Applications of Multivariate Statistical Analysis (MSA) in Microanalysis

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Recent improvements in computer hardware and software for the acquisition, storage and analysis of series of spectra and images allow for a change in strategy for quantitative microanalysis. For example, in the area of X-ray microanalysis, whereas compositional analysis and elemental distributions have been traditionally performed using point microanalysis and simple intensity mapping from a ROI, respectively, the two tasks are now routinely performed simultaneously through X-ray spectrum-imaging, where full spectra are acquired from pixels in a two-dimensional array of points on the specimen. Commercially available software now allows for the acquisition and storage of such spectrum-images, perhaps comprising as ... continued below

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1 p.

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Anderson, I.M. February 16, 1999.

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Recent improvements in computer hardware and software for the acquisition, storage and analysis of series of spectra and images allow for a change in strategy for quantitative microanalysis. For example, in the area of X-ray microanalysis, whereas compositional analysis and elemental distributions have been traditionally performed using point microanalysis and simple intensity mapping from a ROI, respectively, the two tasks are now routinely performed simultaneously through X-ray spectrum-imaging, where full spectra are acquired from pixels in a two-dimensional array of points on the specimen. Commercially available software now allows for the acquisition and storage of such spectrum-images, perhaps comprising as much as 100 MBytes of data or more. A variety of post-acquisition processing tools are provided by the developer to allow the extraction of both X-ray intensity maps, with or without rudimentary background subtraction, or full spectra from pixels of interest. In order to maximize the extraction of information from these large data sets, a number of linear and nonlinear methods are currently being explored that identify statistically significant variations among the series of spectra without a priori assumptions about the content of the data set. Among these methods, linear multivariate statistical analysis (MSA) has a number of significant advantages, including its comprehensiveness, since all spectral variations distinct from the Poisson noise level are identified, and its broad applicability to a variety of microanalytical techniques.

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1 p.

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OSTI as DE00003438

Medium: P; Size: 1 pages

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  • Proceedings of the 5th Annual Biennial Meeting of the Australian Microbeam Analysis Society (AMAS), Sydney (AU), 02/16/1999--02/19/1999

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  • Report No.: ORNL/CP-101036
  • Report No.: KC 02 01 01 0
  • Grant Number: AC05-96OR22464
  • Office of Scientific & Technical Information Report Number: 3438
  • Archival Resource Key: ark:/67531/metadc675630

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • February 16, 1999

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  • July 25, 2015, 2:20 a.m.

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  • April 6, 2017, 6:31 p.m.

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Anderson, I.M. Applications of Multivariate Statistical Analysis (MSA) in Microanalysis, article, February 16, 1999; Tennessee. (digital.library.unt.edu/ark:/67531/metadc675630/: accessed December 12, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.