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An innovative protocol for the study of painting materials involving the combined use of MA-XRF maps and hyperspectral images

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Abstract

X-ray fluorescence (XRF) and reflectance spectroscopy (RS) are commonly used for the characterization of painting materials. It is well known that the former provides the chemical fingerprint of the pictorial layers, while the latter returns the molecular description of the pigments constituting the uppermost layers. Even if these two techniques cannot unveil the stratigraphy, their synergetic application well describes the materials employed for realizing the panels and represents a key turn for non-invasive scientific analysis of works of art. However, the potential of the cross-comparison between XRF and RS is not fully exploited yet. The measurement points often barely match, and they are usually few isolated spots spread over the whole surface of the painting; these facts limit the mutual exchange of information between the data sets and can lead to losing details. In this scenario, XRF mapping (MA-XRF) and hyperspectral reflectance imaging (HRI) provide a connection channel that promises to be a decisive tool to strengthen the relationship between X-ray fluorescence and reflectance spectroscopy and, therefore, to deepen the knowledge about the case studies. Due to the spatial localization of the information they contain, the maps provide not only a straightforward reference for comparing the data but also a three-dimensional collection of elemental and molecular images. By applying computer vision and statistical methods such as spectral angle mapper (SAM), it is possible to implement an innovative approach that exploits the elemental features, obtained from XRF spectra, to improve the comprehension of the molecular aspects given by RS, and vice versa. Once we discussed the main issues behind our approach, we applied it to analyze the painting Chariot Race by Giorgio De Chirico (1928–1929, oil on canvas, Pinacoteca di Brera, Milan, Italy). The results reflect the complexity of the painting, and even if only some of the spectra identified by the method as peculiar are ascribable to recognizable pigments, the mutual correspondence between elemental distributions and SAM maps defines a mixture of materials that matches the description given by the artist in his “Small Treatise on Pictorial Technique” (De Chirico in Abscondita, 2019).

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References

  1. G. De Chirico, Piccolo trattato di tecnica pittorica (Abscondita, Milano, 2013)

  2. M. Alfeld, L. de Viguerie, Spectrochim. Acta B. At. Spectrosc. 136, 81 (2017)

    Article  Google Scholar 

  3. E. D'Elia, P. Buscaglia, A. Piccirillo, M. Picollo, A. Casini, C. Cucci, L. Stefani, F.P. Romano, C. Caliri, M. Gulmini, Microchem. J. 154, 104541 (2020)

    Article  Google Scholar 

  4. D. MacLennan, K. Trentelman, Y. Szafran, A.T. Woollett, J.K. Delaney, K. Janssens, J. Dik, J. Am. Inst. Conserv. 58, 54 (2019)

    Article  Google Scholar 

  5. R. Mulholland, D. Howell, A. Beeby, C.E. Nicholson, K. Domoney, Herit. Sci. 5, 43 (2017)

    Article  Google Scholar 

  6. J.K. Delaney, D.M. Conover, K.A. Dooley, L. Glinsman, K. Janssens, M. Loew, Herit. Sci. 6, 31 (2018)

    Article  Google Scholar 

  7. J.K. Delaney, P. Ricciardi, L.D. Glinsman, M. Facini, M. Thoury, M. Palmer, E.R.D.L. Rie, Stud. Conserv. 59, 91 (2014)

    Article  Google Scholar 

  8. K. Trentelman, M. Bouchard, M. Ganio, C. Namowicz, C.S. Patterson, M. Walton, X-Ray Spectrom. 39, 159 (2010)

    Article  ADS  Google Scholar 

  9. L. Bonizzoni, M. Gargano, N. Ludwig, M. Martini, A. Galli, Appl. Spectrosc. 71, 1915 (2017)

    Article  ADS  Google Scholar 

  10. C. Colombo, S. Bracci, C. Conti, M. Greco, M. Realini, X-Ray Spectrom. 40, 273 (2011)

    Article  ADS  Google Scholar 

  11. L. Bonizzoni, S. Caglio, A. Galli, G. Poldi, Appl. Phys. A Mater. Sci. Process. 92, 203 (2008)

    Article  ADS  Google Scholar 

  12. K. Laclavetine, D. Giovannacci, M. Radepont, A. Michelin, A. Tournié, O. Belhadj, C. Andraud, W. Nowik, X-Ray Spectrom. 50, 358 (2021)

