Infrared spectral characteristics of electrical injuries on swine skin caused by different voltages based on machine learning algorithms

Dong, H. W.; Li, W.; Li, S. Y.; Deng, K. F.; Cao, N.; Luo, Y. W.; Sun, Q. R.; Lin, H. C.; Huang, J. F.; Liu, N. G.; Huang, P.

doi:10.12116/j.issn.1004-5619.2018.06.009

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Infrared spectral characteristics of electrical injuries on swine skin caused by different voltages based on machine learning algorithms
Citation	Dong HW, Li W, Li SY, Deng KF, Cao N, Luo YW, Sun QR, Lin HC, Huang JF, Liu NG, Huang P. Fa Yi Xue Za Zhi 2018; 34(6): 619-624.
Affiliation	Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
Copyright	(Copyright © 2018, Si fa bu Si fa jian ding ke xue ji shu yan jiu suo)
DOI	10.12116/j.issn.1004-5619.2018.06.009
PMID	30896099
Abstract	OBJECTIVES: To explore infrared spectrum characteristics of different voltages induced electrical injuries on swine skin by using Fourier transform infrared-microspectroscopy （FTIR-MSP） combined with machine learning algorithms, thus to provide a reference to the identification of electrical skin injuries caused by different voltages. METHODS: Electrical skin injury model was established on swines. The skin was exposed to 110 V, 220 V and 380 V electric shock for 30 s and then samples were took, with normal skin tissues around the injuries as the control. Combined with the results of continuous section HE staining, the FTIR-MSP spectral data of the corresponding skin tissues were acquired. With the combination of machine learning algorithms such as principal component analysis （PCA） and partial least squares-discriminant analysis （PLS-DA）, different spectral bands were selected （full band 4 000-1 000 cm^-1 and sub-bands 4 000-3 600 cm^-1, 3 600-2 800 cm^-1, 2 800-1 800 cm^-1, and 1 800-1 000 cm^-1）, and various pretreatment methods were used such as orthogonal signal correction （OSC）, standard normal variables （SNV）, multivariate scatter correction （MSC）, normalization, and smoothing. Thus, the model was optimized, and the classification effects were compared. RESULTS: Compared with simple spectrum analysis, PCA seemed to be better at distinguishing electrical shock groups from the control, but was not able to distinguish different voltages induced groups. PLS-DA based on the 3 600-2 800 cm^-1 band was used to identify the different voltages induced skin injuries. The OSC could further optimize the robustness of the 3 600-2 800 cm^-1 band model. CONCLUSIONS: It is feasible to identify electrical skin injuries caused by different voltages by using FTIR-MSP technique along with machine learning algorithms. Copyright© by the Editorial Department of Journal of Forensic Medicine. Language: zh 题目: 基于机器学习算法研究不同电压所致猪皮肤电流损伤红外光谱特征. Language: zh 目的: 通过傅里叶变换红外显微光谱（Fourier transform infrared-microspectroscopy，FTIR-MSP）成像技术结合机器学习算法，对不同电压所致猪皮肤电流损伤红外光谱特征进行分析，旨在为不同电压所致皮肤电流损伤的鉴别提供参考。. Language: zh 方法: 建立猪皮肤电流损伤模型，分为110 V、220 V、380 V电击组及对照组，电击组电击30 s后取电击部位皮肤，对照组取对应部位正常皮肤组织。结合连续切片HE染色结果，应用FTIR-MSP成像技术采集对应区域的光谱数据，结合机器学习算法（主成分分析、偏最小二乘法-判别分析），选取不同光谱波段（全波段4 000~1 000 cm^-1和分波段4 000~3 600 cm^-1、3 600~2 800 cm^-1、2 800~1 800 cm^-1、1 800~1 000 cm^-1）及预处理方式（正交信号校正、标准正态变量、多元散射校正、归一化、平滑）对模型进行优化，比较所建模型的分类效果。. Language: zh 结果: 相较于单纯谱图分析，主成分分析法能很好地区分电击组和对照组，但难以区分不同电压组。基于3 600~2 800 cm^-1波段的偏最小二乘法-判别分析实现了对不同电压触电所致皮肤损伤的鉴别，且采用正交信号校正能进一步优化3 600~2 800 cm^-1波段模型的效能。. Language: zh 结论: 应用FTIR-MSP成像技术结合机器学习算法对不同电压所致猪皮肤电流损伤的鉴别具有可行性。. Language: zh 关键词: 法医病理学；谱学，傅里叶变换红外；电击伤；机器学习算法；皮肤；猪. Language: zh
Keywords	forensic pathology; spectroscopy, Fourier transform infrared; electric injuries; machine learning algorithms; skin; swine