# Non-Invasive Painting Pigment Classification Through Supervised Machine Learning
## 2 Related Work
A convolutional neural network (CNN) trained on these synthetic spectra achieved near-perfect results on simulated inputs and, following transfer learning with a limited number of real measurements, reached a classification accuracy of 96%. Their approach illustrates the feasibility of combining physics-based simulations with deep learning for pigment identification. [PERSON] et al. [17] developed a transfer learning pipeline that combines ResNet50 and support vector machines (SVMs) for authorship and stylistic attribution in Renaissance paintings. By incorporating edge detection algorithms, their model captures brushstroke-level patterns, demonstrating how stylistic and material analysis can be unified through deep feature extraction. [PERSON] et al. [18] returned the focus to pigment classification using a supervised one-dimensional CNN trained on reflectance spectra obtained from 14 th-century illuminated manuscripts. Their end-to-end approach removes the need for manual clustering and produces accurate pigment maps with a mean per-class accuracy exceeding 98%. Building upon this, [PERSON] et al. [19] extended the training set to include ten 15 th-century manuscripts and introduced a dual-CNN model: one trained on raw reflectance spectra and the other on first-derivative data. The derivative-based model exhibited improved generalization, emphasizing the importance of spectral preprocessing in enhancing class separability. More recent studies have begun to address the challenges posed by pigment mixtures and layered structures. [PERSON] and [PERSON] [20] applied the opaque form of the Kubelka-Munk theory to estimate pigment concentrations from hyperspectral data, achieving higher accuracy than traditional reflectance models. [PERSON] et al. [21] applied spectral unmixing techniques to the surfaces of painted artifacts, explicitly accounting for spectral variability--an essential consideration in the analysis of complex heritage materials. From a deep learning perspective, [PERSON] et al. [22] proposed a neural network-based spectral unmixing model capable of effectively separating pigments in hyperspectral images of paintings. Multi-label classification schemes were examined by [PERSON] and [PERSON] [23], who compared training strategies for hyperspectral remote sensing images--a relevant foundation for the modeling of overlapping pigment distributions. [PERSON] et al. [24] introduced a deep learning framework for analyzing stratified XRF data in painted surfaces, showing that layered pigment compositions can be automatically inferred with high accuracy. Collectively, these efforts reflect a clear progression toward intelligent, automatic, and domain-adaptable pigment classification systems. Leveraging a range of imaging modalities, including reflectance and XRF, and incorporating both supervised and unsupervised learning strategies, these innovations increasingly rely on simulated and transfer-learned datasets. As the field evolves, such methodologies are expected to deliver scalable, non-invasive solutions tailored to the analytical demands of cultural heritage science.