Nondestructive detection and classification of traditional handmade paper using near-infrared hyperspectral imaging and machine learning

Traditional handmade papers such as Hanji, Washi, and Xuan paper hold substantial cultural and historical value across East Asia. However, their classification and authentication remain challenging due to variations in raw materials and manufacturing techniques. In this study, we propose a nondestructive approach using near-infrared (NIR) hyperspectral imaging combined with machine learning to classify traditional handmade papers from China, Japan, and Korea. NIR spectra (900–1,700 nm) were extracted from hyperspectral images of 26 paper samples and preprocessed using first derivatives. Dimensionality reduction and clustering were performed using principal component analysis (PCA) and density-based spatial clustering of applications with noise (DBSCAN), which also identified outliers of spectra. Multiple classification models, including support vector machine (SVM), FNN, and XGBoost, were trained and evaluated, with SVM achieving the highest F1-score (1.000). Feature importance derived from XGBoost highlighted key spectral regions relevant to classification. Additionally, the spectral angle mapper (SAM) enabled pixel-wise visualization, revealing spectral heterogeneity among the samples. This study demonstrates the effectiveness of NIR hyperspectral imaging and machine learning for the rapid, interpretable, and noninvasive classification of traditional handmade papers, providing valuable tools for heritage conservation and authenticity verification.