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Spectral Imaging and Archival Data in Analysing Madonna of the Rabbit Paintings by Manet and Titian

  Articoli su Riviste JCR/ISI  (anno 2018)

Autori:  Striova J., Ruberto C., Barucci M., Bla×ek J., Kunzelman D., Dal Fovo A., Pampaloni E., Fontana R

Affiliazione Autori:  National Research Council (CNR)-National Institute of Optics (INO) Largo Enrico Fermi 6, 50125 Florence (Italy); Department of Physics and Astronomy, University of Florence, National Institute of Nuclear Physics (INFN) Via Sansone 1, 50019 Sesto Fiorentino (FI) (Italy); Institute of Information Theory and Automation, Czech Academy of Science Pod VodÓrenskou vezý 4, 18208 Prague (Czech Republic); Opificio delle Pietre Dure di Firenze (Italy)

Riassunto:  A concise insight into the outputs provided by the latest prototype of visible-near infrared (Vis-NIR) multispectral scanner (National Research Council-National Institute of Optics, CNR-INO, Italy) is presented. The analytical data acquired on an oil painting Madonna of the Rabbit by E. Manet are described. In this work, the Vis-NIR was complemented with X-ray fluorescence (XRF) mapping for the chemical and spatial characterization of several pigments. The spatially registered Vis-NIR data facilitated their processing by spectral correlation mapping (SCM) and artificial neural network (ANN) algorithm, respectively, for pigment mapping and improved visibility of pentimenti and of underdrawing style. The data provided several key elements for the comparison with a homonymous original work by Titian studied within the ARCHive LABoratory (ARCHLAB) transnational access project.

Volume n.:  57 (25)      Pagine da: 7408  a: 7412
Ulteriori informazioni:  Financial support by Transnational Access to Research Infrastructures activity EU H2020 Programme (IPERION CH GA 654028) is gratefully acknowledged. XRF scanner was developed with support of FOTONART project funded by Fondazione Cassa di Risparmio di Firenze. Eva Pospisilova (Department of Chemistry, Faculty of Science, Masaryk University, Czech Republic) is acknowledged for her contribution during Erasmus+ traineeship.
DOI: 10.1002/anie.201800624

   *Citazioni: 2
data tratti da "WEB OF SCIENCE" (marchio registrato di Thomson Reuters) ed aggiornati a:  14/07/2019

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