Authentication and inference of seal stamps on Chinese traditional painting by using multivariate classification and near-infrared spectroscopy

Chinese traditional paintings occupy an important position in Chinese cultural heritage and it is very important for archeologist and artist to identify their authenticity, which is difficult to be realized. Near-infrared spectroscopy (NIRS) coupled with multivariate models was used for authenticating stamps of 12 seals on a Chinese traditional painting in this work. The robustness of linear and nonlinear multivariate models, i.e. partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM), were evaluated by adding 5 different levels of noise (from 1% to 5%) into 3 original NIR spectra of each the stamps. These spectral data with noise added were fused together with original spectra to establish identification models and then to evaluate the abilities of the two models to tolerate noise disturbance. Accuracies of 92.6% and 100% were yielded by linear PLS-DA and nonlinear SVM methods respectively. The results demonstrate the feasibility of multivariate approaches in authenticating stamps of seals on the Chinese traditional painting. It is also important and necessary to infer the approximate eras of seal stamps on Chinese traditional painting in archeological study. By comparing the Mahalanobis distances between the 12 stamps on the painting, hierarchical cluster analysis (HCA) was adopted to assist the inference of eras for those unknown seal stamps on the Chinese traditional painting. This work demonstrates that NIR spectroscopy combined with multivariate models can be utilized as a non-destructive approach for authentication of stamps on Chinese traditional painting. HCA can also provide useful information to speculate the time period of the stamps of unknown seals on the Chinese traditional painting.