Artwork appearances change over time due to aging. Smalt, a blue cobalt-tinted glass pigment, deteriorates over time in oil paintings causing significant and irreversible color changes in many artworks. Virtual simulations can hypothesis original appearances while it remains a challenge for smalt-containing paintings. A novel procedure integrates non-invasive imaging methods, X-ray absorption near-edge structure (XANES), and machine learning to simulate the original colors of a smalt-containing discolored paintings. Macro-X-ray fluorescence provided elemental distribution, reflectance imaging spectroscopy captured color spectra of pigments and XANES informed cobalt speciation in cross sections. Friedrich’s Abbey Among Oak Trees (1808-1810) containing smalt and artificially aged model systems were studied. Machine learning predicted the original hues based on XANES. The procedure allowed us to simulate the original, cooler and more vibrant colors of the painting. The innovative approach visualizes a possible original state of the smalt-containing artwork that can be adapted to other alteration phenomena.

Traditional “stack “manufacturing process (16–19th c.) of lead white pigment by lead corrosion is explored by exposing metal to acetic acid, carbon dioxide, dioxygen and water vapor. Global reaction scheme is revealed, along with stratification of corrosion products with CO₂ gradient, leading to flakiness of corrosion layer. Kinetic and epitaxial features rule the polyphased structure, explaining hydrocerussite-rich compositions and absence of plumbonacrite in classical easel paintings. Microstructure of cerussite appears as a differentiation criterion between stack-made and industrial precipitation-synthesized lead whites. Particular conditions reported in ancient writings are tested and discussed to understand unusual processes.