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

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, K, and L 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 R 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 (P → D), 515.3 (D → P), and 521.8 nm (D → P), and Zn at 481.1 nm (S → P), over 50 and 200 scans to visualize the element spatial distribution. Mapping is cross-validated using Pearson's correlation covering a 50 × 50 mm 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.