Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-23T15:25:14.342Z Has data issue: false hasContentIssue false
  • English
  • Français

Automated Quantitative XRF Analysis of Soda-Lime Glass Utilizing Pattern Recognition

Published online by Cambridge University Press:  06 March 2019

A. J. Klimasara  and
T. L. Barry
A. J. Klimasara
Affiliation:
GTE Electrical Products, Sylvania Lighting Center, Danvers, MA 01923
T. L. Barry
Affiliation:
GTE Electrical Products, Sylvania Lighting Center, Danvers, MA 01923
Get access

Abstract

A set of programs in Fortran 77 has been written to automate quantitative analysis on the Rigaku SMAX/PDP-11/73 XRF System. The spectrometer can be instructed and Trained to recognize spectral patterns of soda-lime glass or other materials.

The method of pattern recognition employed is based on the relative position of spectra-vectors in N-Dimension Euclidean feature space. The distance between 2 vector-points: is used as a classifier.

The software correlates between 2 Euclidean feature spaces:

  • - Spectra Feature-Space

  • - Chemistry Feature-Space

Quantitative results are extracted automatically and printed in laboratory report form. Mathematical modeling with associated software and the results obtained are presented.

Type
V. XRF Applications; Fuels and Lubricants, Metals and Alloys, Geological, Heavy Element, Other
Information
Advances in X-Ray Analysis , Volume 30: Thirty-Fifth Annual Conference on Applications of X-ray Analysis, August 4-8, 1986 , 1986 , pp. 325 - 332
Copyright
Copyright © International Centre for Diffraction Data 1986

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.)

References

1. Klimasara, A.J., “Automated Identification of Alloys Analyzed by X-ray Energy Dispersive Method”, General Electric TIS. /R80AEG015, February 4, 1980, GE. lass 2.Google Scholar
2. Klimasara, A.J., “Computer Programming Automates Alloy Identification”, General Electric Publication: CADMAT. lectronics Hews, Issue 6, October 1, 1980, GE. lass 2.Google Scholar
3. Klimasara, A.J., Kaufman, M., “Automated SEARCH/MATCH/INTERPOLAXE. ethod for Far Quantitative Analysis of X-ray Spectra”, General Electric TIS. num;R81AEG015, February 10, 1981, GE. lass 2.Google Scholar
4. Klimasara, A.J., Kaufman, M., “Rapid Quantitative Analysis of X-ray Line Scans by Automated SMI. ethod”, General Electric TIS. /R81AEG024, March 23, 1981, GE. lass 2.Google Scholar
5. Klimasara, A.J., Kaufman, M., “Alloy Chemistry Search/identification on the EDX. omputer”, General Electric TIS. /R82AEB048, April 21, 1982, GE. lass 2.Google Scholar
6. Klimasara, A.J., Kaufman, M., “X-ray Analysis of Aircraft Coatings by Automated ESMI. ethod”, General Electric TIS. num;R83AEB039, June 23, 1983, GE. lass 2.Google Scholar
7. Kaufman, M., Klimasara, A.J. “Chemical Composition of Alloys in V-SEM. EDX. omputer File”, General Electric TM. /83AEB1141, September 22, 1983, GE. lass 1.Google Scholar
8. Klimasara, A.J., “Automated Identification of Alloys with the X-ray Energy Dispersive Method”, General Electric AEBG. The Leading Edge, Cincinnati? Ohio, Summer 1984.Google Scholar
9. Russ, J.C., “Processing of Energy Dispersive X-ray Spectra”. Vol. 20, Advances in X-ray Analysis, Plenum Publ. Corp., NY. 1977).Google Scholar
10. Russ, J.C., Jenkins, R., Shen, R.B., Sanborg, A.O., “Verification of Stability and Precision for Energy Dispersive XRF. ystems”, Vol. 21, Advances in X-ray Analysis, Plenum Publ- Corp. NY. 1978).Google Scholar
11. Wegscheider, W., Leyden, D.E., “’P. REDS1, an interactive on - line system for the interpretation of EDXRF. ata”. Vol. 22, Advances in X-ray Analysis, Plenum Publ. Corp., NY. 1979).Google Scholar
12. KEVEX. ORP., “Kevex Analyst 6600”, abuyer's guide to Kevex Spectrometry Systems, July 1979.Google Scholar
13. Clerc, J.T., “Automated Spectral Interpretation Methods”, Univ. of Berne, Switzerland, First Symposium on Pattern Recognition Methods in Analytical Spectroscopy, Snowbird, Utah, June 16-18, 1986. (in press)Google Scholar
14. Ballard, D.H., Brown, C.M., “Computer Vision”, Prentice-Hall, Inc. Englewood, Cliffs, NJ. 982 15.Google Scholar
15. Devijver, P.A., Kittler, J., “Pattern Recognition - A Statistical Approach”, Prentice-Hall International, I. c., London, 1982.Google Scholar
16. King, S.F., “Syntactic Pattern Recognition and Applications”, Prentice-Hall, Inc. Englewood, Cliffs, NJ. 1982.Google Scholar
17. Kovalesky, V.A., “Image Pattern Recognition”, Springer-Verlag NewYork Inc., 1980.Google Scholar
15. Sklansky, J., Wassel, G.N., “Pattern Classifiers and Trainable Machines”, Springer-Verlag NewYork Inc. 1981 Google Scholar
19. Watanabe, S., “Pattern Recognition - Human and Mechanical”, John Wiley & Sons, NY. 1985.Google Scholar
20. Rigaku, USA. DATA. LEX. 60 Software Manual, April 1985.Google Scholar