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Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil
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Chromium in soil detection using adaptive weighted normalization and linear weighted network framework for LIBS

Xiaolong Li1, Jing Huang1, Rongqin Chen1

  • 1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.

Journal of Hazardous Materials
|February 4, 2023
PubMed
Summary

This study introduces advanced machine learning methods, linear weighted network (LWNet) and adaptive weighted normalization-LWNet (AWN-LWNet), for precise chromium detection in agricultural soils using laser-induced breakdown spectroscopy (LIBS). These techniques significantly reduce uncertainty and matrix effects, enabling accurate soil pollution assessment.

Keywords:
Laser-induced breakdown spectroscopyMatrix effectRapid detectionSoil hazardous metal

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Accurate detection of soil chromium (Cr) is crucial for assessing agricultural soil pollution.
  • Conventional chemical methods for hazardous metal analysis are time-consuming and involve sample destruction.
  • Laser-induced breakdown spectroscopy (LIBS) offers a rapid, chemical-free alternative but faces challenges with uncertainty and matrix effects.

Purpose of the Study:

  • To develop and evaluate novel machine learning strategies for enhancing the accuracy of LIBS-based chromium detection in soils.
  • To address and mitigate the inherent uncertainty and matrix effects in LIBS soil analysis.
  • To improve the efficiency and interpretability of LIBS for hazardous metal detection in diverse soil types.

Main Methods:

  • An average strategy combined with a linear weighted network (LWNet) was employed to reduce spectral uncertainty.
  • An adaptive weighted normalization-LWNet (AWN-LWNet) framework was developed to minimize matrix effects in different soil types.
  • Spectral variables were significantly reduced by the AWN-LWNet model, from 22016 to 72, for improved efficiency.

Main Results:

  • LWNet demonstrated superior performance over traditional machine learning methods, achieving average relative errors (ARE) of 2.08% and 3.03% for yellow brown and lateritic red soils, respectively.
  • AWN-LWNet proved to be the optimal model for reducing matrix effects, with an ARE of 4.12%.
  • The study successfully identified and extracted key chromium feature peaks, facilitating spectral interpretation and intuitive observation of intensity differences.

Conclusions:

  • The proposed LWNet and AWN-LWNet methods significantly improve the accuracy and reliability of laser-induced breakdown spectroscopy for chromium detection in agricultural soils.
  • These advanced machine learning frameworks effectively address the challenges of uncertainty and matrix effects, paving the way for rapid and precise soil pollution assessment.
  • The developed techniques show strong potential for the routine detection of hazardous metals in soils using LIBS, offering a more efficient and interpretable analytical approach.