    Article  ADS  Google Scholar 

  13. M. Alfeld, M. Mulliez, J. Devogelaere, L. de Viguerie, P. Jockey, P. Walter, Microchem. J. 141 (2018).

  14. H. Deborah, S. George, J.Y. Hardeberg, J. Am. Inst. Conserv. 58, 90 (2019)

    Article  Google Scholar 

  15. J. A. Richards, Remote sensing digital image analysis, 5th edn. (Springer, Berlin, 1999)

  16. B. Grabowski, W. Masarczyk, P. Głomb, A. Mendys, J. Cult. Herit. 31, 1 (2018)

    Article  Google Scholar 

  17. A. Polak, T. Kelman, P. Murray, S. Marshall, D.J.M. Stothard, N. Eastaugh, F. Eastaugh, J. Cult. Herit. 26, 1 (2017)

    Article  Google Scholar 

  18. R. Qureshi, M. Uzair, K. Khurshid, H. Yan, Pattern Recognit. 90, 12 (2019)

    Article  ADS  Google Scholar 

  19. R. Gupta, IEEE Trans. Pattern Anal. Mach Intell. 19, 963 (2019)

    Article  Google Scholar 

  20. Visit the website: www.xglab.it

  21. R. Alberti, V. Crupi, R. Frontoni, G. Galli, M.F.L. Russa, M. Licchelli, D. Majolino, M. Malagodi, B. Rossi, S.A. Ruffolo, V. Venuti, J. Anal. At. Spectrom. 32, 117 (2017)

    Article  Google Scholar 

  22. I. Liritzis, N. Zacharias, in X-Ray Fluorescence Spectrometry (XRF) in Geoarchaelogy, ed M. Shackley (Springer, New York, 2011), p. 109

  23. C. Cucci, J.K. Delaney, M. Picollo, Acc. Chem. Res. 49, 2070 (2016)

    Article  Google Scholar 

  24. A. Galli, M. Caccia, R. Alberti, L. Bonizzoni, N. Aresi, T. Frizzi, L. Bombelli, M. Gironda, M. Martini, X-Ray Spectrom. 46, 235 (2017)

    Article  Google Scholar 

  25. J. Orsilli, A. Galli, L. Bonizzoni, M. Caccia, Appl. Sci. 11, 1446 (2021)

    Article  Google Scholar 

  26. J. Tuszynski (2021). read_envihdr (https://www.mathworks.com/matlabcentral/fileexchange/38500-read_envihdr), MATLAB Central File Exchange. Retrieved January 9, 2021.

  27. A. Goshtasby, Pattern Recognit. 19, 459 (1986)

    Article  ADS  Google Scholar 

  28. The MathWorks, (2020). Visit the website: https://it.mathworks.com

  29. R. C. Gonzalez, R. E. Woods, S. L. Eddins, Digital Image Processing Using Matlab, 3rd edn. (Gatesmark, Knoxville, Tennessee, 2020)

  30. R.M. Haralick, L.G. Shapiro, Comput. Vis. Graph. Image Process. 29, 100 (1985)

    Article  Google Scholar 

  31. D. L. Davies, D. W. Bouldin, IEEE Trans. Pattern Anal. Mach. Intell. PAMI-1, 224 (1979)

  32. Visit the IFAC-CNR Spectroscopic Measurement Database (https://www.spectradb.ifac.cnr.it)

  33. M. Mantler, M. Schreiner, X-Ray Spectrom. 29, 3 (2000)

    Article  ADS  Google Scholar 

  34. L. Bonizzoni, A. Galli, G. Poldi, M. Milazzo, X-Ray Spectrom. 36, 55 (2007)

    Article  ADS  Google Scholar 

  35. S. Bruni, S. Caglio, V. Guglielmi, G. Poldi, Appl. Phys. A Mater. Sci. Process. 92, 249 (2008)

    Article  Google Scholar 

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Acknowledgements

The authors are grateful to Marina Gargiulo (Pinacoteca di Brera), Ilaria Perticucci (Studio Perticucci), and Fondazione Atlante.

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Correspondence to Michele Caccia.

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Galli, A., Caccia, M., Caglio, S. et al. An innovative protocol for the study of painting materials involving the combined use of MA-XRF maps and hyperspectral images. Eur. Phys. J. Plus 137, 22 (2022). https://doi.org/10.1140/epjp/s13360-021-02183-4

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  • DOI: https://doi.org/10.1140/epjp/s13360-021-02183-4

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