Response Surface Model Applied to Fine Arts: The Case of the Restoration of Paintings

Abstract

Cleaning polychrome paintings and sculptures is an essential task in restoration treatment, since it irreversibly affects the appearance and material structure of such works of art. It is a completely “analogical” process consisting of removing surface dirt, aged varnishes or repainting (paints added to the original) based on the restorer’s experience and knowledge, as well as on different internationally accepted criteria for such interventions. In this chapter we are presenting an example of the adaptation of the response surface model to this field, which is complex and difficult to adapt to quantitative parameters and has never before been studied with this approach. Using the MODDE Go® experiment optimization and statistical design software, the effectiveness of cleaning pictorial works of art has been studied using various formulas composed mainly of water and a low-toxicity monoterpene: limonene. The model’s statistical validity is demonstrated, as well as its ability to determine the main factors that affect the cleaning by means of different responses (methods) to evaluate its effectiveness: an expert’s opinion using visible light and ultraviolet light, the amount of varnish removed using gas chromatography coupled with mass spectrometry, and the effects on color, lightness and gloss. The main influential factors were the concentrations of the two main components of the proposed formulations, water and limonene, which regulate the cleaners’ level of hydrophilia and lipophilicity, followed by the types of pigments and type of varnish used, and aging. Using an in silico simulation, the proposed model also enables specific compositions to be formulated for different scenarios and cleaning applications that are potentially effective and harmless to the pictorial materials and the restorers’ health.