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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., ((2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).)

A novel method is reported to discriminate human and animal blood by Raman spectroscopy without complex sample preparation. A partial least squares discriminant analysis model was constructed from a calibration set of Raman spectra from three species of animal blood using 785-nm laser excitation. The discrimination between human and nonhuman blood was calculated by three sigma. Various performance measures, including a blind test and external validation, confirmed the discriminatory performance of the chemometric model. The model provided 100% accuracy in its differentiation between human and nonhuman blood. These results demonstrate that Raman spectroscopy is a promising tool for the discrimination of animal and human blood. [ABSTRACT FROM PUBLISHER]