NIR spectroscopy in conjunction with multivariate analysis for non-destructive characterization of Xuan paper

Abstract In the process of conservation mounting, starch paste made from wheat flour is the glue of choice to paste reinforcing strips and backing papers, often Xuan paper, for mending and decorating aged and damaged paper-based Chinese artworks. To keep objects intact and select appropriate conservation materials, this research explored the applicability of near-infrared (NIR) spectroscopy coupled with multivariate analysis for the characterization of contemporary unsized Xuan paper with and without starch. In particular, partial least squares (PLS) regression was used to predict the degree of polymerization (DP) of the paper, i.e., one of the most important properties of paper materials, and principal component analysis (PCA) was used to detect starch and to distinguish between papers with different amounts of starch. Using 12 contemporary unsized Xuan papers, the NIR-PLS method for DP prediction was validated, and the best-performed model was generated using the logarithmic transformation of DP −1 as a response variable, with root mean square error of prediction (RMSEP) of DP 128. The NIR-PCA method was also found to be applicable to separate starch-free papers and papers treated with starch, and the results indicate that the higher the content of starch, the better PCA classification performs. This research provides supporting data for the non-destructive characterization of Xuan paper-based objects and differentiates between Xuan paper before and after treatment with starch.