Multivariate statistical analysis of atom probe tomography data

Citations of this article
Mendeley users who have this article in their library.


The application of spectrum imaging multivariate statistical analysis methods, specifically principal component analysis (PCA), to atom probe tomography (APT) data has been investigated. The mathematical method of analysis is described and the results for two example datasets are analyzed and presented. The first dataset is from the analysis of a PM 2000 Fe-Cr-Al-Ti steel containing two different ultrafine precipitate populations. PCA properly describes the matrix and precipitate phases in a simple and intuitive manner. A second APT example is from the analysis of an irradiated reactor pressure vessel steel. Fine, nm-scale Cu-enriched precipitates having a core-shell structure were identified and qualitatively described by PCA. Advantages, disadvantages, and future prospects for implementing these data analysis methodologies for APT datasets, particularly with regard to quantitative analysis, are also discussed. © 2010 Elsevier B.V.




Parish, C. M., & Miller, M. K. (2010). Multivariate statistical analysis of atom probe tomography data. Ultramicroscopy, 110(11), 1362–1373.

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free