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Conventional versus AI-based spectral data processing and classification approaches to enhance LIBS's analytical

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Artificial Intelligence (AI) enhances Laser-Induced Breakdown Spectroscopy (LIBS) for forensic analysis. AI-driven LIBS significantly improves toner sample discrimination accuracy compared to traditional methods.

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Area of Science:

  • Forensic Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Laser-Induced Breakdown Spectroscopy (LIBS) is a valuable technique for material analysis.
  • Conventional methods for LIBS data processing, such as Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA), often require extensive user preprocessing.
  • There is a need for more efficient and accurate methods for LIBS data interpretation, particularly in forensic applications like sample discrimination.

Purpose of the Study:

  • To develop and evaluate a novel Artificial Intelligence (AI)-driven approach for processing and interpreting Laser-Induced Breakdown Spectroscopy (LIBS) data.
  • To compare the performance of the AI-developed method against conventional techniques (PCA, PLS-DA) for forensic toner sample discrimination.
  • To demonstrate the potential of AI in enhancing the accuracy and efficiency of spectroscopic analysis for forensic applications.

Main Methods:

  • A novel AI-developed method was proposed, integrating normalization, interpolation, and peak detection for automated LIBS spectral analysis.
  • The AI method was compared with conventional Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA).
  • Performance evaluation involved quantitative statistical analysis, including accuracy difference percentage, variance analysis, paired t-test, and cross-validation.

Main Results:

  • The AI-developed method demonstrated superior performance in discriminating between toner samples from various printer and photocopier brands and models.
  • The AI approach significantly improved accuracy compared to conventional LIBS data analysis methods.
  • The proposed method requires no user preprocessing, simplifying the LIBS spectral analysis workflow.

Conclusions:

  • Artificial Intelligence significantly enhances Laser-Induced Breakdown Spectroscopy for forensic applications, particularly in sample discrimination.
  • The novel AI-developed method offers increased efficiency and accuracy in analyzing LIBS data, eliminating the need for user preprocessing.
  • This study highlights the transformative potential of AI in advancing spectroscopic techniques for forensic science and other analytical fields.