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<p>The rapid and reliable in-situ detection of trace elements relevant to nuclear materials and metal elements in environmental matrices is a major challenge in the fields of environmental monitoring, nuclear safety, and resource assessment. Element identification in Laser-induced Plasma Spectroscopy (LIPS, synonymous with Laser-induced Breakdown Spectroscopy or LIBS) can be achieved by analyzing the corresponding peak information emitted by the plasma. However, due to differences in spectral databases (e.g., wavelength shift), making quick judgments remains challenging. To tackle this issue, we developed a novel data analysis strategy that explicitly accounts for spectral peak shifts. This method was evaluated using both the Akaike and Bayesian Information Criteria (AIC/ BIC) and correlation coefficients to enhance the reliability of LIPS for aerosol analysis. This approach statistically models inevitable spectral peak shifts to establish a probabilistic identification framework, offering clear evaluative criteria for detecting key elements in complex environmental samples. Experimental results demonstrate that this method substantially lowers misidentification risks and offers a robust analytical tool for field deployment and real-time environmental screening.</p> Graphical abstract

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Research on peak shift probability method based on laser-induced plasma spectroscopy

  • 期刊论文

  • 发表日期：

    2026年4月23日

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  • Yizhe Luo,
  • Chang Liu,
  • Jinshan Huang,
  • Qiguo Xiao,
  • Hongliang Li,
  • Hui Yang,
  • Jian Shan

摘要

The rapid and reliable in-situ detection of trace elements relevant to nuclear materials and metal elements in environmental matrices is a major challenge in the fields of environmental monitoring, nuclear safety, and resource assessment. Element identification in Laser-induced Plasma Spectroscopy (LIPS, synonymous with Laser-induced Breakdown Spectroscopy or LIBS) can be achieved by analyzing the corresponding peak information emitted by the plasma. However, due to differences in spectral databases (e.g., wavelength shift), making quick judgments remains challenging. To tackle this issue, we developed a novel data analysis strategy that explicitly accounts for spectral peak shifts. This method was evaluated using both the Akaike and Bayesian Information Criteria (AIC/ BIC) and correlation coefficients to enhance the reliability of LIPS for aerosol analysis. This approach statistically models inevitable spectral peak shifts to establish a probabilistic identification framework, offering clear evaluative criteria for detecting key elements in complex environmental samples. Experimental results demonstrate that this method substantially lowers misidentification risks and offers a robust analytical tool for field deployment and real-time environmental screening.

Graphical abstract