Identification of blood species based on surface‐enhanced Raman scattering spectroscopy and convolutional neural network

The identification of blood species is of great significance in many aspects such as forensic science, wildlife protection, and customs security and quarantine. Conventional Raman spectroscopy combined with chemometrics is an established method for identification of blood species. However, the Raman spectrum of trace amount of blood could hardly be obtained due to the very small cross‐section of Raman scattering. In order to overcome this limitation, surface‐enhanced Raman scattering (SERS) was adopted to analyze trace amount of blood. The 785 nm laser was selected as the optimal laser to acquire the SERS spectra, and the blood SERS spectra of 19 species were measured. The convolutional neural network (CNN) was used to distinguish the blood of 19 species including human. The recognition accuracy of the blood species was obtained with 98.79%. Our study provides an effective and reliable method for identification and classification of trace amount of blood. In this paper, surface enhanced Raman spectroscopy technology is used to carry out the research on the species identification of trace blood, and convolutional neural network algorithm is used to distinguish the SERS spectra of 19 species, with an accuracy rate of 98.79%. Our study provides an effective and reliable method for identification and classification of trace amount of blood.