Convolutional Neural Networks Applied to Antimony Quantification via Soil Laboratory Reflectance Spectroscopy in Northern Portugal: Opportunities and Challenges

Abstract

Antimony (Sb) has gained significance as a critical raw material (CRM) within the European Union (EU) due to its strategic importance in various industrial sectors, particularly in the textile industry for flame retardants and as a component of Sb-based semiconductor materials. Moreover, Sb is emerging as a potential alternative for anodes used in lithium-ion batteries, a key element in the energy transition. This study explored the feasibility of identifying and quantifying Sb mineralisations through the spectral signature of soils using laboratory reflectance spectroscopy, a non-invasive remote sensing technique, and by employing convolutional neural networks (CNNs). Standard signal pre-processing techniques were applied to the spectral data, and the soils were analysed by inductively coupled plasma mass spectrometry (ICP-MS). Despite achieving high R-squared (0.7) values and an RMSE of 173 ppm for Sb, the study faces a significant challenge of generalisation of the model to new data. Despite the limitations, this study provides valuable insights into potential strategies for future research in this field.