Determination of minor metal elements in steel using laser-induced breakdown spectroscopy combined with machine learning algorithms

Zhang, Yuqing; Sun, Chen; Gao, Liang; Yue, Zengqi; Shabbir, Sahar; Xu, Weijie; Wu, Mengting; Yu, Jianhua

doi:10.1016/j.sab.2020.105802

Cited by 47 publications

(20 citation statements)

References 35 publications

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“…A transfer learning model training algorithm was developed in this work on the basis of that used for machine learning model training presented in detail in our previous publication 13 and used for various application scenarios 12 , 29 – 33 . The flowchart of transfer learning model training is shown in Fig.…”

Section: Data Treatment Methodsmentioning

confidence: 99%

From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration

Sun

Tan

et al. 2021

Sci Rep

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With the ChemCam instrument, laser-induced breakdown spectroscopy (LIBS) has successively contributed to Mars exploration by determining the elemental compositions of soils, crusts, and rocks. The American Perseverance rover and the Chinese Zhurong rover respectively landed on Mars on February 18 and May 15, 2021, further increase the number of LIBS instruments on Mars. Such an unprecedented situation requires a reinforced research effort on the methods of LIBS spectral data analysis. Although the matrix effects correspond to a general issue in LIBS, they become accentuated in the case of rock analysis for Mars exploration, because of the large variation of rock compositions leading to the chemical matrix effect, and the difference in surface physical properties between laboratory standards (in pressed powder pellet, glass or ceramic) used to establish calibration models and natural rocks encountered on Mars, leading to the physical matrix effect. The chemical matrix effect has been tackled in the ChemCam project with large sets of laboratory standards offering a good representation of various compositions of Mars rocks. The present work more specifically deals with the physical matrix effect which is still lacking a satisfactory solution. The approach consists in introducing transfer learning in LIBS data treatment. For the specific application of total alkali-silica (TAS) classification of rocks (either with a polished surface or in the raw state), the results show a significant improvement in the ability to predict of pellet-based models when trained together with suitable information from rocks in a procedure of transfer learning. The correct TAS classification rate increases from 25% for polished rocks and 33.3% for raw rocks with a machine learning model, to 83.3% with a transfer learning model for both types of rock samples.

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Section: Data Treatment Methodsmentioning

confidence: 99%

From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration

Sun

Tan

et al. 2021

Sci Rep

Self Cite

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“…A transfer learning model training algorithm was developed in this work on the basis of that used for machine learning model training presented in detail in our previous publication 13 and used in various application scenarios. 12,[26][27][28][29][30] The flowchart of transfer learning model training is shown in Fig. 5.…”

Section: Transfer Learning-based Calibration Model Trainingmentioning

confidence: 99%

From Machine Learning to Transfer Learning in Laser-Induced Breakdown Spectroscopy: the Case of Rock Analysis for Mars Exploration

Sun¹,

Xu²,

Tan³

et al. 2021

Preprint

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With the ChemCam instrument, laser-induced breakdown spectroscopy (LIBS) has successively contributed to Mars exploration by determining elemental compositions of soils, crusts and rocks. American Perseverance landed since Feb 18, 2021 on Mars and Chinese Tianwen 1 planned for landing soon, further increase the number of LIBS instruments on Mars. Such unprecedented situation requires a reinforced research effort on the methods of LIBS spectral data treatment. Although the matrix effects correspond to a general issue in LIBS, they become accentuated in the case of rock analysis for Mars exploration, because of the large variation of rock compositions leading to the chemical matrix effect, and the difference in surface physical properties between laboratory standards (in pressed powder pellet, glass or ceramic) used to establish calibration models and natural rocks encountered on Mars, leading to the physical matrix effect. The chemical matrix effect has been tackled in the ChemCam project with large sets of laboratory standards offering a good representation of various compositions of Mars rocks. The present work more specifically deals with the physical matrix effect which is still expecting a satisfactory solution. The approach consists in introducing transfer learning in LIBS data treatment. For the specific application of total alkali-silica (TAS) classification of rocks (either with a polished surface or in the raw state), the results show a significant improvement of the prediction ability of pellet-based models when trained together with suitable information from rocks in a procedure of transfer learning. The correct TAS classification rate increases from 25% for polished rocks and 33.3% for raw rocks with a machine learning model, to 83.3% with a transfer learning model for the both types of rock samples.

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“…A training algorithm of the transfer learning model was developed in this work on the basis of that used for machine learning model training presented in detail in our previous publication 13 and used in various application scenarios. 12,[24][25][26][27] The flowchart of transfer learning model training is shown in Figure 5. We can distinguish 3 main steps: data formatting; model training by optimization through iteration loops and model validation.…”

Section: Transfer Learning-based Calibration Model Trainingmentioning

confidence: 99%

From Machine Learning to Transfer Learning in Laser-Induced Breakdown Spectroscopy: the Case of Rock Analysis for Mars Exploration

Sun,

Xu,

Tan

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

With the ChemCam instrument, laser-induced breakdown spectroscopy (LIBS) has successively contributed to Mars exploration by determining elemental compositions of the soil, crust and rocks. Two new lunched missions, Chinese Tianwen 1 and American Perseverance, will further increase the number of LIBS instruments on Mars after the planned landings in spring 2021. Such unprecedented situation requires a reinforced research effort on the methods of LIBS spectral data treatment. Although the matrix effects correspond to a general issue in LIBS, they become accentuated in the case of rock analysis for Mars exploration, because of the large variation of rock composition leading to the chemical matrix effect, and the difference in morphology between laboratory standard samples (in pressed pellet, glass or ceramics) used to establish calibration models and natural rocks encountered on Mars, leading to the physical matric effect. The chemical matrix effect has been tackled in the ChemCam project with large sets of laboratory standard samples offering a good representation of various 1

show abstract

Determination of minor metal elements in steel using laser-induced breakdown spectroscopy combined with machine learning algorithms

Cited by 47 publications

References 35 publications

From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration

From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration

From Machine Learning to Transfer Learning in Laser-Induced Breakdown Spectroscopy: the Case of Rock Analysis for Mars Exploration

From Machine Learning to Transfer Learning in Laser-Induced Breakdown Spectroscopy: the Case of Rock Analysis for Mars Exploration

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