Support Vector Machine Approach to the Spectroscopic Classification of Archaeological Bitumen Composites in Ancient Mesopotamia

In ancient civilisations, bitumen was widely used for its multifunctional applications in construction, sealing, and adhesion, evidencing early expertise in material engineering and resource optimisation. Here, Sumerian bitumen-based artefacts were studied through Fourier transform infrared spectroscopy (FTIR) and machine learning to investigate ancient practices for the repair, reuse, and recycling of everyday materials. The materials are dated back to the 3rd millennium BC and come from the archaeological site of Abu Tbeirah (Iraq). Four primary classes were identified based on their molecular composition, which revealed a specific gradient determined by the varying proportions of bitumen and other fillers. These composition-based classes were then applied to predict the classification of the undetermined samples, which constitute 50% of the entire dataset, via a kernel-based support vector machine (SVM). The new findings are consistent with philological sources that reference distinct formulations of use in everyday life. The findings offer a new perspective on the social and historical importance of the circular economy.