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Multi-information fusion of characteristic spectral lines for enhanced quantitative accuracy in laser-induced breakdown spectroscopy.

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Title: Multi-information fusion of characteristic spectral lines for enhanced quantitative accuracy in laser-induced breakdown spectroscopy.
Authors: Huang, Yutao1 (AUTHOR), Liu, Yuan1 (AUTHOR), Lin, Xiaomei1,2 (AUTHOR), Lin, Jingjun1,2 (AUTHOR) 1124270941@qq.com, Yang, Jiangfei1 (AUTHOR), Tan, Aiguo1 (AUTHOR), Ma, Keke1 (AUTHOR), Zhang, Hongxiang1 (AUTHOR), Ruan, Nanqi1 (AUTHOR)
Source: JAAS (Journal of Analytical Atomic Spectrometry). May2026, Vol. 41 Issue 5, p1883-1893. 11p.
Subjects: Laser-induced breakdown spectroscopy, Spectral lines, Quantitative research, Soil testing, Heavy metals, Multisensor data fusion, Support vector machines, Signal processing
Abstract: When employing Laser-Induced Breakdown Spectroscopy (LIBS) for the quantitative analysis of heavy metal elements in soil, conventional quantitative analysis methods typically rely solely on single spectral line intensity information. The quantitative results are prone to fluctuations in excitation source energy, environmental parameter variations, and self-absorption effects, resulting in relatively low detection accuracy. To overcome these limitations and improve the accuracy of quantitative analysis, this study first introduced a decision filter based on the Median Absolute Deviation (MAD) algorithm for data processing, thereby improving data reliability. Subsequently, by analyzing trends in peak intensity, Spectral Peak Area (SPA), and full width at half maximum (FWHM) of spectral lines as a function of elemental concentrations, and evaluating the linear fitting performance of these three distinct types of information, the relative advantages of various spectral line information in quantitative analysis were assessed based on excitation principles. Building on this, a nonlinear Support Vector Regression (SVR) model was employed to integrate intensity, SPA, and FWHM from characteristic spectral lines. This integration facilitated information complementarity, thereby improving the accuracy of quantitative analysis. Experimental results demonstrated that the nonlinear regression model, incorporating multiple spectral line information, significantly enhanced quantitative analysis accuracy compared with linear regression models utilizing only single types of spectral line information. Specifically, the root mean square error (RMSE) for chromium (Cr) and lead (Pb) was 0.036 wt% and 0.026 wt%, respectively, with an average relative error (ARE) of 8.624% and 5.733%. The fitting coefficients (R2) exceeded 0.98 for both elements. By integrating the multi-dimensional information of characteristic spectral lines and employing a nonlinear SVR model for calibration, this study effectively overcomes the limitations inherent in traditional quantitative analysis relying on single-peak information. This approach significantly enhances the accuracy and robustness of LIBS technology for quantitative detection of heavy metals in soil, providing an effective solution for high-accuracy, multi-information-fused LIBS quantitative analysis in complex environments. [ABSTRACT FROM AUTHOR]
Copyright of JAAS (Journal of Analytical Atomic Spectrometry) is the property of Royal Society of Chemistry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 193714414
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  Label: Title
  Group: Ti
  Data: Multi-information fusion of characteristic spectral lines for enhanced quantitative accuracy in laser-induced breakdown spectroscopy.
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  Data: <searchLink fieldCode="AR" term="%22Huang%2C+Yutao%22">Huang, Yutao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Yuan%22">Liu, Yuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Xiaomei%22">Lin, Xiaomei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Jingjun%22">Lin, Jingjun</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> 1124270941@qq.com</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Jiangfei%22">Yang, Jiangfei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tan%2C+Aiguo%22">Tan, Aiguo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Keke%22">Ma, Keke</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Hongxiang%22">Zhang, Hongxiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ruan%2C+Nanqi%22">Ruan, Nanqi</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22JAAS+%28Journal+of+Analytical+Atomic+Spectrometry%29%22">JAAS (Journal of Analytical Atomic Spectrometry)</searchLink>. May2026, Vol. 41 Issue 5, p1883-1893. 11p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Laser-induced+breakdown+spectroscopy%22">Laser-induced breakdown spectroscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Spectral+lines%22">Spectral lines</searchLink><br /><searchLink fieldCode="DE" term="%22Quantitative+research%22">Quantitative research</searchLink><br /><searchLink fieldCode="DE" term="%22Soil+testing%22">Soil testing</searchLink><br /><searchLink fieldCode="DE" term="%22Heavy+metals%22">Heavy metals</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: When employing Laser-Induced Breakdown Spectroscopy (LIBS) for the quantitative analysis of heavy metal elements in soil, conventional quantitative analysis methods typically rely solely on single spectral line intensity information. The quantitative results are prone to fluctuations in excitation source energy, environmental parameter variations, and self-absorption effects, resulting in relatively low detection accuracy. To overcome these limitations and improve the accuracy of quantitative analysis, this study first introduced a decision filter based on the Median Absolute Deviation (MAD) algorithm for data processing, thereby improving data reliability. Subsequently, by analyzing trends in peak intensity, Spectral Peak Area (SPA), and full width at half maximum (FWHM) of spectral lines as a function of elemental concentrations, and evaluating the linear fitting performance of these three distinct types of information, the relative advantages of various spectral line information in quantitative analysis were assessed based on excitation principles. Building on this, a nonlinear Support Vector Regression (SVR) model was employed to integrate intensity, SPA, and FWHM from characteristic spectral lines. This integration facilitated information complementarity, thereby improving the accuracy of quantitative analysis. Experimental results demonstrated that the nonlinear regression model, incorporating multiple spectral line information, significantly enhanced quantitative analysis accuracy compared with linear regression models utilizing only single types of spectral line information. Specifically, the root mean square error (RMSE) for chromium (Cr) and lead (Pb) was 0.036 wt% and 0.026 wt%, respectively, with an average relative error (ARE) of 8.624% and 5.733%. The fitting coefficients (R2) exceeded 0.98 for both elements. By integrating the multi-dimensional information of characteristic spectral lines and employing a nonlinear SVR model for calibration, this study effectively overcomes the limitations inherent in traditional quantitative analysis relying on single-peak information. This approach significantly enhances the accuracy and robustness of LIBS technology for quantitative detection of heavy metals in soil, providing an effective solution for high-accuracy, multi-information-fused LIBS quantitative analysis in complex environments. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of JAAS (Journal of Analytical Atomic Spectrometry) is the property of Royal Society of Chemistry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1039/d6ja00043f
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 1883
    Subjects:
      – SubjectFull: Laser-induced breakdown spectroscopy
        Type: general
      – SubjectFull: Spectral lines
        Type: general
      – SubjectFull: Quantitative research
        Type: general
      – SubjectFull: Soil testing
        Type: general
      – SubjectFull: Heavy metals
        Type: general
      – SubjectFull: Multisensor data fusion
        Type: general
      – SubjectFull: Support vector machines
        Type: general
      – SubjectFull: Signal processing
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      – TitleFull: Multi-information fusion of characteristic spectral lines for enhanced quantitative accuracy in laser-induced breakdown spectroscopy.
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            NameFull: Huang, Yutao
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            NameFull: Liu, Yuan
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            NameFull: Lin, Xiaomei
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            NameFull: Lin, Jingjun
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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              Value: 41
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