Adaptive algorithm for pigment identification from unmixing spectral data: Case study with two versions of a XVI century painting Valero Benito, Eva María Martínez Domingo, Miguel Ángel Blanc García, María Rosario López Baldomero, Ana Belén López Montes, Ana María Martín, Domingo Abad Muñoz, David Vílchez Quero, José Luis Spectral imaging Cultural heritage Pigment identification Our thanks to Laura Figueroa for her contribution to the implementation of the superpixel segmentation and the analysis of preliminary trials data. This work was supported by Grant PID2021-124446NB-100 funded by MICIU/AEI /10.13039/501100011033 and by ERDF, EU and by the Ministry of Universities, Spain [grant number FPU2020-05532]. Artists commonly use a relatively reduced palette of pigments and mix them in different proportions to increase the gamut of colors present on artworks. In this study, a complete workflow for pigment identification using spectral unmixing of reflectance spectra in the visible and near infrared is presented. The algorithm includes superpixel segmentation as pre-processing to reduce the number of spectra that are unmixed. Then, a pre-extracted set of relevant color instances from the painting is used to build an adaptive subset of candidate pigments from a reference palette, and pigment identification is achieved by superpixel voting within the reduced subsets corresponding to the automatically extracted endmembers presence maps. Two different moments in time of a Maternity of the 16th century (original and restored) and a modern replica of the same painting are used to showcase the performance of the algorithm, which is able to correctly identify 80 % of the pigments present from a reference library of 23 pigments, taking less than three minutes for processing around 7000 spectra. 2025-12-15T08:36:12Z 2025-12-15T08:36:12Z 2026-04-01 journal article Valero EM, Martínez-Domingo MÁ, Blanc R, López-Baldomero AB, López-Montes A, Martín D, Abad-Muñoz D, Vílchez-Quero JL. Adaptive algorithm for pigment identification from unmixing spectral data: Case study with two versions of a XVI century painting. Talanta. 2025 Nov 25;300:129170. doi: 10.1016/j.talanta.2025.129170 0039-9140 https://hdl.handle.net/10481/108785 10.1016/j.talanta.2025.129170 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier