Hostname: page-component-848d4c4894-sjtt6 Total loading time: 0 Render date: 2024-06-19T09:23:36.573Z Has data issue: false hasContentIssue false
  • English
  • Français

Combined XRD-XRF cluster analysis for automatic chemical and crystallographic surface mappings

Published online by Cambridge University Press:  03 May 2019

M. Bortolotti Open the ORCID record for M. Bortolotti [Opens in a new window] ,
L. Lutterotti ,
E. Borovin  and
D. Martorelli Open the ORCID record for D. Martorelli [Opens in a new window]
M. Bortolotti*
Affiliation:
Department of Industrial Engineering, University of Trento, IT, Italy
L. Lutterotti
Affiliation:
Department of Industrial Engineering, University of Trento, IT, Italy
E. Borovin
Affiliation:
Department of Industrial Engineering, University of Trento, IT, Italy
D. Martorelli
Affiliation:
Department of Industrial Engineering, University of Trento, IT, Italy
*
a)Author to whom correspondence should be addressed. Electronic mail: mauro.bortolotti@unitn.it
Get access Rights & Permissions [Opens in a new window]

Abstract

X-ray diffraction-X-ray fluorescence (XRD-XRF) data sets obtained from surface scans of synthetic samples have been analysed by means of different data clustering algorithms, with the aim to propose a methodology for automatic crystallographic and chemical classification of surfaces. Three data clustering strategies have been evaluated, namely hierarchical, k-means, and density-based clustering; all of them have been applied to the distance matrix calculated from the single XRD and XRF data sets as well as the combined distance matrix. Classification performance is reported for each strategy both in numerical form as the corrected Rand index and as a visual reconstruction of the surface maps. Hierarchical and k-means clustering offered comparable results, depending on both sample complexity and data quality. When applied to XRF data collected on a two-phases test sample, both algorithms allowed to obtain Rand index values above 0.8, whereas XRD data collected on the same sample gave values around 0.5; application to the combined distance matrix improved the correlation to about 0.9. In the case of a more complex multi-phase sample, it has also been found that classification performance strongly depends on both data quality and signal contrast between different regions; again, the adoption of the combined dissimilarity matrix offered improved classification performance.

Keywords

combined XRD-XRFcluster analysissurface mappings
Type
Technical Articles
Information
Powder Diffraction , Volume 34 , Supplement S1 , September 2019 , pp. S36 - S41
Copyright
Copyright © International Centre for Diffraction Data 2019 

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

Ankerst, M., Breunig, M. M., Kriegel, H. P., and Sander, J. (1999). In Proceedings ACM SIGMOD'99 International Conference on Management of Data. doi:10.1145/304182.304187.Google Scholar
Barr, G., Dong, W., and Gilmore, C. J. (2004). “High-throughput powder diffraction. II. Applications of clustering methods and multivariate data analysis,” J. Appl. Crystallogr. 37, 243–252.Google Scholar
Bortolotti, M., Lutterotti, L., and Pepponi, G. (2017). “Combining XRD and XRF analysis in one Rietveld-like fitting,” Powder Diffr. 32(S1), 1–6.Google Scholar
Dunn, J. C. (1974). J. Cybern., 4, 95–104.Google Scholar
Egan, C. K., Jacques, S. D. M., Connolley, T., Wilson, M. D., Veale, M. C., Seller, P., and Cernik, R. J. (2014). “Dark-field hyperspectral X-ray imaging,” Proc. Royal Soc. A: Math. Phys. Eng. Sci. 470, 20130629–20130629.Google Scholar
Ester, M., Kriegel, H. P., Sander, J., and Xu, X. (1996). “A density-based algorithm for discovering clusters in a large spatial databases with noise,” in Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (AAAI Press, Menlo Park, CA), pp. 226–231.Google Scholar
Gilmore, C. J., Barr, G., and Paisley, J. (2004). “High-throughput powder diffraction. I. A new approach to qualitative and quantitative powder diffraction pattern analysis using full pattern profiles,” J. Appl. Crystallogr. 37, 231–242.Google Scholar
Hahsler, M., and Piekenbrock, M. (2017). https://CRAN.R-project.org/package=dbscan.Google Scholar
Kassambara, A., and Mundt, F. (2017). http://www.sthda.com/english/rpkgs/factoextra.Google Scholar
Kaufman, L., Kaufman, L., Rousseeuw, P. J., and Rousseeuw, P. J. (2005). Finding Groups in Data: An Introduction to Cluster Analysis (John Wiley & Sons, Hoboken, NJ).Google Scholar
Lance, G. N., and Williams, W. T. (1967). Comput. J., 9(4), 373–380.Google Scholar
Lloyd, S. P. (1982). “Least squares quantization in PCM,” IEEE Trans. Inf. Theory. 28, 129–137.Google Scholar
Lutterotti, L., Dell'Amore, F., Angelucci, D. E., Carrer, F., and Gialanella, S. (2016). “Combined X-ray diffraction and fluorescence analysis in the cultural heritage field,” Microchem. J. 126, 423–430.Google Scholar
Macqueen, J. (1967). “Some methods for classification and analysis of multivariate observations,” Proc. Fifth Berkeley Symp. on Math. Statist. and Prob. 1, 281–297.Google Scholar
Maechler, M., Struyf, A., Hubert, M., Hornik, K., Studer, M., and Roudier, P. (2015). https://cran.r-project.org/web/packages/cluster/index.html.Google Scholar
Rand, W. M. (1971). “Objective criteria for the evaluation of clustering methods,” J. Am. Stat. Assoc. 66, 846–850.Google Scholar
R Development Core Team, R. (2016). R Foundation for Statistical Computing. doi:10.1007/978-3-540-74686-7.Google Scholar
Tan, P.-N., Steinbach, M., and Kumar, V. (2005). Introduction to Data Mining. doi:10.1016/0022-4405(81)90007-8.Google Scholar
Theodoridis, S., and Koutroumbas, K. (2009). In Pattern Recognition (Fourth Edition). doi:http://dx.doi.org/10.1016/B978-1-59749-272-0.50018-9.Google Scholar
Supplementary material: File

Bortolotti et al. supplementary material

Bortolotti et al. supplementary material 1

Download Bortolotti et al. supplementary material(File)
File 3.6 KB