Rapid elemental imaging of copper-bearing critical ores using laser-induced breakdown spectroscopy coupled with PCA and PLS-DA

Amir Fayyaz; Muhammad Waqas; Haroon Asghar; Rizwan Ahmed; Usman Liaqat; Kashif Naseem; Muhammad Aslam Baig

doi:10.1016/j.talanta.2025.128463

Rapid elemental imaging of copper-bearing critical ores using laser-induced breakdown spectroscopy coupled with PCA and PLS-DA

Talanta. 2025 Jun 19:296:128463. doi: 10.1016/j.talanta.2025.128463. Online ahead of print.

Authors

Amir Fayyaz¹, Muhammad Waqas², Haroon Asghar³, Rizwan Ahmed³, Usman Liaqat⁴, Kashif Naseem⁵, Muhammad Aslam Baig⁶

Affiliations

¹ National Centre for Physics, Quaid-i-Azam University Campus, Islamabad, 45320, Pakistan; Atomic and Molecular Physics Laboratory, Department of Physics, Quaid-i-Azam University, Islamabad, 45320, Pakistan. Electronic address: amirfayyaz@phys.qau.edu.pk.
² Department of Mining and Geological Engineering, The University of Arizona, Tucson, AZ, 85719, USA.
³ National Centre for Physics, Quaid-i-Azam University Campus, Islamabad, 45320, Pakistan.
⁴ School of Chemical and Materials Engineering (SCME), National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan.
⁵ School of Energy and Material Science, Hunan University of Humanities Science and Technology, Loudi, China.
⁶ National Centre for Physics, Quaid-i-Azam University Campus, Islamabad, 45320, Pakistan; Atomic and Molecular Physics Laboratory, Department of Physics, Quaid-i-Azam University, Islamabad, 45320, Pakistan.

PMID: 40555112
DOI: 10.1016/j.talanta.2025.128463

Abstract

This study presents the application of laser-induced breakdown spectroscopy (LIBS) for analyzing various copper-bearing critical ores with significant Cu concentrations. LIBS detected Cu as a base element, along with other minor elements including Al, C, Fe, Mg, Ni, Si, and Zn, under optimized experimental conditions that include 80 ± 0.3 mJ laser energy, 2 μs delay time, ∼500 μm spot size, and a 45° angle between the collecting lens and the sample surface. The energy-dispersive X-ray technique was employed to determine the elemental concentrations and spatial distributions within the sample, based on K_α, K_β, and L_α characteristic lines. Quantitative analysis in LIBS is challenging due to matrix effects on line intensities, often requiring matrix-matched standards; however, the multielemental quality of LIBS spectra enables the detection of matrix types for accurate classification. In this contribution, we applied an unsupervised principal component analysis (PCA) on pre-processed LIBS data to reduce dimensionality and visualize clusters, showing that the first three principal components (PCs) account for 97.9 % of the total variance (PC1: 69.8 %, PC2: 20.3 %, PC3: 7.8 %). Elliptical PCA clustering with a 96 % confidence interval was achieved using SIMCA. A supervised partial least squares-discrimination analysis model is used to identify the variables that contribute most to classification. The model yields cumulative X and Y variances of 97.86 % and 99.96 %, respectively, with an R² range of 0.83-0.99 across the first 6 factors. Furthermore, LIBS 2D mapping is carried out using Cu spectral lines at 510.6 (²P_3/2 → ²D_5/2), 515.3 (²D_3/2 → ²P_1/2), and 521.8 nm (²D_5/2 → ²P_3/2), and Zn at 481.1 nm (³S₁ → ³P₂), over 50 and 200 scans to visualize the element spatial distribution. Mapping is cross-validated using Pearson's correlation covering a 50 × 50 mm² area, achieving ∼150 μm spatial resolution and an average root mean PRESS of ∼94 % with a high correlation of ∼0.989. The results show the efficiency of LIBS integrated with multivariate methods for pattern recognition, classification, and spatial analysis in the exploration of copper ores.

Keywords: Copper ores; LIBS; LIBS 2D spatial mapping; Laser-induced breakdown spectroscopy; PCA; PLS-DA; Partial least squares-discrimination analysis; Principal component analysis.