Blood species identification of human and animals has attracted much attention in the areas of customs inspection and forensic science. The combination of vibrational spectroscopy and machine learning has been proven to be feasible and effective for this purpose. However, the popularization of this technology needs instrument which is compact, robust and more suitable for field application. Besides the quantity of the blood sample should be as little as possible. In this study, we proposed a system using echelle Raman spectrometer combined with surface enhanced Raman spectroscopy (SERS), which protocol combines the advantages of broadband and high resolution of echelle Raman spectrometer with the advantages of high SERS spectral sensitivity. The SERS spectra of 26 species including human were collected with echelle Raman spectrometer, and the convolutional neural network was used for species identification, with an accuracy rate of over 94%. The feasibility, validity and reliability of the combination of echelle Raman spectrometer and SERS for blood species identification were realized.
Keywords: Blood species identification; Convolutional neural network; Echelle Raman spectrometer; Surface enhanced Raman spectroscopy.
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