Code and data sharing are crucial practices to advance toward sustainable archaeology. This article explores the performance of supervised machine learning classification methods for provenancing archaeological pottery through the use of freeware R code in the form of R Markdown files. An illustrative example was used to show all the steps of the new methodology, starting from the requirements to its implementation, the verification of its classification capability and finally, the production of cluster predictions. The example confirms that supervised methods are able to distinguish classes with similar features, and provenancing is achievable. The provided code contains self-explanatory notes to guide the users through the classification algorithms. Archaeometrists without previous knowledge of R should be able to apply the novel methodology to similar well-constrained classification problems. Experienced users could fully exploit the code to set up different combinations of parameters, and they could further develop it by adding other classification algorithms to suit the requirements of diverse classification strategies.

The traditional pottery industry was an important activity in Catalonia (NE Spain) up to the 20th century. However, nowadays only few workshops persist in small villages were the activity is promoted as a touristic attraction. The preservation and promotion of traditional pottery in Catalonia is part of an ongoing strategy of tourism diversification that is revitalizing the sector. The production of authenticable local pottery handicrafts aims at attracting cultivated and high-purchasing power tourists. The present paper inspects several approaches to set up a scientific protocol based on the chemical composition of both raw materials and pottery. These could be used to develop a seal of quality and provenance to regulate the sector. Six Catalan villages with a renowned tradition of local pottery production have been selected. The chemical composition of their clays and the corresponding fired products has been obtained by Energy dispersive X-ray fluorescence (EDXRF). Using the obtained geochemical dataset, a number of unsupervised and supervised machine learning methods have been applied to test their applicability to define geochemical fingerprints that could allow inter-site discrimination. The unsupervised approach fails to distinguish samples from different provenances. These methods are only roughly able to divide the different provenances in two large groups defined by their different SiO₂ and CaCO₃ concentrations. In contrast, almost all the tested supervised methods allow inter-site discrimination with accuracy levels above 80%, and accuracies above 85% were obtained using a meta-model combining all the predictive supervised methods. The obtained results can be taken as encouraging and demonstrative of the potential of the supervised approach as a way to define geochemical fingerprints to track or attest the provenance of samples.

Archaeometric studies on mosaics often concentrate only on glass tesserae, while comprehensive studies including both stone and glass tesserae are scarce; however, both types of tesserae can sometimes bring relevant data to elaborate archaeological knowledge on a studied mosaic. In this paper, a representative set of tesserae from a large polychrome Roman mosaic retrieved in Barcelona (NE Spain) is investigated using various methods. Most of the techniques were directly applied on samples prepared as petrographic thin sections (including polarized-light, cathodoluminescence and electron microscopies, and synchrotron through-the-substrate μX-ray diffraction). The results indicate that, from the ten sampled stone tesserae, there are (i) seven limestones, one of them identified as Alveolina limestone (early Eocene) from the southern Pyrenees (ii) two sandstones from Barcelona’s Montjuïc hill (Miocene) and, (iii) a Carrara white marble from the Apuan Alps (Italy). The profuse presence of tesserae of both local and imported materials with well-known uses in architecture, epigraphy, and sculpture could imply that tesserae were a by-product of their main use. Two different production technologies were identified for the three sampled glass tesserae. The concurrent use of antimony- and tin-based opacifiers is in agreement with the accepted archaeological chronology of the mosaic (4th century AD).

A multi-analytical approach is usually applied in provenance studies of archaeological marbles. However, for very similar marble varieties, additional techniques and approaches are required. This paper uses a case study to illustrate this with two Catalan marble districts (Gualba and Ceret) and three sets of archaeological marbles. The common multi-method approach is unable to discriminate between the two districts, and such distinction is only partially glimpsed using unsupervised multivariate data analyses on a transformed geochemical dataset of reference samples. In contrast, several supervised classification models have been successfully trained to discriminate between the quarries without any special data transformation. All the trained models agree to assign the three sets of archaeological samples to the Gualba quarry district. Additional outcomes of the paper are a comprehensive archaeometric characterization of the little-known marbles of Gualba and Ceret and the first archaeometrically supported evidence of the use of Gualba marble during Roman and Medieval times.