Advanced spectral and image-based detection of rare earth elements using machine learning

The escalating global demand for rare earth elements (REEs) in green energy and advanced technologies has made efficient resource assessment more critical than ever. However, conventional laboratory characterization methods in geological samples, such as inductively coupled plasma mass spectrometry and x-ray fluorescence, are often destructive, time-consuming, and cost-intensive. Consequently, to accelerate exploration and processing pipelines, there is an urgent need to transition toward rapid, in-situ, and non-destructive analytical techniques for mineralogical screening. This review explores the paradigm shift brought about by synergizing modern spectroscopic methods and microchemical imaging with advanced machine learning (ML) and data-driven architectures. Spectroscopic methods evaluated include visible to shortwave infrared spectroscopy, laser-induced breakdown spectroscopy, and mass spectrometry. Microchemical imaging focuses on scanning electron microscopy coupled with energy dispersive x-ray spectroscopy to map complex mineral phases. Traditional mineralogical and spectral analyses of complex geological ores and secondary resources (e.g., coal fly ash, e-waste, and bauxites) are frequently hindered by severe matrix effects, elemental interferences, and dense spectral overlaps. Here, it is highlighted how intelligent algorithmic models effectively decode these high-dimensional mineralogical datasets. Rather than focusing on pure computational architecture, this paper emphasizes how AI-driven frameworks autonomously extract hidden geochemical correlations, suppress geological background noise, and identify multi-elemental patterns or microstructural mineral inclusions. Ultimately, the deployment of ML-empowered optical sensors and automated mineral tracker algorithms significantly improves the detection limits, phase quantification, and prediction accuracy for REEs. This integration establishes a robust foundation for autonomous mineral exploration, efficient metallurgical resource screening, and the advancement of processing technologies within the circular economy.