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Proceedings Paper

Removal of nonresonant background in MCARS spectra using Fourier filtering
Author(s): Stephen D. Roberson; Paul M. Pellegrino
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Paper Abstract

Multiplex coherent anti-Stokes Raman spectroscopy (MCARS) has been used to create a complete Raman spectrum of a material of interest in milliseconds. However, these MCARS spectra often embedded in a nonresonant background that reduces the ability to use those spectra to positively identify the material of interest. There are a number of techniques that are used experimentally to reduce the nonresonant background when taking the MCARS spectrum. However, there are situations where these experimental nonresonant background reduction techniques may result in a loss of the desired MCARS signal. In an effort to maintain the signal strength of the MCARS spectrum, analytical methods of background removal are employed. There are a number of analytical techniques for nonresonant background removal from MCARS signals. However, many of them either make blanket assumptions about the nonresonant background that sacrifice accuracy of the technique or require knowledge of the material of interest before removing the nonresonant background. We will be reporting on an analytical method to remove the nonresonant background that utilizes a combination of the maximum entropy method to reproduce the spectrum as well as complex spectral filtering to remove the nonresonant background and accurately determine the CARS spectrum interest without prior knowledge of the material of interest.

Paper Details

Date Published: 2 June 2015
PDF: 9 pages
Proc. SPIE 9455, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XVI, 94550H (2 June 2015); doi: 10.1117/12.2181043
Show Author Affiliations
Stephen D. Roberson, U.S. Army Research Lab. (United States)
Paul M. Pellegrino, U.S. Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 9455:
Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XVI
Augustus Way Fountain III, Editor(s)

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