• Multi-analytical study of 14th-century Königsfelden stained glass (Switzerland). • Combination of pXRF, LA-ICP-MS, OAS, Raman, and machine learning (GLORIA). • Predominant Central European signature with Rhenish and French contributions. • First evidence of Pb-rich green glasses in Swiss medieval stained glass. • Validates calibrated pXRF as a quantitative, non-destructive heritage tool. The chemical composition and optical properties of the stained-glass windows in the Königsfelden Abbey church (Aargau, Switzerland), which are among the oldest and best-preserved examples from the 14th century in the region, were analysed on site using portable X-ray fluorescence (pXRF) and optical absorption spectroscopy. A subset of loose fragments was further characterised by Raman spectroscopy and LA-ICP-MS, and these data were used to improve the calibration of the results obtained with in situ pXRF. A machine-learning classifier (GLORIA) was applied to assess the variability of the glasses and their provenance relative to a reference database of European stained-glass windows. The results demonstrate that the Königsfelden corpus consists of potash-based forest glasses, mostly attributed to Central Europe (59%), with additional inputs from the Rhine region (32%) and Northwestern France (9%). While the colouring mechanisms for cobalt blue, copper red, manganese purple, silver yellow and iron sulphide amber are relatively well understood, the colouring techniques of a distinctive bluish grey and rare Pb-rich green glass (∼10 wt% PbO) are unusual and have no clear published parallels. One hypothesis for the high PbO contents in green glass is that a CuO-PbO frit was deliberately employed to facilitate the dissolution of metallic copper in the silicate melt. The blue-grey glass, unique in hue and composition, appears to result from the combination of a manganese–cobalt glass with an additional, yet unidentified, contribution. Our study thus provides new insights into medieval glass recipes, trade networks, and workshop practices, while also validating the potential of calibrated pXRF combined with reference standards and machine learning for heritage science applications.
Langagne et al. (Sat,) studied this question.