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Classification of small specimen uranium ores using LIBS combined with machine learning and deep learning algorithms.

Jingrong Li1, Xiaoliang Liu1,2, Min Zhang1

  • 1Jiangxi Province Key Laboratory of Nuclear Physics and Technology, East China University of Technology, Nanchang, 330013, China. 201960177@ecut.edu.cn.

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This study introduces machine learning and deep learning models for classifying uranium ores using laser-induced breakdown spectroscopy (LIBS). Principal Component Analysis (PCA) with deep learning achieved 100% accuracy for small ore specimens.

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Area of Science:

  • Geochemistry
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Accurate classification of uranium ores is crucial for resource management and safety.
  • Traditional methods for analyzing small ore specimens can be time-consuming and less precise.
  • Laser-Induced Breakdown Spectroscopy (LIBS) offers rapid elemental analysis but faces challenges with small sample classification.

Purpose of the Study:

  • To develop and evaluate machine learning (ML) and deep learning (DL) classification models for small uranium ore specimens using LIBS data.
  • To compare the effectiveness of different feature extraction methods (LASSO, PCA) in conjunction with various ML/DL algorithms.
  • To establish a reliable technical pathway for rapid and high-precision identification of uranium ores.

Main Methods:

  • Collected LIBS spectral data from 12 types of uranium ore samples.
  • Preprocessed spectral data using Standard Normal Variate (SNV).
  • Constructed classification models using Random Forest (RF), Feedforward Neural Network (FNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) algorithms.
  • Employed Least Absolute Shrinkage and Selection Operator (LASSO) and Principal Component Analysis (PCA) for feature extraction.

Main Results:

  • Random Forest (RF) models showed significant overfitting with small training datasets.
  • Deep learning models combined with LASSO feature selection improved performance over RF but still had misclassifications.
  • Principal Component Analysis (PCA), using the first five components, effectively retained spectral discriminative information.
  • All deep learning models utilizing PCA features achieved 100% classification accuracy on both training and testing sets.

Conclusions:

  • PCA is effective in extracting global spectral information crucial for classifying small uranium ore specimens with LIBS.
  • Deep learning algorithms, when combined with PCA, significantly enhance classification performance and generalization ability.
  • This approach provides a robust method for the rapid and accurate identification of small uranium ore samples.