Comparison of macro x‐ray fluorescence and reflectance imaging spectroscopy for the semi‐quantitative analysis of pigments in easel paintings: A study on lead white and blue verditer

Abstract

Macroscopic x‐ray fluorescence imaging spectroscopy (MA‐XRF) and reflectance imaging spectroscopy (RIS) are important tools in the analysis of cultural heritage objects, both for conservation and art historical research purposes. The elemental and molecular distributions provided by MA‐XRF and RIS respectively, are particularly useful for the identification and mapping of pigments in easel paintings. While MA‐XRF has relatively established data processing methods based on modeling of the underlying physics, RIS data cannot be modeled with sufficient precision and its processing has considerable room for improvements. This work seeks to improve RIS data processing workflows in the short wavelength infrared range (SWIR, 1000–2500 nm) with a novel method that fits Gaussian profiles to pigment‐specific absorption features, and we compare its performance to MA‐XRF for the task of semi‐quantitative pigment mapping, evaluating their limits of detection (LODs) and the matrix effects that affect their signals. Two pigments are considered in this work, lead white and blue verditer, which are mapped in SWIR RIS using the first overtone of OH stretching of their primary compounds, hydrocerussite (Pb3(CO3)2(OH)2) and azurite (Cu3(CO3)2(OH)2), at 1447 and 1497 nm respectively, and in MA‐XRF using the Pb‐L and Cu‐K fluorescence signals. The methods are evaluated using two sets of custom‐prepared paint samples, as well as a 16th‐century painting, discussing the identification, mapping, and semi‐quantitative analysis of the considered pigments. We found SWIR RIS to be a pigment‐specific method with a longer linear range but inferior LODs and penetration depth when compared to MA‐XRF, the latter is often not capable of discriminating between different pigments with identical elemental markers. We furthermore present a novel color scale that allows the simultaneous visualization of signals above and below a confidence limit.