Automatic preprocessing of laser-induced breakdown spectra using partial least squares regression and feed-forward artificial neural network: Applications to Earth and Mars data
Due to its relatively simple and versatile nature, laser-induced breakdown spectroscopy experiments can yield enormous amount of data that normally needs to be preprocessed to remove background signal, electron continuum, and noise, and for some applications, correct for the instrument response func...
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Published in | Spectrochimica acta. Part B: Atomic spectroscopy Vol. 171; p. 105930 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Oxford
Elsevier B.V
01.09.2020
Elsevier BV |
Subjects | |
Online Access | Get full text |
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Abstract | Due to its relatively simple and versatile nature, laser-induced breakdown spectroscopy experiments can yield enormous amount of data that normally needs to be preprocessed to remove background signal, electron continuum, and noise, and for some applications, correct for the instrument response function and normalize the signal prior to conducting spectroscopic analysis. In experiments where the focus is on the analysis of samples of similar composition, preprocessing can be repetitive and tedious. We show that preprocessing of such LIBS data can be performed in an automated or semi-automated manner using machine learning tools. To demonstrate this approach, we apply partial least squares regression and artificial neural networks on two laser-induced breakdown spectra datasets. The first dataset is used to develop predictive models for abundances of various elements in geological samples analyzed by a laboratory model of ChemCam. The second dataset consists of spectra obtained from ChemCam as it interrogates Martian targets. We show that using the two machine learning techniques, we can predict the preprocessed spectra of samples with a relatively high accuracy for both datasets.
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•Preprocessing of LIBS data acquired by Curiosity is performed using machine learning.•Partial least squares regression and artificial neural networks used to preprocess LIBS data.•Highly accurate predictions of preprocessed spectra acquired by Curiosity are obtained. |
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AbstractList | Due to its relatively simple and versatile nature, laser-induced breakdown spectroscopy experiments can yield enormous amount of data that normally needs to be preprocessed to remove background signal, electron continuum, and noise, and for some applications, correct for the instrument response function and normalize the signal prior to conducting spectroscopic analysis. In experiments where the focus is on the analysis of samples of similar composition, preprocessing can be repetitive and tedious. We show that preprocessing of such LIBS data can be performed in an automated or semi-automated manner using machine learning tools. To demonstrate this approach, we apply partial least squares regression and artificial neural networks on two laser-induced breakdown spectra datasets. The first dataset is used to develop predictive models for abundances of various elements in geological samples analyzed by a laboratory model of ChemCam. The second dataset consists of spectra obtained from ChemCam as it interrogates Martian targets. We show that using the two machine learning techniques, we can predict the preprocessed spectra of samples with a relatively high accuracy for both datasets. Due to its relatively simple and versatile nature, laser-induced breakdown spectroscopy experiments can yield enormous amount of data that normally needs to be preprocessed to remove background signal, electron continuum, and noise, and for some applications, correct for the instrument response function and normalize the signal prior to conducting spectroscopic analysis. In experiments where the focus is on the analysis of samples of similar composition, preprocessing can be repetitive and tedious. We show that preprocessing of such LIBS data can be performed in an automated or semi-automated manner using machine learning tools. To demonstrate this approach, we apply partial least squares regression and artificial neural networks on two laser-induced breakdown spectra datasets. The first dataset is used to develop predictive models for abundances of various elements in geological samples analyzed by a laboratory model of ChemCam. The second dataset consists of spectra obtained from ChemCam as it interrogates Martian targets. We show that using the two machine learning techniques, we can predict the preprocessed spectra of samples with a relatively high accuracy for both datasets. [Display omitted] •Preprocessing of LIBS data acquired by Curiosity is performed using machine learning.•Partial least squares regression and artificial neural networks used to preprocess LIBS data.•Highly accurate predictions of preprocessed spectra acquired by Curiosity are obtained. |
ArticleNumber | 105930 |
Author | Ewusi-Annan, Ebo Wiens, Roger C. Delapp, Dorothea M. Melikechi, Noureddine |
Author_xml | – sequence: 1 givenname: Ebo surname: Ewusi-Annan fullname: Ewusi-Annan, Ebo organization: Department of Physics and Applied Physics, Kennedy College of Sciences, University of Massachusetts Lowell, Lowell, MA 01854, USA – sequence: 2 givenname: Dorothea M. surname: Delapp fullname: Delapp, Dorothea M. organization: Los Alamos National Laboratory, Los Alamos, NM, USA – sequence: 3 givenname: Roger C. surname: Wiens fullname: Wiens, Roger C. organization: Los Alamos National Laboratory, Los Alamos, NM, USA – sequence: 4 givenname: Noureddine surname: Melikechi fullname: Melikechi, Noureddine email: noureddine_melikechi@uml.edu organization: Department of Physics and Applied Physics, Kennedy College of Sciences, University of Massachusetts Lowell, Lowell, MA 01854, USA |
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Spectrom. doi: 10.1039/C8JA00209F contributor: fullname: Képeš |
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Snippet | Due to its relatively simple and versatile nature, laser-induced breakdown spectroscopy experiments can yield enormous amount of data that normally needs to be... |
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SubjectTerms | Analytical methods Artificial neural network Artificial neural networks Automation Background noise ChemCam Datasets Geological samples Laser induced breakdown spectroscopy Lasers Learning algorithms Least squares method LIBS Machine learning Mars Neural networks PLS Prediction models Preprocessing Regression analysis Response functions Scale models Spectra Spectroscopy Spectrum analysis SuperCam |
Title | Automatic preprocessing of laser-induced breakdown spectra using partial least squares regression and feed-forward artificial neural network: Applications to Earth and Mars data |
URI | https://dx.doi.org/10.1016/j.sab.2020.105930 https://www.proquest.com/docview/2450516373 |
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