Characterizing Malysheva Emeralds (Urals, Russia) by Microscopy, Spectroscopy, Trace Element Chemistry, and Machine Learning - Zheng, Yu-Yu; Yu, Xiao-Yan; Xu, Bo; Gao, Yu-Jie (Crystals (Basel), 2024)
The Malysheva emerald mine (Urals, Russia) boasts a long history and extraordinary emerald output. However, recent studies indicate that Malysheva emeralds share highly similar inclusion varieties, UV-visible-near infrared (UV-Vis-NIR) spectra, and compositional characteristics with other tectonic-magmatic-related (type I) emeralds from Zambia, Brazil, and Ethiopia. This similarity poses challenges for determination of the emeralds’ origin. This paper systematically investigates the microscopy, spectroscopy, and trace element chemistry of Malysheva emerald samples and compiles previously reported compositional data for the aforementioned Type I emeralds. Based on this dataset, principal component analysis (PCA) and machine learning methods are employed to construct models for emerald provenance discrimination. The results have updated the provenance characteristics of Malysheva emeralds, confirming the solid phase component of their three-phase inclusions as siderite and revealing two UV-Vis-NIR spectral patterns. Furthermore, the unique infrared absorptions related to HDO and D2O molecules within the 2600–2830 cm−1 range were discovered, which can be indicative of the origin of Malysheva. The prediction results of the machine learning model demonstrate an accuracy rate of 98.7%, and for an independent validation set of Malysheva emeralds, the prediction accuracy reached 100%. The feature importance ranking of the model highlights trace elements and parameters strongly correlated with the emeralds’ origin. These results illustrate the enormous potential of machine learning in the field of emerald origin determination, offering new insights into the traceability of precious gemstones.