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Quantitative methods for compensation of matrix effects in LIBS signals of solids

Quantitative methods for compensation of matrix effects in LIBS signals of solids
Quantitative methods for compensation of matrix effects in LIBS signals of solids
This paper reviews methods to compensate for matrix effects and self-absorption during quantitative analysis of compositions of solids measured using Laser Induced Breakdown Spectroscopy (LIBS) and their applications to in-situ analysis. Methods to reduce matrix and self-absorption effects on calibration curves are first introduced. The conditions where calibration curves are applicable to quantification of compositions of solid samples and their limitations are discussed. While calibration-free LIBS (CF-LIBS), which corrects matrix effects theoretically based on the Boltzmann distribution law and Saha equation, has been applied in a number of studies, requirements need to be satisfied for the calculation of chemical compositions to be valid. Also, peaks of all elements contained in the target need to be detected, which is a bottleneck for in-situ analysis of unknown materials. Multivariate analysis techniques are gaining momentum in LIBS analysis. Among the available techniques, principal component regression (PCR) analysis and partial least squares (PLS) regression analysis, which can extract related information to compositions from all spectral data, are widely established methods and have been applied to various fields including in-situ applications in air and for planetary explorations. Artificial neural networks (ANNs), where non-linear effects can be modelled, have also been investigated as a quantitative method and its application is introduced. The ability to make quantitative estimates based on LIBS signals is seen as a key element for the technique to gain wider acceptance as an analytical method, especially in in-situ applications. In order to accelerate this process, it is recommended that the accuracy should be described using common figures of merit which express the overall normalised accuracy, such as the normalised root mean square errors (NRMSE), when comparing the accuracy obtained from different setups and analytical methods.
0584-8547
31-42
Takahashi, Tomoko
937057f6-8e83-4a7f-b11f-b549c94afdf6
Thornton, Blair
8293beb5-c083-47e3-b5f0-d9c3cee14be9
Takahashi, Tomoko
937057f6-8e83-4a7f-b11f-b549c94afdf6
Thornton, Blair
8293beb5-c083-47e3-b5f0-d9c3cee14be9

Takahashi, Tomoko and Thornton, Blair (2017) Quantitative methods for compensation of matrix effects in LIBS signals of solids. Spectrochimica Acta Part B: Atomic Spectroscopy, 138, 31-42. (doi:10.1016/j.sab.2017.09.010).

Record type: Article

Abstract

This paper reviews methods to compensate for matrix effects and self-absorption during quantitative analysis of compositions of solids measured using Laser Induced Breakdown Spectroscopy (LIBS) and their applications to in-situ analysis. Methods to reduce matrix and self-absorption effects on calibration curves are first introduced. The conditions where calibration curves are applicable to quantification of compositions of solid samples and their limitations are discussed. While calibration-free LIBS (CF-LIBS), which corrects matrix effects theoretically based on the Boltzmann distribution law and Saha equation, has been applied in a number of studies, requirements need to be satisfied for the calculation of chemical compositions to be valid. Also, peaks of all elements contained in the target need to be detected, which is a bottleneck for in-situ analysis of unknown materials. Multivariate analysis techniques are gaining momentum in LIBS analysis. Among the available techniques, principal component regression (PCR) analysis and partial least squares (PLS) regression analysis, which can extract related information to compositions from all spectral data, are widely established methods and have been applied to various fields including in-situ applications in air and for planetary explorations. Artificial neural networks (ANNs), where non-linear effects can be modelled, have also been investigated as a quantitative method and its application is introduced. The ability to make quantitative estimates based on LIBS signals is seen as a key element for the technique to gain wider acceptance as an analytical method, especially in in-situ applications. In order to accelerate this process, it is recommended that the accuracy should be described using common figures of merit which express the overall normalised accuracy, such as the normalised root mean square errors (NRMSE), when comparing the accuracy obtained from different setups and analytical methods.

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Accepted/In Press date: 11 August 2017
e-pub ahead of print date: 18 September 2017
Published date: December 2017

Identifiers

Local EPrints ID: 414148
URI: http://eprints.soton.ac.uk/id/eprint/414148
ISSN: 0584-8547
PURE UUID: b70fecc1-f882-492c-98e9-75a707527a1e

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Date deposited: 15 Sep 2017 16:30
Last modified: 07 Oct 2020 05:01

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