Non-destructive distinction between geogenic and anthropogenic calcite by Raman spectroscopy combined with machine learning workflow.

Here, we demonstrate, for the first time, the possibility of distinguishing between geogenic and anthropogenic calcite in a non-destructive and effective way. Geogenic calcite derives from natural sedimentary and metamorphic rocks whereas anthropogenic calcite is formed artificially due to the carbonation process in mortars and plaster lime binders. Currently, their distinction is a major unaddressed issue although it is crucial across several fields such as C dating of historical mortars to avoid contamination with carbonate aggregates, investigating the origins of pigments, and studying the origins of sediments, to name a few. In this paper, we address this unmet need combining high-resolution micro-Raman spectroscopy with data mining and machine learning methods. This approach provides an effective means of obtaining robust and representative Raman datasets from which samples' origins can be effectively deduced; moreover, a distinction between sedimentary and metamorphic calcite has been also highlighted. The samples, chemically identical, exhibit systematic and reliable differences in Raman band positions, band shape and intensity, which are likely related to the degree of structural order and polarization effects.