https://doi.org/10.1140/epjp/s13360-021-02028-0
Regular Article
A novel photoluminescence hyperspectral camera for the study of artworks
1
Physics Department, Politecnico Di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
2
IFN, CNR, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
3
XGLab S.R.L.- Bruker Nano Analytics, Via Conte Rosso 23, 20134, Milan, Italy
4
Institute for Conservation Sciences, Staatliche Akademie der Bildenden Künste, Am Weißenhof 1, 70191, Stuttgart, Germany
5
University of Stuttgart, Materials Testing Institute, Pfaffenwaldring 2b, 70569, Stuttgart, Germany
6
Paz Laboratorien Für Archäometrie, Planiger Straße 34/Haus 18/19, 55543, Bad Kreuznach, Germany
Received:
19
May
2021
Accepted:
3
October
2021
Published online:
20
October
2021
We present the application of a novel hyperspectral camera, based on the Fourier-transform approach, to study the photoluminescence emission from artworks at different spatial scales and emission timescales. The hyperspectral system relies on an innovative wide-field, compact and ultra-stable interferometer coupled to different excitation and detection methods. Here, we describe and illustrate the potentialities and limitations of its use when coupled with excitation at variable fluence and with time-gated detection. The developed methods allow an in-depth characterization of the optical emission from luminescent materials in cultural heritage and provide information on the nature of the recombination pathways in crystalline pigments. Indeed, one of the main difficulties in the interpretation of the optical emission from artworks is the presence of multiple emitting compounds with spectra characterized by broad emission bands. The photoluminescence imaging methods here proposed allow to partially solve this issue, by separating emission from different materials on the basis of their different timescales and spectral emission properties, thus providing important information to support material identification. Furthermore, the high spectral accuracy achievable with a hyperspectral camera, such as the one proposed in this paper, allows the collection of highly resolved spectral datacubes, which can then be post-processed with computational and multivariate statistical analysis methods to better assess material identification and mapping.
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjp/s13360-021-02028-0.
© The Author(s) 2021
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