Non-Destructive Species Discrimination of Japanese Bast Fibers: A Feasibility Study Using Micro-Hyperspectral Imaging and Chemometrics

Accurate paper fiber identification is crucial for cultural heritage conservation. To address the destructive nature of traditional staining and the “black-box” limitations of macroscopic AI models, this study explores the feasibility of a non-destructive testing paradigm using micro-hyperspectral imaging (Micro-HSI). Three traditional Japanese pure bast fibers (Kozo, Mitsumata, and Gampi) were analyzed as standard samples. Raw relative reflectance spectra from microscopic regions of the fibers were extracted via Micro-HSI. Dynamic normalization and Savitzky–Golay first-derivative filtering were applied to suppress scattering and baseline drift. Principal component analysis (PCA) and linear discriminant analysis (LDA) were subsequently employed for dimensionality reduction and supervised classification. The results showed that while unsupervised PCA suffered from inter-class overlap due to shared cellulose-dominated structures, supervised LDA amplified weak chemical fingerprint differences, achieving complete class separation of the highly similar fibers. Analysis of the feature loadings confirmed that the classification relies on the visible-range reflectance baseline, lignin π→π∗ transition absorption (400–450 nm), and near-infrared O-H and C-H overtone vibrations (~835 nm). This proof-of-concept study demonstrates that combining Micro-HSI with chemometrics enables high-precision, non-destructive fiber separation while retaining rigorous physicochemical interpretability, thereby providing an optical reference baseline for future historical paper analysis.