Hostname: page-component-7dd5485656-wxk4p Total loading time: 0 Render date: 2025-10-31T13:50:31.020Z Has data issue: false hasContentIssue false
  • English
  • Français

Materials assurance through orthogonal materials measurements: X-ray fluorescence aspects

Published online by Cambridge University Press:  20 June 2017

Mark A. Rodriguez ,
Mark H. Van Benthem ,
Donald F. Susan ,
James J. M. Griego ,
Pin Yang ,
Curtis D. Mowry  and
David G. Enos
Mark A. Rodriguez*
Affiliation:
Sandia National Laboratories, Albuquerque, New Mexico 87185-1411
Mark H. Van Benthem
Affiliation:
Sandia National Laboratories, Albuquerque, New Mexico 87185-1411
Donald F. Susan
Affiliation:
Sandia National Laboratories, Albuquerque, New Mexico 87185-1411
James J. M. Griego
Affiliation:
Sandia National Laboratories, Albuquerque, New Mexico 87185-1411
Pin Yang
Affiliation:
Sandia National Laboratories, Albuquerque, New Mexico 87185-1411
Curtis D. Mowry
Affiliation:
Sandia National Laboratories, Albuquerque, New Mexico 87185-1411
David G. Enos
Affiliation:
Sandia National Laboratories, Albuquerque, New Mexico 87185-1411
*
a) Author to whom correspondence should be addressed. Electronic mail: marodri@sandia.gov
Get access Rights & Permissions [Opens in a new window]

Abstract

X-ray fluorescence (XRF) has been employed as one of several orthogonal means of screening materials to prevent counterfeit and adulterated products from entering the product stream. We document the use of principal component analysis (PCA) of XRF data on compositionally similar and dissimilar stainless steels for the purpose of testing the feasibility of employing XRF spectra to parse and bin these alloys as the same or significantly different alloy materials. The results indicate that XRF spectra can separate and assign alloys via PCA, but that important corrections for detector drift and scaling must be performed in order to achieve valid results.

Keywords

X-ray fluorescencestainless steel alloysprincipal component analysismaterials verification

Information

Type
Technical Articles
Information
Powder Diffraction , Volume 32 , Issue 2 , June 2017 , pp. 124 - 132
Copyright
Copyright © International Centre for Diffraction Data 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Boxplot (2016). MATLAB Statistics and Machine Learning Toolbox, version. 10.2 (Computer Software), The MathWorks, Inc., Natick, MA, USA.Google Scholar
Byron, K. (2007). “FDA Expands Pet Food Recall” posted on CNN.com on April 18, 2007 at 6:32 p.m. EDT. http://www.cnn.com/2007/US/04/18/pet.food/ Google Scholar
FDA (2007). http://www.fda.gov/AnimalVeterinary/SafetyHealth/RecallsWithdrawals/ucm129575.htm.Google Scholar
Keenan, M. R. (2009). “Exploiting spatial-domain simplicity in spectral image analysis,” Surf. Interface Anal 41, 79–87.Google Scholar
Matlab (2016). MATLAB Release 2016a (Computer Software), The MathWorks, Inc., Natick, MA, USA.Google Scholar
Rodriguez, M. A., Kotula, P. G., Griego, J. M., Heath, J. E., Bauer, S. J., and Wesolowski, D. E. (2012). “Multivariate statistical analysis of micro-X-ray fluorescence spectral images,” Powder Diff 27, 108–113.CrossRefGoogle Scholar
World Health Organization –WHO (2008). “Toxicological and health aspects of melamine and cyanuric acid,” in Report of a WHO Expert Meeting in Collaboration with FAO, Supported by Health Canada, 1–4 December, 2008, ISBN 978 92 4 159795 1.Google Scholar