  The value of luxury goods, particularly investment-grade gemstones, is
greatly influenced by their origin and authenticity, sometimes resulting in
differences worth millions of dollars. Traditionally, human experts have
determined the origin and detected treatments on gemstones through visual
inspections and a range of analytical methods. However, the interpretation of
the data can be subjective and time-consuming, resulting in inconsistencies. In
this study, we propose Gemtelligence, a novel approach based on deep learning
that enables accurate and consistent origin determination and treatment
detection. Gemtelligence comprises convolutional and attention-based neural
networks that process heterogeneous data types collected by multiple
instruments. Notably, the algorithm demonstrated comparable predictive
performance to expensive laser-ablation inductively-coupled-plasma
mass-spectrometry (ICP-MS) analysis and visual examination by human experts,
despite using input data from relatively inexpensive analytical methods. Our
innovative methodology represents a major breakthrough in the field of gemstone
analysis by significantly improving the automation and robustness of the entire
analytical process pipeline.
