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Issue 3, 2015
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Strategies for processing mega-pixel X-ray fluorescence hyperspectral data: a case study on a version of Caravaggio's painting Supper at Emmaus

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Abstract

Technical progress in the fields of X-ray sources, optics and detectors is constantly enhancing the pace of data acquisition in XRF imaging. This enlarges the size of the hyperspectral datasets and the number of their sub-parts. This paper describes the challenges in processing large XRF datasets featuring several million pixels/spectra and the strategies developed to overcome them. During the investigation of historical paintings by scanning macro-XRF the main challenges are the correct identification of all spectral features in a dataset and its timely processing. For the identification of spectral features different approaches are discussed, i.e. the use of sum spectra, maximum pixel spectra and of χr2 maps. For the time-efficient, artefact-free evaluation of XRF imaging data, different software packages are evaluated and intercompared (AXIL, PyMCA, GeoPIXE and the in-house written datamuncher). The process of data evaluation is illustrated on a large dataset (3.4 MPixels) acquired during the investigation of a version of Caravaggio's Supper at Emmaus (143 × 199.5 cm2). This 17th century painting is currently the largest object entirely scanned with macroscopic XRF.

Graphical abstract: Strategies for processing mega-pixel X-ray fluorescence hyperspectral data: a case study on a version of Caravaggio's painting Supper at Emmaus

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Publication details

The article was received on 31 Oct 2014, accepted on 19 Jan 2015 and first published on 12 Feb 2015


Article type: Paper
DOI: 10.1039/C4JA00387J
Citation: J. Anal. At. Spectrom., 2015,30, 777-789
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    Strategies for processing mega-pixel X-ray fluorescence hyperspectral data: a case study on a version of Caravaggio's painting Supper at Emmaus

    M. Alfeld and K. Janssens, J. Anal. At. Spectrom., 2015, 30, 777
    DOI: 10.1039/C4JA00387J

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