Study on the echinococcosis blood serum detection based on Raman spectroscopy combined with neural network

Abstract

A Raman spectroscopy method combined with neural network is used for the invasive and rapid detection of echinococcosis. The Raman spectroscopy measurements are performed on two groups of blood serum samples, which are from 28 echinococcosis patients and 38 healthy persons, respectively. The normalized Raman reflection spectra show that the reflectivity of the echinococcosis blood serum is higher than that of the normal human blood serum in the wavelength ranges of 101—175 nm and 1 801—2 701 nm. Then the principal component analysis (PCA) and back propagation neural network (BPNN) model are used to obtain the diagnosis results. The diagnosis rates for healthy persons and echinococcosis persons are 93.333 3% and 90.909 1%, respectively, so the average final diagnosis rate is 92.121 2%. The results demonstrate that the Raman spectroscopy analysis of blood serum combined with PCA-BPNN has considerable potential for the non-invasive and rapid detection of echinococcosis.