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

Abstract

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.

SERS microscopy as a tool for comprehensive biochemical characterization in complex samples

“…ANNs are often referred to as the approach of machine learning and artificial intelligence, because of their broad successful use in many fields. 157 They have also been applied for the analysis of SERS data of several types of biological samples, e.g., for food analysis, 158 forensics, 159,160 palynology, 161 or medical diagnostics. 162 Due to the variability of SERS signals, convolutional neural networks are particularly well suited and perform very well, 132 because they contain convolutional and pooling layers that summarize features that may be present in neighboring data points.…”

RepDwNet: Lightweight Deep Learning Model for Special Biological Blood Raman Spectra Analysis

“…Huang et al [16] designed a hierarchical convolutional neural network, achieving an average accuracy of 97% in a blind test involving 20 different animal species. Additionally, Chen et al [17] combined a convolutional neural network with the Stochastic Gradient Descent (SGD) optimizer, achieving significant results in differentiating 19 types of blood in experiments. The model in this study demonstrated a recognition accuracy as high as 98.79%, establishing a solid foundation for research in trace blood analysis.…”