Discrimination of human and nonhuman blood using Raman spectroscopy with self-reference algorithm

Abstract

We report a self-reference algorithm to discriminate human and nonhuman blood by calculating the ratios of identification Raman peaks to reference Raman peaks and choosing appropriate threshold values. The influence of using different reference peaks and identification peaks was analyzed in detail. The Raman peak at 1003 cm-1 was proved to be a stable reference peak to avoid the influencing factors, such as the incident laser intensity and the amount of sample. The Raman peak at 1341 cm-1 was found to be an efficient identification peak, which indicates that the difference between human and nonhuman blood results from the C-H bend in tryptophan. The comparison between self-reference algorithm and partial least square method was made. It was found that the self-reference algorithm not only obtained the discrimination results with the same accuracy, but also provided information on the difference of chemical composition. In addition, the performance of self-reference algorithm whose true positive rate is 100% is significant for customs inspection to avoid genetic disclosure and forensic science.

Raman spectroscopy in experimental rock art: Improving the study of ancient paintings

“…Between ca. 700 and 1,800 cm −1 , spectra roughly match those reported for ancient, dried, and/or liquid human blood, [ 72–77 ] with main peaks at ca. 745, 1,000, 1,130, 1,240, 1,350, and 1,590 cm −1 (Figure 3c, Table 2).…”

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

“…In the figure, the main Raman peaks of human and nonhuman blood are similar. According to the literature, no obvious difference is observed among the Raman spectra of nonhuman blood collected from different species under 785-nm laser excitation [18,20]. However, when the excitation wavelength was 532 nm, the Raman spectra of the blood collected from chickens, ducks, geese, and doves differed from those collected from pigs, rabbits, and sheep.…”

“…In 2014, a method combining visible reflectance spectroscopy with partial least-squares discriminant analysis (PLS-DA) was demonstrated by Lin to be a powerful tool for species differentiation [15]. Moreover, Raman spectroscopy was reported as an effective technology for discriminating human and nonhuman blood [16][17][18][19][20][21][22]. Given its advantagessuch as, being nondestructive, not requiring sample preparation, and having high accuracy for the identification of organic, inorganic, and biological species-Raman spectroscopy has shown greater potential for species identification applications than other spectral methods.…”

“…Because the compositions of human and nonhuman blood are similar, the Raman peaks for both types of blood samples are the same. Chemometric algorithms, including principal component analysis [18], PLS-DA [19,22], support vector machines [23], and self-reference algorithms [18], are used to identify the species of origin. All the aforementioned methods were proven to be effective for blood samples, which should be transferred from the vacuum tube onto a slide coated with aluminum by practitioners.…”

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].…”