Determination of Electrocution Using Fourier Transform Infrared Microspectroscopy and Machine Learning Algorithm.
Fa Yi Xue Za Zhi
; 36(1): 35-40, 2020 Feb.
Article
in En, Zh
| MEDLINE
| ID: mdl-32250076
ABSTRACT
ABSTRACT Objective To analyze the differences among electrical damage, burns and abrasions in pig skin using Fourier transform infrared microspectroscopy ï¼FTIR-MSPï¼ 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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Machine Learning
Type of study:
Prognostic_studies
Limits:
Animals
Language:
En
/
Zh
Journal:
Fa Yi Xue Za Zhi
Journal subject:
JURISPRUDENCIA
Year:
2020
Document type:
Article
Affiliation country:
China