Determination of Electrocution Using Fourier Transform Infrared Microspectroscopy and Machine Learning Algorithm.
10.12116/j.issn.1004-5619.2020.01.008
- Author:
Ya TUO
1
;
Shi Ying LI
2
;
Ji ZHANG
2
;
Kai Fei DENG
2
;
Yi Wen LUO
2
;
Qi Ran SUN
2
;
He Wen DONG
2
;
Ping HUANG
2
Author Information
1. School of Basic Medical Science, Shanghai University of Medicine & Health Science, Shanghai 201318, China.
2. Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
- Publication Type:Journal Article
- Keywords:
forensic pathology;
electric injuries;
spectroscopy, Fourier transform infrared;
machine learning;
skin;
pigs
- MeSH:
Algorithms;
Animals;
Fourier Analysis;
Least-Squares Analysis;
Machine Learning;
Swine
- From:
Journal of Forensic Medicine
2020;36(1):35-40
- CountryChina
- Language:English
-
Abstract:
Objective To analyze the differences among electrical damage, burns and abrasions in pig skin using Fourier transform infrared microspectroscopy （FTIR-MSP） combined with machine learning algorithm, to construct three kinds of skin injury determination models and select characteristic markers of electric injuries, in order to provide a new method for skin electric mark identification. Methods Models of electrical damage, burns and abrasions in pig skin were established. Morphological changes of different injuries were examined using traditional HE staining. The FTIR-MSP was used to detect the epidermal cell spectrum. Principal component method and partial least squares method were used to analyze the injury classification. Linear discriminant and support vector machine were used to construct the classification model, and factor loading was used to select the characteristic markers. Results Compared with the control group, the epidermal cells of the electrical damage group, burn group and abrasion group showed polarization, which was more obvious in the electrical damage group and burn group. Different types of damage was distinguished by principal component and partial least squares method. Linear discriminant and support vector machine models could effectively diagnose different damages. The absorption peaks at 2 923 cm-1, 2 854 cm-1, 1 623 cm-1, and 1 535 cm-1 showed significant differences in different injury groups. The peak intensity of electrical injury's 2 923 cm-1 absorption peak was the highest. Conclusion FTIR-MSP combined with machine learning algorithm provides a new technique to diagnose skin electrical damage and identification electrocution.
