Innovative lithium prediction based on artificial neural networks and hyperspectral data: traditional v. active hyperspectral scanning systems at the Zinnwald deposit

To advance Europe's self-reliance in sustainably sourcing lithium, innovative exploration technologies that enhance the efficiency of both exploration and extraction are urgently required. Lithium's role in rechargeable batteries necessitates advanced methods to support domestic resource development and meet rising demand. This study evaluates artificial neural networks (ANN) combined with hyperspectral data for lithium prediction, comparing the HySpex Mjolnir S-620 sensor and the Technical Research Centre of Finland Oy's (VTT) novel active hyperspectral scanner (AHS). The AHS employs a broadband short-wave infrared supercontinuum light source, avoiding dependency on external illumination and overcoming limitations of traditional systems in underground settings. At Germany's Zinnwald/Cínovec greisen deposit, lithium prediction was achieved via ANN trained on portable X-ray fluorescence (pXRF)-derived rubidium data, a proxy for lithium in zinnwaldite-rich mineralization. Hyperspectral processing extracted diagnostic absorption features (e.g. band ratios, minimum wavelength mapping) and classified lithium-bearing minerals. The ANN model produced a high-resolution lithium concentration map, demonstrating the AHS's ability to enhance mine-face characterization for both detailed exploration and operational workflows, such as grade control. Integrating hyperspectral sensing and machine learning supports Europe's sustainable objectives by enabling more precise and efficient resource extraction, offering scalable solutions for modern mining. The AHS–ANN framework also holds potential for agriculture, environmental monitoring and defence, where rapid material characterization is paramount.