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Published January 2011 | Published
Journal Article Open

A wavelet transform algorithm for peak detection and application to powder x-ray diffraction data


Peak detection is ubiquitous in the analysis of spectral data. While many noise-filtering algorithms and peak identification algorithms have been developed, recent work [P. Du, W. Kibbe, and S. Lin, Bioinformatics 22, 2059 (2006); A. Wee, D. Grayden, Y. Zhu, K. Petkovic-Duran, and D. Smith, Electrophoresis 29, 4215 (2008)] has demonstrated that both of these tasks are efficiently performed through analysis of the wavelet transform of the data. In this paper, we present a wavelet-based peak detection algorithm with user-defined parameters that can be readily applied to the application of any spectral data. Particular attention is given to the algorithm's resolution of overlapping peaks. The algorithm is implemented for the analysis of powder diffraction data, and successful detection of Bragg peaks is demonstrated for both low signal-to-noise data from theta–theta diffraction of nanoparticles and combinatorial x-ray diffraction data from a composition spread thin film. These datasets have different types of background signals which are effectively removed in the wavelet-based method, and the results demonstrate that the algorithm provides a robust method for automated peak detection.

Additional Information

© 2011 American Institute of Physics. Received 7 June 2010; accepted 1 October 2010; published online 13 January 2011. The authors thank Brian Leonard and Francis DiSalvo for providing the PtZn nanoparticle powder diffraction data and for helpful suggestions. This material is based upon work supported as part of the Energy Materials Center at Cornell (EMC2), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award No. DE-SC0001086. The authors thank Alexander Kazimirov for assistance with the synchrotron XRD experiments, which were conducted at the Cornell High Energy Synchrotron Source (CHESS) with support from the National Science Foundation and the National Institutes of Health/National Institute of General Medical Sciences under NSF Award No. DMR-0225180.

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