Improving spatially-resolved lithium quantification in drill core samples of spodumene pegmatite by using laser-induced breakdown spectroscopy and pixel-matched reference areas

• Published in: Journal of geochemical exploration Vol. 250; p. 107235
• Main Authors: Müller, Simon, Meima, Jeannet A., Gäbler, Hans-Eike
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2023
• Subjects: Core scanner; Laser ablation-inductively coupled plasma-time of flight mass spectrometry (LA-ICP-TOFMS); Laser-induced breakdown spectroscopy (LIBS); Least-Square Support Vector Machines (LS-SVM); Li-bearing Spodumene Pegmatite; Spatial quantification; Spatial quantification; Least-Square Support Vector Machines (LS-SVM); Laser ablation-inductively coupled plasma-time of flight mass spectrometry (LA-ICP-TOFMS); Li-bearing Spodumene Pegmatite; Core scanner; Laser-induced breakdown spectroscopy (LIBS)
• DOI: 10.1016/j.gexplo.2023.107235
• ISSN: 0375-6742

Laser ablation-inductively coupled plasma-time of flight mass spectrometry (LA-ICP-TOFMS) concentrations were used to develop accurate calibration models for laser-induced breakdown spectroscopy (LIBS) mappings of pegmatitic drill cores samples. Both methods were applied on the same area of drill core samples, providing two spatially-resolved datasets for this area. The datasets were aligned pixel by pixel to create a pixel-matched reference area that covered the heterogeneity of the complete drill core. This way, different matrix effects affecting LIBS intensities could be taken into account and accurate spatial quantification of Li2O, SiO2, Al2O3, Na2O, and K2O from LIBS measurements was enabled. In particular, LIBS intensities and LA-ICP-TOFMS concentrations of individual pixels of the reference area were used as the input for a linear Partial Least Square Regression (PLSR) and a non-linear Least Square Support Vector Machines (LS-SVM) calibration model. Varying numbers between 100 and 2000 pixels were used for model creation, and root mean square error (RMSE) and R2 of each model were compared. Better values were achieved for the LS-SVM calibration model. Based on these results, the PLSR model was discarded and only the LS-SVM model with 1000 train pixels was further validated. For two different validation areas, LA-ICP-TOFMS concentrations were compared to LIBS-based concentrations obtained from the LS-SVM calibration model. The spatially-resolved quantification results of the LIBS data agree very well with the independently analysed LA-ICP-TOFMS concentrations, which is e.g. reflected in R2 values between 0.83 and 0.96 (mean 0.89) for Li2O, SiO2, Al2O3, Na2O, and K2O. In general, it was shown that the combination of LA-ICP-TOFMS and LIBS yields a comprehensive dataset for robust multivariate calibration. This enables spatially-resolved LIBS-based quantification of pegmatite samples that are subject to significant physical and chemical matrix effects. •Quantified distributions of Li, Si, Al, Na, and K are obtained from LIBS mappings.•LA-ICP-TOFMS is used to create a pixel-matched reference sample for quantification.•Calibration model for LIBS for spatially-resolved quantification of pegmatites•Pixel-matched reference samples overcome LIBS matrix effects of pegmatitic material.