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全反射 X 射线荧光法与化学计量技术在考古陶瓷产地研究中的结合。

Combination of Total-Reflection X-Ray Fluorescence Method and Chemometric Techniques for Provenance Study of Archaeological Ceramics.

机构信息

Institute of the Earth's Crust, Siberian Branch of the Russian Academy of Sciences, 128 Lermontov St., 664033 Irkutsk, Russia.

Institute of Petroleum, Chemistry and Nanotechnologies, Kazan National Research Technological University, 68 Karl Marx St., 420015 Kazan, Russia.

出版信息

Molecules. 2023 Jan 21;28(3):1099. doi: 10.3390/molecules28031099.

Abstract

The provenance study of archaeological materials is an important step in understanding the cultural and economic life of ancient human communities. One of the most popular approaches in provenance studies is to obtain the chemical composition of material and process it with chemometric methods. In this paper, we describe a combination of the total-reflection X-ray fluorescence (TXRF) method and chemometric techniques (PCA, k-means cluster analysis, and SVM) to study Neolithic ceramic samples from eastern Siberia (Baikal region). A database of ceramic samples was created and included 10 elements/indicators for classification by geographical origin and ornamentation type. This study shows that PCA cannot be used as the primary method for provenance purposes, but can show some patterns in the data. SVM and k-means cluster analysis classified most of the ceramic samples by archaeological site and type with high accuracy. The application of chemometric techniques also showed the similarity of some samples found at sites located close to each other. A database created and processed by SVM or k-means cluster analysis methods can be supplemented with new samples and automatically classified.

摘要

考古材料的产地研究是了解古代人类社区文化和经济生活的重要步骤。产地研究中最流行的方法之一是获取材料的化学成分,并通过化学计量方法对其进行处理。在本文中,我们描述了一种组合使用全反射 X 射线荧光(TXRF)方法和化学计量技术(主成分分析、k-均值聚类分析和支持向量机)来研究西伯利亚东部(贝加尔地区)的新石器时代陶瓷样本。创建了一个陶瓷样本数据库,其中包含 10 个元素/指标,用于按地理起源和装饰类型进行分类。本研究表明,主成分分析不能作为主要的产地研究方法,但可以显示数据中的一些模式。支持向量机和 k-均值聚类分析可以高精度地根据考古地点和类型对大多数陶瓷样本进行分类。化学计量技术的应用还表明,一些在彼此靠近的地点发现的样本具有相似性。通过 SVM 或 k-均值聚类分析方法创建和处理的数据库可以补充新的样本并自动进行分类。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f158/9920330/f0043d96cd44/molecules-28-01099-g001.jpg

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