Dual-model analysis for improving the discrimination performance of human and nonhuman blood based on Raman spectroscopy

Abstract

The discrimination accuracy for human and nonhuman blood is important for customs inspection and forensic applications. Recently, Raman spectroscopy has shown effectiveness in analyzing blood droplets and stains with an excitation wavelength of 785 nm. However, the discrimination of liquid whole blood in a vacuum blood tube using Raman spectroscopy, which is a form of noncontact and nondestructive detection, has not been achieved. An excitation wavelength of 532 nm was chosen to avoid the fluorescent background of the blood tube, at the cost of reduced spectroscopic information and discrimination accuracy. To improve the accuracy and true positive rate (TPR) for human blood, a dual-model analysis method is proposed. First, model 1 was used to discriminate human-unlike nonhuman blood. Model 2 was then used to discriminate human-like nonhuman blood from the "human blood" obtained by model 1. A total of 332 Raman spectra from 10 species were used to build and validate the model. A blind test and external validation demonstrated the effectiveness of the model. Compared with the results obtained by the single partial least-squares model, the discrimination performance was improved. The total accuracy and TPR, which are highly important for practical applications, increased to 99.1% and 97.4% from 87.2% and 90.6%, respectively.

Raman ConvMSANet: A High-Accuracy Neural Network for Raman Spectroscopy Blood and Semen Identification

“…With the development of machine learning, Raman spectroscopy has achieved better identification results with machine learning algorithms. Partial least-squares discriminant analysis (PLS-DA) – and principal component analysis (PCA) , are widely used in blood spectral analysis. The first successful study of blood identification using near-infrared Raman spectroscopy was carried out by Virkler and Lednev in 2009.…”

“…They used PCA to extract three principal component features and plotted 3D maps to distinguish human, feline, and canine blood visually. To distinguish between human and nonhuman blood from the Raman spectra of 10 blood species, Bian et al 16 constructed a pairwise double PLS-DA model, significantly improving single PLS-DA identification. Wang et al 20 used the support vector machine (SVM) method to identify four poultry species’ blood and analyze the presence of food additives in the blood.…”

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

“…In 2017, J. Fujihara reported the discrimination of human blood from non-human blood based on analysis of 11 blood Raman spectra using principal component analysis (PCA) [15]. Other research groups have also achieved a lot of progress [16][17][18][19]. Deep learning methods such as convolutional neural network have also been adopted as a new tool for spectroscopic analysis [20][21][22][23][24][25][26][27], and used in blood identification [28,29].…”

Blood species identification based on deep learning analysis of Raman spectra

“…The nondestructive and noncontact detection method needs to be established to discriminate the liquid whole blood in vacuum blood tube directly without sampling, because it's not only time-saving but also safe to the inspectors. In our previous work, the discrimination of fresh blood droplet and whole blood in vacuum blood tube using combination of Raman spectroscopy and PLS were studied [18][19][20][21].…”