# Non-Invasive Painting Pigment Classification Through Supervised Machine Learning
###### Abstract  
Accurate pigment classification is essential for the analysis and conservation of historical paintings. This study presents a non-invasive approach based on supervised machine learning for classifying pigments using image data acquired under three distinct spectral illumination conditions: visible-light reflectography (VIS), ultraviolet false-color imaging (UVFC), and infrared false-color imaging (IRFC). A dataset was constructed by extracting 32 \(\times\) 32 pixel patches from pigment samples, resulting in 200 classes representing 40 unique pigments with five preparation variants each. A total of 600 initial raw images were acquired, from which 4000 image patches were extracted for feature engineering. Feature vectors were obtained from visible reflectography, ultraviolet false-color imaging (UVFC), and infrared false-color imaging (IRFC) using statistical descriptors derived from RGB channels. This study demonstrates that accurate pigment classification can be achieved with a minimal set of three illumination types, offering a practical and cost-effective alternative to hyperspectral imaging in real-world conservation practice. Among the evaluated classifiers, the random forest model achieved the highest accuracy (99.30 \(\pm\) 0.15%). The trained model was subsequently validated on annotated regions of historical paintings, demonstrating its robustness and applicability. The proposed framework offers a lightweight, interpretable, and scalable solution for non-invasive pigment analysis in cultural heritage research that can be implemented with accessible imaging hardware and minimal post-processing. p 2025157594 Non-invasive imaging techniques have been developed to overcome these limitations, preserving the integrity of artworks while providing meaningful data. However, many rely on subjective visual interpretation by human experts, which can introduce potential for error due to perceptual variation. While hyperspectral imaging offers improved classification accuracy, it typically requires complex instrumentation and significant computational resources. These factors limit its applicability in conservation practice [2]. A fundamental challenge in non-invasive pigment classification, particularly when utilizing a limited number of broad spectral bands, is the issue of spectral overlap [3; 4; 5]. Certain pigments, such as ultramarine blue and Prussian blue, exhibit highly similar spectral responses across the visible, ultraviolet, and near-infrared ranges, making their differentiation challenging solely based on these modalities. This inherent ambiguity necessitates careful consideration of the diagnostic resolution achievable with a reduced spectral set. Furthermore, the spectral response of pigments in real-world artworks can be significantly influenced by various factors beyond their intrinsic properties, including the type of binding medium used, the effects of aging, and various degradation processes [6; 7]. Such variations can alter the pigment's spectral signature, complicating accurate classification. A major challenge also arises from the common historical painting practices of pigment mixing and layering. Simple classification models, which often assume a single dominant pigment per analyzed region, may struggle to accurately resolve these complex compositions [3]. Finally, there is an inherent trade-off between non-invasiveness and diagnostic resolution. While non-invasive methods are crucial for preserving fragile artworks, they typically provide less detailed chemical information compared to other advanced spectroscopic techniques like XRF or Raman spectroscopy. Acknowledging these limitations upfront establishes a clearer problem statement, demonstrating how the proposed lightweight solution aims to provide a practical and accessible alternative that balances these challenges rather than offering a complete replacement for all analytical needs.