Abstract
Frequently, the study of modern and contemporary paintings requires the taking of micro-samples to gain an in-depth understanding of the employed materials and techniques. However, since this procedure is characterized by its invasive nature, it must be carried out only if strictly necessary. This study aimed to evaluate the potentiality of K-means clustering to the corrected images of paintings to identify mixtures of pigments. This could assist in obtaining relevant preliminary information, facilitate the research process, and guide the sampling collection. Additionally, this method would be less expensive than the traditional multi-analytical approach as it would only require a modified digital camera, lenses, a color target and three computational resources for the processing of data (Imatest Master, Adobe Express—online, and R), out of which the latter two are freely available. The six paintings that have been selected for this study belong to the International Gallery of Modern Art Ca’ Pesaro in Venice (Italy) and have been depicted by Andreina Rosa (1924–2019), a Venetian artist. The artworks were thoroughly investigated mainly through non-invasive analytical techniques (FORS, RAMAN, ER-FTIR, EDXRF). Using cluster analysis, simulating mixtures, and calculating the color differences, it was possible to infer the existence of color mixtures of two/three detected primary colors from the examined images, which could be validated by the analytical results. Hence, it was concluded that samples taken from mixtures might suffice, since primary colors would be concomitantly analyzed.
Subject
Materials Science (miscellaneous),Archeology,Conservation