Sharing calibration information among laser-induced breakdown spectroscopy instruments using spectral line binning and calibration transfer

Abstract

The ability of a publicly accessible preexisting laser-induced breakdown spectroscopy (LIBS) database to train multivariate models to predict rock and mineral compositions from other laboratories or from remotely collected spectra is evaluated using LIBS spectra collected on >2500 unique geological targets using three different instruments and a range of collection protocols. Datasets collected under increasingly disparate conditions are utilized, including a single instrument with different resolution settings; two different instruments with very similar ablation and collection optics; and a benchtop instrument and portable instrument with different collection protocols, resolutions, and plasma conditions. Cross-calibration among datasets is performed for a range of scenarios designed to test the efficacy of post-processing techniques.Major element predictions are most accurate when instrument parameters match among training and test spectra. Use of a piecewise direct standardization-partial least squares (PDS-PLS) calibration transfer algorithm reduces major element prediction uncertainties when the resolution of training and test spectra do not match. Even when training and test spectra are collected on different instruments, reasonable predictions can be derived by binning peak areas prior to training calibration models. Finally, incorporating a large, preexisting database into a smaller dataset collected under test conditions has the potential to greatly improve the reliability of predicted compositions. With careful consideration to match plasma conditions and utilize post-processing, existing large-scale LIBS calibration databases like the one used in this study can be used to boost LIBS accuracy and expand the potential of LIBS as a widely-applicable quantitative geochemical tool.