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.

Accurate paper fiber identification is essential for cultural heritage conservation. Traditional staining methods are destructive, while macroscopic AI models often lack physicochemical interpretability. This study explores the feasibility of a non-destructive analytical approach using micro-hyperspectral imaging (Micro-HSI) to overcome both limitations. Three traditional Japanese bast fibers, Kozo, Mitsumata, and Gampi, were analyzed as standard reference samples. Relative reflectance spectra were extracted from microscopic fiber regions using Micro-HSI. Dynamic normalization and Savitzky–Golay first-derivative filtering were applied to suppress scattering effects and baseline drift. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied in parallel for dimensionality reduction and supervised classification, respectively. The results indicated that unsupervised PCA exhibited substantial inter-class overlap because of the shared cellulose matrix among the fiber types. In contrast, supervised LDA amplified subtle chemical differences and achieved clear separation among the three fibers. Feature-loading analysis indicated that the classification was mainly associated with visible range reflectance characteristics, lignin π→π* absorption bands in the 400–450 nm region, and near-infrared O−H and C−H overtone vibrations near 835 nm. Leave-One-Specimen-Out Cross-Validation yielded an overall accuracy of 77.8%, with error-free classification of Kozo (F1 = 1.00) and misclassification limited to the chemically similar Gampi and Mitsumata pair. This proof-of-concept study demonstrates that combining Micro-HSI with chemometric analysis enables non-destructive fiber discrimination while retaining physicochemically interpretable spectral features. The findings also establish a microscopic spectral reference framework for future non-destructive analysis of historical paper materials.