Identification of Mulberry Bast Fiber Using a Multivariate Analysis Technique

Abstract

Mulberry bast fiber, the primary raw material in Korean paper, exhibits superior crystallinity and polymerization of cellulose compared with wood fiber, contributing to the exceptional tensile strength and durability of Hanji, a traditional Korean paper made from mulberry paper. Therefore, Hanji is robust and has excellent preservation qualities. The expansion of the domestic conservation science industry and the efficient management of mulberry fiber supply and demand necessitate the adoption of non-destructive methods for analysis. This study proposes a taxonomic strategy for identifying the origins of paper mulberry bast fiber through non-destructive methods, paving the way for broader applications of this fiber. The mulberry bast fiber samples were categorized based on their origins and pulping methods. Infrared spectroscopy, coupled with unsupervised learning algorithms, including principal component analysis (PCA), multidimensional scaling (MDS), and K-means cluster analysis (KMCA), was employed for multivariate analysis. The findings demonstrate the accurate classification based on pulping methods. However, classification by country of origin was only partially successful. These outcomes suggest the potential for non-destructive analysis of national records using traditional Korean paper, contingent upon effective application of this algorithm.