Hyper-spectral acquisition on historically accurate reconstructions of red organic lakes

Tatiana Vitorino, Andrea Casini, Costanza Cucci, Maria João Melo, Marcello Picollo, Lorenzo Stefani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Citations (Scopus)


Our cultural heritage is constituted by irreplaceable artworks that must be known and preserved. Their study and documentation should be in principle carried out using non-invasive approaches. The technological advances in spectroscopic imaging acquisition devices made it possible to apply this methodology to such purpose. In this context, the present paper discusses a particularly challenging task within the conservation field, which is the identification of red lake pigments in artworks, applying Vis-NIR hyper-spectral imaging spectroscopy. The latter was used to characterize and discriminate between historically accurate paint reconstructions of brazilwood (vegetal) and cochineal (animal) lake pigments. The same paints were also analyzed with Fiber Optic Reflectance Spectroscopy to validate the data obtained with the imaging method. The requirements for a successful identification of these pigments are addressed, and future research is suggested in order to increase the usefulness of the technique's application.

Original languageEnglish
Title of host publicationImage and Signal Processing - 6th International Conference, ICISP 2014, Proceedings
Number of pages8
Volume8509 LNCS
ISBN (Print)978-3-319-07997-4
Publication statusPublished - 2014
Event6th International Conference on Image and Signal Processing, ICISP 2014 - Cherbourg, France
Duration: 30 Jun 20142 Jul 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8509 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference6th International Conference on Image and Signal Processing, ICISP 2014


  • Brazilwood
  • Cochineal
  • Hyper-spectral imaging
  • Non-invasive approach
  • Red lake pigments


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