Open AccessMachine learning efficiently corrects LIBS spectrum variation due to change of laser fluence
Author(s) -
Zengqi Yue,
Chen Sun,
Liang Gao,
Yuqing Zhang,
Sahar Shabbir,
Weijie Xu,
Mengting Wu,
Long Zou,
Yongqi Tan,
Fengye Chen,
Jianhua Yu
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.392176
Subject(s) - laser induced breakdown spectroscopy , fluence , optics , laser , materials science , laser ablation , spectroscopy , plasma , spectral line , range (aeronautics) , physics , quantum mechanics , astronomy , composite material
This work demonstrates the efficiency of machine learning in the correction of spectral intensity variations in laser-induced breakdown spectroscopy (LIBS) due to changes of the laser pulse energy, such changes can occur over a wide range, from 7.9 to 71.1 mJ in our experiment. The developed multivariate correction model led to a precise determination of the concentration of a minor element (magnesium for instance) in the samples (aluminum alloys in this work) with a precision of 6.3% (relative standard deviation, RSD) using the LIBS spectra affected by the laser pulse energy change. A comparison to the classical univariate corrections with laser pulse energy, total spectral intensity, ablation crater volume and plasma temperature, further highlights the significance of the developed method.