The goal of this project is to use machine learning (ML) algorithms to model how the reflectance spectra of complex mixtures of colored materials from historical artworks, specifically illuminations and/or decorated leBers from ancient manuscripts, depend on their chemical composition. Covering a wide range of energy from visible (VIS) to mid-infrared (mid-IR) coupled with X-ray fluorescence spectroscopy (XRF), a spectral database of reference inorganic and organic materials will be built.
Subsequently, a supervised ML algorithm will be trained on the database, and the obtained ML model will be capable of simultaneously predicting the composition of the colored layer and the abundance of materials present in the mixture. Next, we will use the trained ML model to generate a greater variety of artificial reflectance spectra, thus expanding the existing data set. A second ML model will then be trained on the expanded data set to understand how the reflectance spectrum correlates with the composition of the pictorial mixture. This second model will be applied to the analysis of historical manuscripts. The candidate is expected to develop an approach that will offer an efficient and general method for onsite determina8on of the composi8on of historical illumina8ons based on their spectral response.