This study first developed non-destructive and accurate methods to predict the relative contents of mixed mineral pigments in ancient Chinese wall paintings using multiple spectroscopic techniques. The colorimetry, attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR), ultraviolet-visible-near-infrared (UV-Vis-NIR) spectroscopy, and Raman spectroscopy were employed. Analyses were conducted including color difference, spectral reflection, ATR FT-IR spectra, and Raman mapping for simulated samples (malachite-lazurite mixed with rabbit glue samples) before and after aging. Models were then established for predicting the relative pigment contents of samples using UV-Vis-NIR and ATR FT-IR spectral data with Beer-Lambert law, and mathematical methods comprising principal component analysis (PCA) and nonlinear curve fitting. In particular, PCA and empty modeling methods combined with non-negative partial least squares were developed to predict the relative pigment contents based on Raman mapping data. The results demonstrated that approaches comprising PCA, mathematical model, and empty modeling based on the spectral data were effective at predicting the relative pigment contents. The predicted results obtained using the mathematical model based on UV-Vis-NIR spectra had an error of about 2%, and the best prediction based on ATR FT-IR spectra had an error of <3.6% at 1041 cm-1. The errors for the predictions using PCA and empty modeling based on Raman mapping data were 0.01-9.30% and 0.28-7.15%, respectively. However, the predicted relative pigment contents obtained based on ATR FT-IR data combined with the Beer-Lambert law had higher errors. The findings of this study confirm the strong feasibility of using spectroscopic techniques for quantitatively analyzing mixed mineral pigments.
Keywords: Ancient Chinese wall paintings; Raman mapping; attenuated total reflection Fourier transform infrared spectroscopy; mixed mineral pigments; near-infrared; relative content prediction; ultraviolet–visible.