Quality Assessment of Papers Using Hyperspectral Imaging and Non-Imaging Techniques: A Review

Abstract

The preservation of both ancient and modern paper documents involves analytical procedures that can determine their status without physical destruction. This review assesses the use of Hyperspectral Non-Imaging Spectroscopy (HNIS), alongside Hyperspectral Imaging (HSI) and other spectroscopic methods, in the identification of paper composition, quality and deterioration. This work offers insights into the detection of chemical and structural variations like acidity, oxidation, fungal activity, and additive presence in non-contact analysis based on studies conducted between 2001 and 2025. HNIS offers a high spectral resolution and sensitivity to small chemical differences, and is suitable to delicate artefacts, whereas HSI can map large areas complementary to HNIS. The implementation of machine learning to predict degradation, classify, and estimate chronologically is also discussed in the article. Some of the most notable issues determined include the lack of common spectral reference collections, inadequate combination of real-time environmental information, and inadequate variance of training data. The offered HNIS-ML integration framework provides a scalable, non-invasive method of early damage detection and informed preservation planning in museums, libraries and archives.