Composition Analysis and Identification of Ancient Glass Products based on Correlation Analysis and Linear Regression

Authors

  • Yueyang Wu
  • Yonglin Zou
  • Ziqi Liao
  • Zhi Li

DOI:

https://doi.org/10.54691/sjt.v4i11.2948

Keywords:

Glass Relics; Model; Weathering; Composition.

Abstract

Ancient glass is very easy to weathering in the process of long-time burial, resulting in the change of its composition, and then affect the identification of glass cultural relics. Therefore, in this paper, Spearman correlation analysis is used to solve the composition analysis of ancient glass products. Descriptive statistical methods and Spearman correlation analysis were used to study the statistical rule of the chemical composition content of cultural relics samples. In addition, mathematical models were established to predict the chemical composition content before and after weathering using the average value and content sum.

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References

WANG Chengyu; TAO Ying. THE WEATHERING OF SILICATE GLASSES [J]. Journal of The Chinese Ceramic Society,2003(1): 78-85.

Fuxi Gan. SOME CONSIDERIATIONS ABOUT RESEARCH OF CHINESE ANCIENT GLASSES[J]. Journal of the Chinese Ceramic Society,2004(02):182-188.

LI Qinghui; GAN Fuxi; GU Donghong. Some Questions Related to the Research of Ancient Chinese Glasses [J]. Studies in the History of Natural Sciences,2007(02):234-247.

ZHANG Liyan; LI Hong; CHEN Shubin; LI Zhongdi; RUAN Minzhi; XUE Tianfeng; QIAN Min; FAN Sijun. Simulation Methods of Glass Composition and Properties: A Short Review [J]. Journal of the Chinese Ceramic Society,2022,50(08):2338-2350.

WANG Xiaoyan; LI Meizhou. The Relationship of Rank Correlation Coefficient and Spearman Rank Correlation Coefficient [J]. Journal of Guangdong Industry Technical College,2006(04):26-27.

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Published

2022-11-22

Issue

Section

Articles

How to Cite

Wu, Y., Zou, Y., Liao, Z., & Li, Z. (2022). Composition Analysis and Identification of Ancient Glass Products based on Correlation Analysis and Linear Regression. Scientific Journal of Technology, 4(11), 107-113. https://doi.org/10.54691/sjt.v4i11.2948