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Determination of Electrocution Using Fourier Transform Infrared Microspectroscopy and Machine Learning Algorithm.

Y Tuo1, S Y Li2, J Zhang2

  • 1School of Basic Medical Science, Shanghai University of Medicine & Health Science, Shanghai 201318, China.

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Summary
This summary is machine-generated.

Fourier transform infrared microspectroscopy (FTIR-MSP) combined with machine learning effectively distinguishes electrical damage, burns, and abrasions in pig skin. This novel method identifies characteristic markers for electrical injuries, aiding in electrocution identification.

Keywords:
forensic pathology; electric injuries; spectroscopy, Fourier transform infrared; machine learning; skin; pigs

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Area of Science:

  • Forensic Science
  • Biomedical Engineering
  • Spectroscopy

Background:

  • Distinguishing between electrical damage, burns, and abrasions is crucial in forensic investigations.
  • Traditional methods for identifying skin injuries can be limited in specificity.

Purpose of the Study:

  • To develop and validate a new method for identifying skin electrical marks.
  • To differentiate between electrical damage, burns, and abrasions using Fourier transform infrared microspectroscopy (FTIR-MSP) and machine learning.
  • To identify characteristic spectral markers of electrical injuries.

Main Methods:

  • Establishment of pig skin models for electrical damage, burns, and abrasions.
  • Morphological examination using Hematoxylin and Eosin (HE) staining.
  • Epidermal cell spectral analysis using FTIR-MS.
  • Classification analysis using Principal Component Analysis (PCA) and Partial Least Squares (PLS).
  • Model construction using Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM).
  • Characteristic marker selection via factor loading.

Main Results:

  • Epidermal cells in all injury groups showed polarization, most pronounced in electrical damage and burns.
  • PCA and PLS methods successfully differentiated between injury types.
  • LDA and SVM models demonstrated high accuracy in diagnosing different skin damages.
  • Significant differences in absorption peaks at 2923 cm⁻¹, 2854 cm⁻¹, 1623 cm⁻¹, and 1535 cm⁻¹ were observed.
  • Electrical injuries exhibited the highest peak intensity at 2923 cm⁻¹.

Conclusions:

  • FTIR-MSP combined with machine learning offers a novel approach for diagnosing skin electrical damage.
  • This technique provides a reliable method for identifying electrocution marks.
  • The study successfully identified characteristic spectral markers for electrical injuries.