Unsupervised and interpretable discrimination of lithium-bearing minerals with Raman spectroscopy imaging

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Guimaraes, D (Author) Other; Monteiro, C (Author) Other; Teixeira, J (Author) Other; Lopes, T (Author) Other; Capela, D (Author) Other; Dias, F (Author) Other; Alexandre Lima (Author) FCUP; Jorge, PAS (Author) FCUP; Silva, NA (Author) Other

Journal

Title: Heliyon

Vol. 10

ISSN: 2405-8440

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-012-PK0

DOI: 10.1016/j.heliyon.2024.e35632

Abstract (EN): As lithium-bearing minerals become critical raw materials for the field of energy storage and advanced technologies, the development of tools to accurately identify and differentiate these minerals is becoming essential for efficient resource exploration, mining, and processing. Conventional methods for identifying ore minerals often depend on the subjective observation skills of experts, which can lead to errors, or on expensive and time-consuming techniques such as Inductively Coupled Plasma Mass Spectrometry (ICP-MS) or Optical Emission Spectroscopy (ICPOES). More recently, Raman Spectroscopy (RS) has emerged as a powerful tool for characterizing and identifying minerals due to its ability to provide detailed molecular information. This technique excels in scenarios where minerals have similar elemental content, such as petalite and spodumene, by offering distinct vibrational information that allows for clear differentiation between such minerals. Considering this case study and its particular relevance to the lithium- mining industry, this manuscript reports the development of an unsupervised methodology for lithium-mineral identification based on Raman Imaging. The deployed machine-learning solution provides accurate and interpretable results using the specific bands expected for each mineral. Furthermore, its robustness is tested with additional blind samples, providing insights into the unique spectral signatures and analytical features that enable reliable mineral identification.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11