In recent years, non-destructive imaging technology has made rapid advancements in the field of painting research and preservation. Among these, macro X-ray fluorescence (MA-XRF) analysis stands out, helping experts identify pigments and analyze painting techniques, providing valuable insights into the artist's creative process. However, MA-XRF technology generates large and complex datasets that challenge traditional data analysis methods. Recently, Italian researchers applied deep learning algorithms to spectral analysis of MA-XRF datasets, developing a brand new analysis approach.
