Methods for spectral image analysis by exploiting spatial simplicity
Abstract
Several fullspectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squaresbased Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights. For many cases of practical importance, imaged samples are "simple" in the sense that they consist of relatively discrete chemical phases. That is, at any given location, only one or a few of the chemical species comprising the entire sample have nonzero concentrations. The methods of spectral image analysis of the present invention exploit this simplicity in the spatial domain to make the resulting factor models more realistic. Therefore, more physically accurate and interpretable spectral andmore »
 Inventors:

 (Albuquerque, NM)
 Issue Date:
 Research Org.:
 Sandia Corporation (Albuquerque, NM)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1014729
 Patent Number(s):
 7,840,626
 Application Number:
 US Patent Application 12/702,934
 Assignee:
 Sandia Corporation (Albuquerque, NM) NNSASC
 DOE Contract Number:
 AC0494AL85000
 Resource Type:
 Patent
 Country of Publication:
 United States
 Language:
 English
Citation Formats
Keenan, Michael R. Methods for spectral image analysis by exploiting spatial simplicity. United States: N. p., 2010.
Web.
Keenan, Michael R. Methods for spectral image analysis by exploiting spatial simplicity. United States.
Keenan, Michael R. Tue .
"Methods for spectral image analysis by exploiting spatial simplicity". United States. https://www.osti.gov/servlets/purl/1014729.
@article{osti_1014729,
title = {Methods for spectral image analysis by exploiting spatial simplicity},
author = {Keenan, Michael R.},
abstractNote = {Several fullspectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squaresbased Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights. For many cases of practical importance, imaged samples are "simple" in the sense that they consist of relatively discrete chemical phases. That is, at any given location, only one or a few of the chemical species comprising the entire sample have nonzero concentrations. The methods of spectral image analysis of the present invention exploit this simplicity in the spatial domain to make the resulting factor models more realistic. Therefore, more physically accurate and interpretable spectral and abundance components can be extracted from spectral images that have spatially simple structure.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2010},
month = {11}
}