Discrimination of blood species using Raman spectroscopy combined with a recurrent neural network

Abstract

Species identification of human and animal blood is of critical importance in the areas of custom inspection, forensic science, wildlife preservation, and veterinary purpose. In this study, the combination of Raman spectroscopy and a recurrent neural network (RNN) is proposed for the discrimination of 20 kinds of blood species including human, poultry, wildlife, and experimental animals. The chemometric multi-classification model based on RNN was established and optimized by hyperparameter tuning and structure selection. The performance scores of the bidirectional RNN model with GRU for 20 kinds of species are as follows: accuracy 97.7%, precision 97.8%, recall 97.8% and F1-score 97.7%. The model resistant to wavenumber drift and cross-instrumental model were also studied for practical application purpose using a subset of Raman spectra by both commercial and laboratory-built Raman spectrometers. The evaluation shows an accuracy of 98.2%. These results indicate that our approach has great potential for blood species identification in real application scenarios.