Decoding Nacre: Uncovering Unique Identifiers of Pearl Provenance

Abstract Pearls are nacreous biogenic products that can be classified by whether they are natural or cultured, what species of mollusk produced them, and what environment they were grown in. Due to the subtle compositional and morphological differences between pearl types, determining pearl provenance can be problematic. To address these challenges, in this study we introduce a pearl identification workflow combining stable isotope analysis of pearl carbonate minerals to identify their geographic origin, and a Raman spectroscopy-based machine learning model to determine pearl species. Stable isotope data reveal clustering in δ18O based on the geographic origin of saltwater pearls. Additionally, pearl oyster shells from the Arabian Gulf were used as a tractable model system to investigate links between seawater geochemistry and carbonate stable isotope signatures. Paralleling these studies, Raman spectra of pearls formed by P. radiata, P. maxima, and P. fucata oysters were utilized to train a support vector machine classifier to predict pearl species with 96.4% accuracy. The combined results from these investigations demonstrate their utility in tracing pearl provenance by identifying geography and species of origin, which could be employed at a larger scale to improve pearl classification and provide insights into the formation mechanisms of biogenic carbonates.