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Multivariate Statistical Analysis of Spectrum Lines and Images

Published online by Cambridge University Press:  02 July 2020

Ian M. Anderson  and
Jim Bentley
Ian M. Anderson
Affiliation:
Metals & Ceramics Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN, 37831
Jim Bentley
Affiliation:
Metals & Ceramics Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN, 37831
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Extract

Recent developments in instrumentation and computing power have greatly improved the potential for quantitative imaging and analysis. For example, products are now commercially available that allow the practical acquisition of spectrum images, where an EELS or EDS spectrum can be acquired from a sequence of positions on the specimen. However, such data files typically contain megabytes of information and may be difficult to manipulate and analyze conveniently or systematically. A number of techniques are being explored for the purpose of analyzing these large data sets. Multivariate statistical analysis (MSA) provides a method for analyzing the raw data set as a whole. The basis of the MSA method has been outlined by Trebbia and Bonnet.

MSA has a number of strengths relative to other methods of analysis. First, it is broadly applicable to any series of spectra or images. Applications include characterization of grain boundary segregation (position-), of channeling-enhanced microanalysis (orientation-), or of beam damage (time-variation of spectra).

Type
Quantitative Analysis For Series of Spectra and Images: Getting The Most From Your Experimental Data
Information
Microscopy and Microanalysis , Volume 3 , Issue S2: Proceedings: Microscopy & Microanalysis '97, Microscopy Society of America 55th Annual Meeting, Microbeam Analysis Society 31st Annual Meeting, Histochemical Society 48th Annual Meeting, Cleveland, Ohio, August 10-14, 1997 , August 1997 , pp. 931 - 932
Copyright
Copyright © Microscopy Society of America 1997

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References

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Research at the Oak Ridge National Laboratory (ORNL) SHaRE User Facility was sponsored by the Division of Materials Sciences, U.S. Department of Energy, under contract DE-AC05- 96OR22464 with Lockheed Martin Energy Research Corp., and by an appointment (IMA) to the ORNL Postdoctoral Research Associates Program, which is administered jointly by the Oak Ridge Institute for Science and Education and ORNLGoogle Scholar