Data from Multiple Portable XRF Units and Their Significance for Ancient Glass Studies

Yatsuk, Oleh; Ferretti, M.; Gorghinian, Astrik; Fiocco, Giacomo; Malagodi, Marco; Agostino, Angelo; Gulmini, Monica

doi:10.3390/molecules27186068

Cited by 14 publications

(4 citation statements)

References 55 publications

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“…The data obtained here from the p-XRF analyses are reported in Table S2 (Supporting Information), subdivided according to the groups indicated in Section 2.1. The expected accuracy has been discussed in [29] and is reported in Table 2. The elements detected sporadically in a few analytical spots are not included in Table S2.…”

Section: Portable X-ray Fluorescence Spectrometry (P-xrf)mentioning

confidence: 97%

“…The spectra were fitted with the PyMCA software (v. 5.6.7) [28] after creating a model with fundamental parameters, which was implemented (and verified) using the Corning Museum of Glass references A, B, and D, and several chips of archaeological glass of known composition. A separate paper is available with a more detailed description of the workflow [29]. The estimated accuracy and limit of quantification (LOQ) of the method are shown in Table 2.…”

Section: Portable X-ray Fluorescence (P-xrf)mentioning

confidence: 99%

“…It is generally accepted that, although the accuracy may not be ideal for other specific applications, p-XRF supports the classification of glass according to some major compositional differences, provided that an appropriate calibration is followed [29].…”

Section: Portable X-ray Fluorescence Spectrometry (P-xrf)mentioning

confidence: 99%

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The Non-Invasive Characterization of Iron Age Glass Finds from the “Gaetano Chierici” Collection in Reggio Emilia (Italy)

et al. 2023

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This work reports the results of the analyses performed on a set of glass finds from the “Gaetano Chierici” collection in the Civic Museums of Reggio Emilia. Forty-eight typologically heterogeneous glass finds were included in the list of the analyzed objects, dating from the fifth century BCE to the first century CE. The objects primarily consisted of glass beads; however, bangles, pinheads, and one vessel fragment were also included in the set. The items were analyzed using non-invasive spectroscopic methods that were implemented with portable equipment on the museum’s premises. Fiber optic reflectance spectroscopy was used to identify the coloring species in the glass matrix, while X-ray fluorescence spectrometry provided compositional information. Both techniques allowed for a discussion of the mechanisms of coloring and opacification, as well as the raw materials used for this purpose. The results provided a complex picture of the various colorants used to manipulate the appearance of the glass, which was a crucial aspect in the creation of the personal adornments represented by these glass items. The data revealed evidence of different sources of raw glass and colorants. Most of the samples were colored with cobalt, but iron and copper also influenced the color of many of them. Both white and yellow were utilized for the glass decorations, and these colors were achieved with the addition of either antimony-containing crystals or tin-containing ones. This finding suggests that the transition from antimonate to stannate started as early as the second century BCE. The “Gaetano Chierici” collection contains representative objects distributed throughout the investigated period. The results obtained here are a starting point for future studies of glass technology and provenance in the area.

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Section: Portable X-ray Fluorescence Spectrometry (P-xrf)mentioning

confidence: 97%

Section: Portable X-ray Fluorescence (P-xrf)mentioning

confidence: 99%

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The Non-Invasive Characterization of Iron Age Glass Finds from the “Gaetano Chierici” Collection in Reggio Emilia (Italy)

et al. 2023

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“…Logistic regression begins with the assumption that the data obeys the Bernoulli distribution [9], by constructing a maximum likelihood function and using the gradient descent method to obtain the parameters for the purpose of classifying the data. Logistic regression has the advantages of being easy to implement, easy to interpret and highly scale.…”

Section: Introductionmentioning

confidence: 99%

A study of ancient glass type prediction based on logistic regression models

Gong

2023

3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023)

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Ancient glasses identification and classification has been widely developed to investigate the ancient culture and historical issues. However, existing researches are concentrated on the classifications for ancient glass with unacceptable identification accuracy, which ignores the prediction method can also successful identify the types of ancient glass. Therefore, there is a demand for correctly predicting types of ancient glass with acceptable identification accuracy and reasonable system costs. Logistic regression models are statistical methods used to predict a binary (yes/no, 0/1) outcome based on one or more predictor variables. The aim of this paper is to make species predictions for ancient glass based on chemical composition distributions, and to determine whether an unknown glass sample is a high potassium or leadbarium glass. We collected data from 61 sets of samples, each corresponding to fourteen chemical compositions, and used a logistic regression algorithm to make species predictions for these sample glasses, solving for the parameters using Newton's method and gradient descent, and comparing them.

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It is not crystal clear: “nuances” in the selection of raw materials for Iron Age translucent glass revealed by chemical analyses of beads from central Italy

Yatsuk,

Koch,

Giannossa

et al. 2024

Archaeol Anthropol Sci

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The Iron Age was a period of change, with many innovations in the glass-making technology. The chemical composition of the set of objects considered in the present study demonstrates the diversity of the raw materials used and the depth of knowledge about the manipulation of glass appearance in the eighth-sixth centuries BCE. The study was carried out using fibre optics reflection spectroscopy and portable X-ray fluorescence spectrometry at the museums’ premises to examine a large number of glass beads and preliminarily group them on the basis of their composition and spectral characteristics. In addition, a smaller set of selected samples was analysed by laser ablation inductively coupled plasma mass spectrometry to provide a comprehensive chemical characterisation of the material. The compositional data indicated that the samples belonged to the high magnesium and low magnesium glass compositional types. Only one sample was recognised as low magnesium medium potassium glass. Glasses within each group were made from different sands, suggesting different provenances. Some of the samples were suggested to be of local origin, while the others were interpreted as imported glass. Evidence of glass colouring, decolouring and recycling are also discussed.

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Data from Multiple Portable XRF Units and Their Significance for Ancient Glass Studies

Cited by 14 publications

References 55 publications

The Non-Invasive Characterization of Iron Age Glass Finds from the “Gaetano Chierici” Collection in Reggio Emilia (Italy)

The Non-Invasive Characterization of Iron Age Glass Finds from the “Gaetano Chierici” Collection in Reggio Emilia (Italy)

A study of ancient glass type prediction based on logistic regression models

It is not crystal clear: “nuances” in the selection of raw materials for Iron Age translucent glass revealed by chemical analyses of beads from central Italy

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