Journal article

Multivariate analysis of extremely large ToFSIMS imaging datasets by a rapid PCA method

Peter J Cumpson, Naoko Sano, Ian W Fletcher, Jose F Portoles, Mariela Bravo-Sanchez, Anders J Barlow



Principal component analysis (PCA) and other multivariate analysis methods have been used increasingly to analyse and understand depth profiles in X-ray photoelectron spectroscopy (XPS), Auger electron spectroscopy (AES) and secondary ion mass spectrometry (SIMS). These methods have proved equally useful in fundamental studies as in applied work where speed of interpretation is very valuable. Until now these methods have been difficult to apply to very large datasets such as spectra associated with 2D images or 3D depth-profiles. Existing algorithms for computing PCA matrices have been either too slow or demanded more memory than is available on desktop PCs. This often forces analysts to 'bi..

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University of Melbourne Researchers


Awarded by Engineering and Physical Sciences Research Council

Funding Acknowledgements

The authors are very grateful to Dr John Fletcher for the SIMS images acquired from our samples using the Ionoptika J105 instrument in Gothenburg in the months before we took delivery of our own J105 at Newcastle. We thank Dr Graham Purvis of Newcastle University for the basalt rock sample. We thank users of the National EPSRC XPS Users' Service (NEXUS) for motivation in developing these methods for XPS applications, and EPSRC for funding the NEXUS Mid-Range Facility.