Chemical analyses on late antique glass finds from Histria, Romania

Bugoi, Roxana; Țârlea, Alexandra; Szilágyi, Veronika; Harsányi, Ildikó; Cliante, Laurenţiu; Kasztovszky, Zsolt

doi:10.1111/arcm.12732

Cited by 6 publications

(2 citation statements)

References 48 publications

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“…In this paper, we collected data related to glass products, classified them into high potassium glass and lead-barium glass [3], and conducted modeling analysis based on their related data. The weathering of the glass surface is analyzed in terms of type, decoration and color, respectively, and the classification rules of high potassium glass and lead-barium glass are found out, and the division methods and results are given, and finally the results are analyzed in terms of reasonableness and sensitivity [4].…”

Section: Introductionmentioning

confidence: 99%

Study on Chemical Composition Analysis and Methods of Ancient Glass Relics

An,

Si,

Cui

2023

HSET

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In this paper, the relationship between the surface weathering of glass artifacts and the type, decoration, and color of glass was visualized by using a columnar table analysis[1], and the relationship between the three factors on the surface weathering was tested separately for significance by using a chi-square test; in addition, a multi-factor ANOVA was used to analyze whether the three factors of the type, decoration, and color of glass and their interaction on the surface weathering of glass artifacts had The significant relationships were analyzed by multivariate ANOVA. Then, the relative contributions of different types of glass to the prediction accuracy of the model were ranked by importance according to the proportion of chemical components before and after weathering at each sample point through random forest, and the chemical components were selected according to the importance ranking, and after removing most of the unimportant chemical components, the final retained chemical components were used to construct the final random forest regression model to obtain the laws of their classification, and finally the corresponding The results were obtained. The chemical components such as calcium oxide, copper oxide, iron oxide, and nitrogen oxide were drawn for visual analysis according to their importance, and we compared the derived results with the known literature and found that they were consistent with the results classified by the known literature.

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Section: Introductionmentioning

confidence: 99%

Study on Chemical Composition Analysis and Methods of Ancient Glass Relics

An,

Si,

Cui

2023

HSET

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“…The results of elemental analysis have been used to derive ML-based models for the classification of glass fragments. − ,− Thus, Tallon–Ballesteros and Riquelme used several ML algorithms such as random forest (RF), Bayes classifiers, artificial neural networks (ANNs), and nearest-neighbor methods to classify glass fragments into six classes based on their elemental composition. Similarly, Park and Carriquiry determined that better associations between the specimen and source could be obtained with RF and Bayesian Additive Regression Trees than with other algorithms.…”

Section: Introductionmentioning

confidence: 99%

Toward Developing Techniques─Agnostic Machine Learning Classification Models for Forensically Relevant Glass Fragments

Kaspi

Israelsohn-Azulay²,

Zidon³

et al. 2022

J. Chem. Inf. Model.

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Glass fragments found in crime scenes may constitute important forensic evidence when properly analyzed, for example, to determine their origin. This analysis could be greatly helped by having a large and diverse database of glass fragments and by using it for constructing reliable machine learning (ML)-based glass classification models. Ideally, the samples that make up this database should be analyzed by a single accurate and standardized analytical technique. However, due to differences in equipment across laboratories, this is not feasible. With this in mind, in this work, we investigated if and how measurement performed at different laboratories on the same set of glass fragments could be combined in the context of ML. First, we demonstrated that elemental analysis methods such as particle-induced X-ray emission (PIXE), laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), scanning electron microscopy with energy-dispersive X-ray spectrometry (SEM-EDS), particle-induced Gamma-ray emission (PIGE), instrumental neutron activation analysis (INAA), and prompt Gamma-ray neutron activation analysis (PGAA) could each produce lab-specific ML-based classification models. Next, we determined rules for the successful combinations of data from different laboratories and techniques and demonstrated that when followed, they give rise to improved models, and conversely, poor combinations will lead to poor-performing models. Thus, the combination of PIXE and LA-ICP-MS improves the performances by ∼10–15%, while combining PGAA with other techniques provides poorer performances in comparison with the lab-specific models. Finally, we demonstrated that the poor performances of the SEM-EDS technique, still in use by law enforcement agencies, could be greatly improved by replacing SEM-EDS measurements for Fe and Ca by PIXE measurements for these elements. These findings suggest a process whereby forensic laboratories using different elemental analysis techniques could upload their data into a unified database and get reliable classification based on lab-agnostic models. This in turn brings us closer to a more exhaustive extraction of information from glass fragment evidence and furthermore may form the basis for international-wide collaboration between law enforcement agencies.

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Transparency at its best: neutrons probing archaeological glass finds from Romania

Bugoi,

Ţârlea,

Szilágyi

et al. 2024

Appl. Phys. A

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This paper demonstrates the suitability of Prompt Gamma Activation Analysis (PGAA) technique for the compositional study of ancient glass finds, illustrating this idea by presenting and commenting the chemical composition of 41 Roman glass fragments discovered in archaeological excavations at Tomis, Histria, and Beidaud, Romania. According to typological and stratigraphic criteria, most finds were dated to the 1st-4th centuries AD. The PGAA measurements were performed at the Budapest Neutron Centre (BNC), Hungary. Regardless of their color (or the lack of it), most of the analyzed vitreous items have compositional traits often encountered in coeval glass artefacts discovered at various sites all over the Roman Empire. The sample set turned out to include several examples of Foy Série 2.1 and a single HIMT glass item, in agreement with the later chronological attribution of the corresponding fragments. This study provides some information about the chemistry of the Roman glass finds unearthed during the archaeological research performed in the region of the western Black Sea coast.

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Chemical analyses on late antique glass finds from Histria, Romania

Cited by 6 publications

References 48 publications

Study on Chemical Composition Analysis and Methods of Ancient Glass Relics

Study on Chemical Composition Analysis and Methods of Ancient Glass Relics

Toward Developing Techniques─Agnostic Machine Learning Classification Models for Forensically Relevant Glass Fragments

Transparency at its best: neutrons probing archaeological glass finds from Romania

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