Mixing to unmix: painting component identification in historical graphic documents via data fusion of DRIFTS and VNIR-SWIR reflectance spectra Moronta-Montero, Francisco Reichert, Anna S. López Baldomero, Ana Belén Blanc García, María Rosario Cardell Fernández, Carolina López Montes, Ana María Hernández Andrés, Javier Valero Benito, Eva María Hyperspectral imaging Diffuse reflectance Fourier transform infrared spectroscopy (DRIFTS) Spectral unmixing Data fusion Painting mock-ups Illuminated historical manuscripts This work supported by grant PID2021-124446NB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF, EU; grant PRE2022-101352 funded by MICIU/AEI/10.13039/501100011033 and “ESF+”; and grant FPU2020-05532 funded by Ministry of Universities (Spain) . This work was also supported by the Andalusian Research Group RNM-179 . Funding for open access charge: Universidad de Granada / CBUA. The identification of historical painting materials is essential for understanding artistic techniques, proposing conservation strategies, and supporting dating and provenance studies. However, the presence of pigment and dye mixtures, along with complex pigment–binder interactions, poses significant challenges for non-invasive analysis. This study presents a spectral unmixing approach based on the data fusion of hyperspectral imaging (HSI) and Diffuse Reflectance Fourier Transform Spectroscopy (DRIFTS) data to identify the painting components of mock-up samples that replicate historical objects. Results show that fusion significantly improves the identification of mixture painting components compared to single-technique analysis. The complement of the Goodness-of-Fit Coefficient (cGFC) outperformed other metrics, achieving the highest rate of correct painting component identification and lowest error for the greater presence of some elements compared to others. Preprocessing steps including Savitzky–Golay derivative, spectral cropping, and normalization proved essential for maximizing performance. The method was further validated on a set of historical manuscripts, correctly identifying painting materials in the majority of cases. 2025-09-26T09:51:53Z 2025-09-26T09:51:53Z 2025-09-17 journal article Moronta-Montero, F. et al. (2025). Mixing to unmix: Painting component identification in historical graphic documents via data fusion of DRIFTS and VNIR–SWIR reflectance spectra. Microchemical Journal, 218, 115223. https://doi.org/10.1016/j.microc.2025.115223 https://hdl.handle.net/10481/106656 10.1016/j.microc.2025.115223 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier