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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2024

Autores

Carvalho, M (Autor) Outra; Cardoso-Fernandes, J (Autor) FCUP; Alexandre Lima (Autor) FCUP; Ana Teodoro (Autor) FCUP

Revista

Título: Remote Sensing

Vol. 16

Página Final: 1964

ISSN: 2072-4292

Editora: MDPI

Indexação

ISI Web of Knowledge - 4 Citações

Scopus - 4 Citações

Outras Informações

ID Authenticus: P-010-G5K

DOI: 10.3390/rs16111964

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 20