specification by a knowledgeable user, which is typically accomplished by trial and error. In most situations, optimized parameters represent a compromise between noise reduction and signal preservation. In this work, we demonstrate a nonparametric regression approach to spectral smoothing using a spatially adaptive penalized least squares (SAPLS) approach. An iterative optimization procedure is employed that permits gradual flexibility in the smooth fit when statistically significant trends based on multiscale statistics assuming white Gaussian noise are detected. With an estimate of the noise level in the spectrum the procedure is fully automatic with a specified confidence level for the statistics. Potential application to the heteroscedastic noise case is also demonstrated. Performance was assessed in simulations conducted on several synthetic spectra using traditional error measures as well as comparisons of local extrema in the resulting smoothed signals to those in the true spectra. For the simulated spectra, a best case comparison with the Savitzky–Golay smoothing via an exhaustive parameter search was performed while the SAPLS method was assessed for automated application. The application to several dissimilar experimentally obtained Raman spectra is also presented.

Virtual Issues
Vol. 6, Iss. 7 Virtual Journal for Biomedical Optics

Citation
Aaron A. Urbas and Steven J. Choquette, "Automated Spectral Smoothing with Spatially Adaptive Penalized Least Squares," Appl. Spectrosc. 65, 665-677 (2011)
http://www.opticsinfobase.org/as/abstract.cfm?URI=as-65-6-665


Sort:  Journal  |  Reset

References

References are not available for this paper.

Cited By

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited