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
Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
Department of Public Security Technology, Railway Police College, Zhengzhou 450053, China.
Fa Yi Xue Za Zhi. 2018 Jun;34(6):619-624. doi: 10.12116/j.issn.1004-5619.2018.06.009. Epub 2018 Dec 25.
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.
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 and sub-bands 4 000-3 600 cm, 3 600-2 800 cm, 2 800-1 800 cm, and 1 800-1 000 cm）, 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.
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 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 band model.
It is feasible to identify electrical skin injuries caused by different voltages by using FTIR-MSP technique along with machine learning algorithms.
采用傅里叶变换红外光谱显微镜（FTIR-MSP）结合机器学习算法，探索不同电压所致猪皮肤电损伤的红外光谱特征，为不同电压所致皮肤电损伤的鉴定提供参考。
建立猪皮肤电损伤模型。将皮肤分别暴露于110V、220V和380V电击30s，然后取样，以损伤周围正常皮肤组织作为对照。结合连续切片HE染色结果，获取相应皮肤组织的FTIR-MSP光谱数据。结合主成分分析（PCA）和偏最小二乘判别分析（PLS-DA）等机器学习算法，选择不同光谱波段（全波段4000 - 1000cm以及子波段4000 - 3600cm、3600 - 2800cm、2800 - 1800cm和1800 - 1000cm），并采用多种预处理方法，如正交信号校正（OSC）、标准正态变量变换（SNV）、多元散射校正（MSC）、归一化和平滑处理。由此对模型进行优化，并比较分类效果。
与简单光谱分析相比，PCA在区分电击组与对照组方面表现较好，但无法区分不同电压所致组。基于3600 - 2800cm波段的PLS-DA用于鉴定不同电压所致的皮肤损伤。OSC可进一步优化3600 - 2800cm波段模型的稳健性。
利用FTIR-MSP技术结合机器学习算法鉴定不同电压所致的皮肤电损伤是可行的。
