Chemical composition analysis and type identification of ancient glass products
DOI:
https://doi.org/10.54097/hset.v52i.9602Keywords:
Chi-square test, Grey prediction, K-means cluster analysis, Logistic regression model, Canonical correlation analysis.Abstract
In this paper, the chemical composition analysis and type identification of ancient glass products are studied. Firstly, the qualitative analysis is changed into quantitative analysis, the type of glass, pattern and color are treated quantitatively, and the Chi-square test model is established. On this basis, the Chi-square cross thermal map is output to analyze the correlation degree of samples. The results of effect quantification are obtained. In order to study the division method and results of the internal chemical components of high potassium glass and lead barium glass, the principal component analysis method was adopted to reduce the dimension of the chemical components. Then train the data and build the model. This paper discusses the differences of correlation between chemical constituents of different types of cultural relics samples from the perspective of statistics.
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