Towards robust calibration models for laser-induced breakdown spectroscopy using unsupervised clustered regression techniques

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Silva N.A. (Author) Other; Capela D. (Author) Other; Ferreira M. (Author) Other; Gonçalves F. (Author) Other; Lima A. (Author) Other; Guimarães D. (Author) Other; Jorge, PAS (Author) FCUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: Results in Optics

Vol. 9

ISSN: 2666-9501

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-00W-SHH

DOI: 10.1016/j.rio.2022.100245

Abstract (EN): One of the caveats of laser-induced breakdown spectroscopy technique is the performance for quantification purposes, in particular when the matrix of the sample is complex or the problem spans over a wide range of concentrations. These two questions are key issues for geology applications including ore grading in mining operations and typically lead to sub-optimal results. In this work, we present the implementation of a class of clustered regression calibration algorithms, that previously search the sample space looking for similar samples before employing a linear calibration model that is trained for that cluster. For a case study involving lithium quantification in three distinct exploration drills, the obtained results demonstrate that building local models can improve the performance of standard linear models in particular in the lower concentration region. Furthermore, we show that the models generalize well for unseen data of exploration drills on distinct rock veins, which can motivate not only further research on this class of methods but also technological applications for similar mining environments.

Language: English

Type (Professor's evaluation): Scientific