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Classification of Copper Minerals by Handheld Laser-Induced Breakdown Spectroscopy and Nonnegative Tensor

Michał Wójcik1, Pia Brinkmann2, Rafał Zdunek1

  • 1Department of Field Theory, Electronic Circuits and Optoelectronics, Faculty of Electronics, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50370 Wroclaw, Poland.

Sensors (Basel, Switzerland)
|September 12, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method combining handheld Laser-Induced Breakdown Spectroscopy (LIBS) with non-negative tensor factorization for classifying copper minerals. This approach improves classification accuracy in portable devices.

Keywords:
HALSLIBSNTFclassificationcopper minerals

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Area of Science:

  • Analytical Chemistry
  • Geoscience
  • Spectroscopy

Background:

  • Laser-Induced Breakdown Spectroscopy (LIBS) is increasingly used for material classification.
  • Current high-accuracy LIBS often requires bulky, non-portable equipment and intensive computation.
  • There is a need for compact, portable LIBS for field applications like mining, despite hardware limitations.

Purpose of the Study:

  • To develop and evaluate a novel approach for classifying copper minerals using a handheld LIBS device.
  • To overcome the limitations of compact LIBS hardware through advanced software techniques.
  • To enhance the accuracy of material classification in challenging, hard-to-access environments.

Main Methods:

  • A handheld Laser-Induced Breakdown Spectroscopy (LIBS) system was employed.
  • Non-negative tensor factorization was utilized for spectral data analysis.
  • Source spectra extraction and labelling based on percentage contribution were performed.
  • A regression model was used for validation, comparing results with traditional classifiers.

Main Results:

  • The proposed method successfully classified copper minerals using handheld LIBS.
  • Non-negative tensor factorization enabled effective spectral data decomposition.
  • The novel approach achieved approximately a 5% increase in classification accuracy.
  • Performance surpassed that of conventional classifiers like SVM, LDA, and k-NN.

Conclusions:

  • Handheld LIBS combined with non-negative tensor factorization offers a viable solution for accurate mineral classification in the field.
  • Software-based improvements, like tensor methods, can compensate for hardware limitations in portable spectroscopic devices.
  • This approach provides a pathway for more efficient and accurate material analysis in remote or difficult-to-access locations.