Applications of Multivariate Statistical Analysis (MSA) in Microanalysis
- ORNL
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.
- Research Organization:
- Oak Ridge National Lab., TN (US)
- Sponsoring Organization:
- USDOE Office of Energy Research (ER) (US)
- DOE Contract Number:
- AC05-96OR22464
- OSTI ID:
- 3438
- Report Number(s):
- ORNL/CP-101036; KC 02 01 01 0; KC 02 01 01 0
- Country of Publication:
- United States
- Language:
- English
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