PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Convolutional Neural Networks Applied to Antimony Quantification via Reflectance Spectroscopy Using Soils from Northern Portugal: Opportunities and Challenges
Version 1
: Received: 23 February 2024 / Approved: 26 February 2024 / Online: 26 February 2024 (11:43:43 CET)
How to cite:
Carvalho, M.; Cardoso-Fernandes, J.; Lima, A.; Teodoro, A.C. Convolutional Neural Networks Applied to Antimony Quantification via Reflectance Spectroscopy Using Soils from Northern Portugal: Opportunities and Challenges. Preprints2024, 2024021438. https://doi.org/10.20944/preprints202402.1438.v1
Carvalho, M.; Cardoso-Fernandes, J.; Lima, A.; Teodoro, A.C. Convolutional Neural Networks Applied to Antimony Quantification via Reflectance Spectroscopy Using Soils from Northern Portugal: Opportunities and Challenges. Preprints 2024, 2024021438. https://doi.org/10.20944/preprints202402.1438.v1
Carvalho, M.; Cardoso-Fernandes, J.; Lima, A.; Teodoro, A.C. Convolutional Neural Networks Applied to Antimony Quantification via Reflectance Spectroscopy Using Soils from Northern Portugal: Opportunities and Challenges. Preprints2024, 2024021438. https://doi.org/10.20944/preprints202402.1438.v1
APA Style
Carvalho, M., Cardoso-Fernandes, J., Lima, A., & Teodoro, A.C. (2024). Convolutional Neural Networks Applied to Antimony Quantification via Reflectance Spectroscopy Using Soils from Northern Portugal: Opportunities and Challenges. Preprints. https://doi.org/10.20944/preprints202402.1438.v1
Chicago/Turabian Style
Carvalho, M., Alexandre Lima and Ana Caudia Teodoro. 2024 "Convolutional Neural Networks Applied to Antimony Quantification via Reflectance Spectroscopy Using Soils from Northern Portugal: Opportunities and Challenges" Preprints. https://doi.org/10.20944/preprints202402.1438.v1
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 focused on exploring the feasibility of identifying and quantifying Sb mineralizations through the spectral signature of soils using reflectance spectroscopy, a non-invasive remote sensing technique, and by employing deep learning algorithms such as Convolutional Neural Networks (CNNs). Common signal preprocessing techniques were applied to the spectral data, and the soils were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Despite achieving high R-squared values, the study faces a significant challenge of generalization of the model to new data. Despite the limitations, this study provides valuable insights into potential strategies for future research in this field.
Keywords
Antimony; exploration; reflectance spectroscopy; soil analysis; critical raw materials; deep learning; geology
Subject
Environmental and Earth Sciences, Geophysics and Geology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
