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

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.

Archaeometallurgical characterization of the earliest European metal helmets.

Archaeometric analyses on conical and decorated cap helmets from the Bronze Age are presented. The helmets are dated to the 14-12th century BC according to associated finds in hoards. Alloy composition, material structure and manufacturing processes are determined and shed light on the earliest development of weaponry production in Central and Eastern Europe. Analyses were carried out using light and dark field microscopy, SEM-EDXS, PIXE, TOF-ND and PGAA. The results allowed reconstructing the manufacturing process, the differences between the cap of the helmets and their knobs (i.e. alloy composition) and the joining technique of the two parts.

Shedding Light on Roman Glass Consumption on the Western Coast of the Black Sea.

The chemical composition of 48 glass finds from Histria and Tomis, Romania, chiefly dated to the 1st-4th c. AD, was determined using prompt gamma activation analysis (PGAA) at the Budapest Neutron Centre (BNC). Most fragments have composition typical for the Roman naturally colored blue-green-yellow (RNCBGY) glass; Mn-colorless, Sb-colorless, and Sb-Mn colorless glass finds were evidenced, too. Several Foy Série 2.1 and Foy Série 3.2 glass fragments, as well as an HIMT and a plant ash glass sample, were identified in the studied assemblage. The archaeological evidence, the glass working waste items, and the samples with compositional patterns suggestive of recycling are proofs of the secondary glass working activities at Tomis during the Early Roman Empire period.

Hidden behind the mask: An authentication study on the Aztec mask of the Museum of Ethnography, Budapest, Hungary.

Turquoise covered mosaic objects - especially masks - were attractive components of treasures transported to Europe from Mexico after the fall of the Aztec Empire in the 1500s. According to our present knowledge, the mosaic masks were manufactured for ritual purpose. The main material of mosaics, the turquoise was a high-prestige semi-precious stone among Mexican native people. During the 20th century, such objects derived both from illegal treasure hunting and documented archaeological excavations. The aim of our research was the authentication of a turquoise covered Aztec wooden mask, which presumably originates from the Tehuacán Valley, Mexico and exchanged by the Museum of Ethnography, Budapest, in 1973. The detailed and complex analytical investigation of the mask is a curiosity. To reveal the origin of the object, UV photographs were taken, the wooden base was subjected to biological studies and C-14 dating, the organic glue fixing the tesserae and the inorganic mosaic tesserae were investigated by non-destructive chemical, FT-IR and Raman spectroscopic methods. Our investigations determined that the mask of the Museum of Ethnography was made of an alder species of tree and its age is AD 1492-1653. The light-coloured covering mosaic lamellae were identified as alabaster and claystone. Comparing the turquoise tesserae cover with reference materials, their chemical composition has been clearly differentiated from most of the well-known turquoise sources of the US Southwest. Based on our results, the Aztec mask of the Museum of Ethnography proved to be an original piece of art from the 15th-17th century.