Identification of ancient glass categories based on distance discriminant analysis

Abstract

It is crucial for archaeological investigations to identify the category of cultural relics by analyzing their chemical composition. This study analyzed the chemical composition distribution of glass cultural relics and applied distance discriminant analysis methods to classify them into two categories. Through stepwise regression, four key feature factors ({SiO}_{2}, {K}_{2}O, PbO, and the presence of weathering on the artifact's surface) were selected from a total of 15 features, including surface weathering. Aside from using columnar table analysis to determine weathering on the surface of the artifact and correlations between categories, and using Spearman correlation coefficients to select key feature factors such as {SiO}_{2}, {K}_{2}O, PbO, BaO, and ,SrO from 14 total feature factors (excluding weathering on the surface), we established a Mahalanobis distance discriminant model to differentiate unknown glass artifacts. Results indicate that Spearman-Mahalanobis distance discrimination outperformed stepwise regression-Mahalanobis distance discrimination, with an overall accuracy of 99.10% for the former and 98.69% for the latter in identifying high-potassium glass or lead-barium glass.