Advancing automated mineral identification through LIBS imaging for lithium-bearing mineral species

• Tipo: Artigo em Revista Científica Internacional
• Data: 2025

Autores

Capela, D (Autor) Outra; Lopes, T (Autor) Outra; Dias, F (Autor) Outra; Ferreira, MFS (Autor) Outra; Teixeira, J (Autor) Outra; Alexandre Lima (Autor) FCUP; Jorge, PAS (Autor) FCUP; Silva, NA (Autor) Outra; Guimaraes, D (Autor) Outra

Revista

A Revista está pendente de validação pelos Serviços Administrativos.

Título: SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY

Vol. 223

ISSN: 0584-8547

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-017-GXY

DOI: 10.1016/j.sab.2024.107085

Abstract (EN): Mineral identification is a challenging task in geological sciences, which often implies multiple analyses of the physical and chemical properties of the samples for an accurate result. This task is particularly critical for the mining industry, where proper and fast mineral identification may translate into major efficiency and performance gains, such as in the case of the lithium mining industry. In this study, a mineral identification algorithm optimized for analyzing lithium-bearing samples using Laser-induced breakdown spectroscopy (LIBS) imaging, is put to the test with a set of representative samples. The algorithm incorporates advanced spectral processing techniques-baseline removal, Gaussian filtering, and data normalization-alongside unsupervised clustering to generate interpretable classification maps and auxiliary charts. These enhancements facilitate rapid and precise labelling of mineral compositions, significantly improving the interpretability and interactivity of the user interface. Extensive testing on diverse mineral samples with varying complexities confirmed the algorithm's robustness and broad applicability. Challenges related to sample granulometry and LIBS resolution were identified, suggesting future directions for optimizing system resolution to enhance classification accuracy in complex mineral matrices. The integration of this advanced algorithm with LIBS technology holds the potential to accelerate the mineral evaluation, paving the way for more efficient and sustainable mineral exploration.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9