Infrared Spectral Characteristics of Electrical Injuries on Swine Skin Caused by Different Voltages Based on Machine Learning Algorithms.

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.