Ancient chinese glass heritage classification based on compositional data and machine learning

Ancient Chinese glass resembles foreign glass in appearance but differs in chemical composition, which is further altered by weathering, thereby complicating the classification of artefacts. According to classification information and compositional proportion data for a set of ancient glass samples, we applied compositional data analysis based on the centered log-ratio (CLR) transformation, combined with chi-square and Fisher’s exact tests, to investigate the relationships between surface weathering, glass type, emblazonry and color. Summary statistics, box plots, normality tests and two-sample t tests were used to compare chemical compositions before and after weathering and to estimate pre-weathering compositions from median ratios. Decision trees, logistic regression, support vector machines and random forests were then used to classify high-potassium and lead-barium glass, and ANOVA, significance tests and K-means clustering were used to divide their compositional sub-categories. The resulting models show robust classification performance and provide a reproducible, data-driven framework for the classification of ancient Chinese glass.