# Non-Invasive Painting Pigment Classification Through Supervised Machine Learning
## 6 Discussion  
The proposed framework offers a lightweight, interpretable, and cost-effective solution for non-invasive pigment classification in cultural heritage contexts. Its reliance on accessible imaging hardware and compact statistical features ensures operational efficiency and rapid pigment identification, making it practical for institutions without access to advanced spectroscopic systems.  
The high classification performance achieved by ensemble methods, particularly the random forest classifier (99.30% accuracy), confirms their suitability for modeling the complex, non-linearly separable feature space of pigment data. In contrast, simpler models such as linear classifiers and shallow decision trees struggled to generalize, often exhibiting underfitting or overfitting depending on the class distribution and feature granularity. This highlights the benefit of using more flexible learning algorithms in small, high-dimensional pigment datasets.  
Furthermore, the subclass-based classification strategy, which differentiates between pigment variants (e.g., pure, opaque, or mixed with white pigments), consistently outperformed the unified-class approach in top-N accuracy metrics. This indicates that capturing preparation-specific distinctions improves the model's ability to generate more precise, ranked lists of plausible pigment candidates, which is particularly valuable in conservation workflows.  
Despite these strengths, several limitations must be acknowledged. A primary challenge is the difficulty of distinguishing pigments with highly similar spectral profiles using only three broad spectral bands. For example, misclassifications were observed between ultramarine blue and Prussian blue, particularly in the near-infrared and ultraviolet bands. The model also showed reduced sensitivity in detecting organic lakes and degraded materials, which tend to exhibit weak or ambiguous reflectographic signals. While the laboratory dataset included samples prepared using various traditional binding media, systematic testing of artificially aged binders and varnishes was not conducted as part of this pilot study. The influence of such aging processes on spectral characteristics remains a complex and open research question.  
An additional limitation is the framework's assumption of a single dominant pigment per patch. This simplification, while suitable for visually homogeneous regions, does not account for the presence of pigment mixtures or stratified layers, which are common in historical artworks. More realistic modeling of such scenarios will require the integration of multi-label classification approaches or spectral unmixing techniques capable of resolving complex pigment combinations.  
Compared to hyperspectral imaging systems, the framework offers a significantly more accessible and resource-efficient alternative. While deep learning methods provide end-to-end solutions in related domains, the classical machine learning approach employed here prioritizes interpretability, low computational overhead, and compatibility with limited training data. This makes the framework well-suited for deployment in smaller research environments or field-based conservation projects. Overall, the results support the practical viability of the approach and establish a foundation for future methodological extensions.