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Machine Learning Allows Calibration Models to Predict Trace Element Concentration in Soils with Generalized LIBS

Chen Sun1, Ye Tian2, Liang Gao1

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This study introduces a machine learning model to improve trace element analysis in soils using laser-induced breakdown spectroscopy. The new multivariate approach effectively addresses matrix effects, enhancing analytical accuracy for soil composition determination.

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Area of Science:

  • Analytical Chemistry
  • Geochemistry
  • Spectroscopy

Background:

  • Matrix effects in soil analysis using laser-induced breakdown spectroscopy (LIBS) arise from variations in soil composition and physical properties.
  • Univariate models often yield suboptimal analytical performance for LIBS data due to these matrix effects.
  • Machine learning (ML) offers advanced algorithms for spectroscopic data analysis, surpassing traditional chemometric methods.

Purpose of the Study:

  • To develop a robust multivariate model for trace element determination in soils using LIBS.
  • To mitigate the significant impact of the soil matrix effect on analytical accuracy.
  • To enhance the performance of spectroscopic data treatment models through advanced ML techniques.

Main Methods:

  • Development of a multivariate model employing a back-propagation neural network (BPNN).
  • Introduction of the 'generalized spectrum' concept, incorporating soil matrix information as an input dimension.
  • Application of ML for feature selection and neural network training in spectroscopic data processing.

Main Results:

  • The developed multivariate model demonstrates significantly improved analytical performance compared to univariate approaches.
  • Average relative errors of calibration (REC) and prediction (REP) were achieved within the range of 5-6%.
  • The generalized spectrum approach effectively accounts for soil matrix variations, reducing analytical uncertainty.

Conclusions:

  • The proposed ML-based multivariate model offers a powerful solution for overcoming matrix effects in LIBS soil analysis.
  • The generalized spectrum concept provides a novel way to integrate matrix information into spectroscopic models.
  • This approach enhances the accuracy and reliability of trace element determination in diverse soil samples.