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This study presents a laboratory-based XANES protocol optimized for the chemical characterization of historical pigments in cultural heritage materials. Using a refined lab-scale X-ray absorption spectroscopy set-up, we demonstrate the capability to extract chemically meaningful near-edge features - such as oxidation-state–dependent edge shifts, pre-edge features, and spectral shoulders - that enable reliable discrimination among pure pigment systems. The protocol was benchmarked against synchrotron XANES reference data and supported by ab-initio simulations, confirming the preservation of diagnostically relevant near-edge information under laboratory energy resolution constraints. Linear Combination Fitting (LCF) was employed to obtain semi-quantitative insights into pigment mixtures. The protocol was first tested on well-defined binary mixtures simulating the co-existence of original pigments and their potential deterioration products and was subsequently applied to unknown and compositionally complex mixtures, where the identification of different materials is critical for the study of ancient and modern artists’ materials. Laboratory XANES applied to optimized thin sections was demonstrated to reproducibly capture As(III) spectral features in orpiment-based mock-ups, and clearly distinguish it from As₂O₃, demonstrating its potential for chemical speciation studies in micrometric paint layers. While laboratory-based XANES does not replace synchrotron measurements in terms of ultimate spectral resolution, it enables reproducible discrimination of oxidation states and local coordination environment and supports semi-quantitative phase assessment in contexts where synchrotron access is not feasible. Overall, the results establish a robust methodological framework for extending XANES-informed chemical interpretation into routine heritage science workflows, proving that laboratory XANES, compared to synchrotron experiments, can be less invasive and preserve sufficient near-edge sensitivity for reliable speciation and semi-quantitative bulk analysis, despite the lower spectral resolution.
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Santagati et al. (2026) studied this question.
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