A Novel Multivariate Model Based on Dominant Factor for Laser-induced Breakdown Spectroscopy Measurements

TL;DR

This paper introduces a new multivariate model combining dominant factor principles with PLS to improve elemental concentration measurement accuracy in LIBS, effectively modeling non-linear effects and reducing error.

Contribution

It proposes a novel multivariate model that integrates dominant factors based on physical principles with PLS, enhancing robustness and accuracy in LIBS analysis.

Findings

RMSEP reduced to 2.33% from 5.25%

Model effectively accounts for non-linear effects

Improves accuracy over conventional PLS

Abstract

This paper presents a new approach of applying partial least squares method combined with a physical principle based dominant factor. The characteristic line intensity of the specific element was taken to build up the dominant factor to reflect the major elemental concentration and partial least squares (PLS) approach was then applied to further improve the model accuracy. The deviation evolution of characteristic line intensity from the ideal condition was depicted and according to the deviation understanding, efforts were taken to model the non-linear self-absorption and inter-element interference effects to improve the accuracy of dominant factor model. With a dominant factor to carry the main quantitative information, the novel multivariate model combines advantages of both the conventional univariate and PLS models and partially avoids the overuse of the unrelated noise in the spectrum for PLS application. The dominant factor makes the combination model more robust over a wide concentration range and PLS application improves the model accuracy for samples with matrices within the calibration sample set. Results show that RMSEP of the final dominant factor based PLS model decreased to 2.33% from 5.25% when using the conventional PLS approach with full spectral information. Furthermore, with the development in understanding the physics of the laser-induced plasma, there is potential to easily improve the accuracy of the dominant factor model as well as the proposed novel multivariate model.