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Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection.

Ashwin Kumar Myakalwar1, Nicolas Spegazzini2, Chi Zhang3

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Feature selection in laser-induced breakdown spectroscopy (LIBS) improves material identification. A genetic algorithm approach enhanced classification accuracy for explosives detection, enabling more compact and field-operable systems.

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemometrics

Background:

  • Laser-induced breakdown spectroscopy (LIBS) offers advantages for material identification but faces challenges with classification model robustness due to spurious correlations.
  • Existing LIBS classification models often lack rigorous feature selection, hindering fundamental interpretation and potentially suffering from the curse of dimensionality.

Purpose of the Study:

  • To investigate feature selection approaches for robust LIBS-based material identification.
  • To develop and compare chemometric classifiers using different feature selection strategies for secondary explosives.

Main Methods:

  • Acquisition of LIBS data from secondary explosives.
  • Development of two chemometric classifiers: one using prior sample composition knowledge for feature selection, and another employing a genetic algorithm for feature selection.
  • Evaluation of classification performance using full spectral input versus selected spectral windows.

Main Results:

  • Full spectral input achieved approximately 92% classification accuracy.
  • Selecting only the carbon to hydrogen spectral window yielded similar performance to the full spectrum.
  • A genetic algorithm-derived classifier achieved a statistically significant improvement to approximately 94% accuracy with an order of magnitude fewer features.

Conclusions:

  • Rigorous feature selection significantly impacts LIBS performance for material identification.
  • The genetic algorithm approach offers a robust and efficient method for feature selection in LIBS.
  • The findings suggest the feasibility of developing cheaper, compact LIBS systems for field applications using discrete filter-based detectors.