| Autor: |
H W, Dong, W, Li, S Y, Li, K F, Deng, N, Cao, Y W, Luo, Q R, Sun, H C, Lin, J F, Huang, N G, Liu, P, Huang |
| Rok vydání: |
2017 |
| Předmět: |
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| Zdroj: |
Fa yi xue za zhi. 34(6) |
| ISSN: |
1004-5619 |
| Popis: |
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 cmCompared 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 cmIt is feasible to identify electrical skin injuries caused by different voltages by using FTIR-MSP technique along with machine learning algorithms.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.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。.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。.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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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