Non-destructive prediction and pixel-level visualization of polysaccharide-based properties in ancient paper using...

Ancient documents and artworks are invaluable cultural heritage artworks that require careful preservation. Traditional methods for assessing their physical and chemical properties-such as tearing index, tensile index, water absorption, and pH-are often destructive, risking irreversible damage. This study introduces a novel, non-destructive approach using Short-Wave Near-Infrared (SWNIR) hyperspectral imaging (HSI) combined with advanced machine learning models. By integrating spectral preprocessing, feature selection, and machine learning techniques-including Back Propagation Neural Networks (BPNN), Long Short-Term Memory Networks (LSTM), and Convolutional Neural Networks (CNN)-with Sparrow Search Algorithm (SSA) optimization and Gray Level Co-occurrence Matrix (GLCM) texture feature extraction, the resulting SSA-BP-UVE-GLCM model achieved high predictive accuracy (R² ≥ 0.98). This framework enables precise, pixel-level predictions of paper properties, influenced by polysaccharides like cellulose, offering a non-invasive analysis that supports targeted restoration strategies and advances the conservation of cultural heritage. The findings enhance non-invasive testing and classification methods for polysaccharide-based materials, providing a foundation for further exploration of environmental impacts on artwork integrity using sophisticated machine learning algorithms.