Molecular-Composition Analysis of Glass Chemical Composition Based on Time-Series and Clustering Methods

Zou, Ying

doi:10.3390/molecules28020853

Cited by 6 publications

(4 citation statements)

References 8 publications

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“… Zou (2023) used time series and clustering methods for molecular compositional analysis of glass chemical compositions. Ye et al (2015) investigated new gray prediction models to predict four typical interval gray number sequences separately.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction and classification of chemical composition of ancient glass objects based on generalized Shapley functions

Cai,

Yin,

Han

2024

Front. Chem.

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Ancient glass products have suffered from the baptism of time and experienced changes in the burial environment and weathering, resulting in a change in the proportions of their chemical composition and interfering with their accurate identification by later generations. In this paper, the chemical composition of ancient glass products is predicted and identified. First, the multivariate statistical ANOVA test is applied to explore the relationship between whether the cultural relics samples are weathered or not and the glass type, decoration, and color to derive a law of chemical composition of the cultural relics and to analyze the correlation and difference among the four factors. Second, compared with the relevant data of the existing glass products, the missing values are processed by using the method of filling in the plurality. The weathering condition of the sampling points of the samples whose surfaces are not weathered is judged by the “distance discrimination method.” Combined with the characteristics of the lead-barium glass and the high-potassium glass, the law of the chemical composition content on the surface of the samples, weathered or not, is explored. The modeling of the gray prediction method was applied again to predict the chemical composition content before weathering. Finally, the generalized Shapley function of fuzzy measurement was used to analyze the correlation between indicators and the chemical compositions and their differences. The scheme proposed in this paper can solve the difficult problem of category judgment in archeology, which is of great significance in promoting the smooth progress of archaeological work.

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Section: Literature Reviewmentioning

confidence: 99%

Prediction and classification of chemical composition of ancient glass objects based on generalized Shapley functions

Cai,

Yin,

Han

2024

Front. Chem.

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“…Thus, accurately predicting the chemical composition of glass before weathering is a prerequisite for achieving precise classification. Moreover, although several studies on the classification of glass types have been reported, for example, Zou et al [16] used one-way analysis of variance to subclassify glass artifacts. Li et al [17] employed a multivariate linear regression model for classifying unknown glass products.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

Chen,

Xu,

Zhang

et al. 2024

Applied Sciences

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Ancient glass artifacts were susceptible to weathering from the environment, causing changes in their chemical composition, which pose significant obstacles to the identification of glass products. Analyzing the chemical composition of ancient glass has been beneficial for evaluating their weathering status and proposing measures to reduce glass weathering. The objective of this study was to explore the optimal machine learning algorithm for glass type classification based on chemical composition. A set of glass artifact data including color, emblazonry, weathering, and chemical composition was employed and various methods including logistic regression and machine learning techniques were used. The results indicated that a significant correlation (p < 0.05) could only observed between surface weathering and the glass types (high-potassium and lead–barium). Based on the random forest and logistic regression models, the primary chemical components that signify glass types and weathering status were determined using PbO, K2O, BaO, SiO2, Al2O3, and P2O5. The random forest model presented a superior ability to identify glass types and weathering status, with a global accuracy of 96.3%. This study demonstrates the great potential of machine learning for glass chemical component estimation and glass type and weathering status identification, providing technical guidance for the appraisal of ancient glass artifacts.

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“…The purpose of this research is to analyze the statistical patterns and intrinsic relationships between the chemical compositions found in these two kinds of glasses, as well as to investigate and build prediction models between glass types and chemical compositions and to map their functional figures. In terms of research approach, this study employs a number of mathematical models for problem solving and model selection for each model's training effect before using the chosen model for prediction, analysis, and solution to improve the mathematical model's accuracy [2][3][4][5][6][7][8]. This is also the major focus of this article, which uses a portable X-ray fluorescence spectrometer to examine the chemical composition of ancient glass artifacts.…”

Section: Introductionmentioning

confidence: 99%

Chemical Composition Study of Ancient Glass Products Based on Machine Learning and Clustering Algorithm

He,

2024

Preprint

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The measurement of the chemical composition of ancient glass artifacts has far-reaching consequences for archaeological work and chemical research and has aroused scholars' curiosity. The goal of this paper is to understand the differences in chemical composition between different weathering types and different glass types, to discover statistical patterns of intrinsic chemical composition content, and to develop prediction and classification models between chemical composition and glass types and weathering types. To ensure the accuracy of the experimental data, this study evaluates many machine learning models and selects the one that best matches prediction and classification. Cluster analysis is then used to sub-classify the known glass kinds in order to provide the framework for future work on the categorization of glass goods. Neural networks were used with principal component analysis to create a mathematical model to generate a fitted function picture between glass kinds and chemical compositions. As a result, valuable suggestions for future chemical studies and archaeological investigations were made. Finally, glass product chemical composition correlations were examined to investigate the variations in chemical composition correlations across various categories.

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Molecular-Composition Analysis of Glass Chemical Composition Based on Time-Series and Clustering Methods

Cited by 6 publications

References 8 publications

Prediction and classification of chemical composition of ancient glass objects based on generalized Shapley functions

Prediction and classification of chemical composition of ancient glass objects based on generalized Shapley functions

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

Chemical Composition Study of Ancient Glass Products Based on Machine Learning and Clustering Algorithm

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