Study on Recognition and Classification of Blood Fluorescence Spectrum with BP Neural Network

There is no doubt that spectrum technology has a positive role in applied prospects of biological and medical testing. Because of the complexity and the similarity of blood component, study on recognition and classification of different animal's blood is still an open issue. Based on the theory of machine learning, by BP neural network, the authors proposed a method of feature extraction and classification for different animal's blood fluorescence spectra. In this experiment, fluorescence spectra data of whole blood and red blood cell with different concentration (1% and 3%) is collected, respectively. By neighborhood average method, the original data is denoised in order to reduce the impact of noise on the feature extraction and classification. For the specialty of blood fluorescence spectra, the authors proposed a new feature extraction method of "Combination and Amplification method", and established a BP neural network classifier. Compared with other common spectra feature, "Combination and Amplification" feature and the BP neural network classifiercan achieve good recognition and classification for different animal's blood fluorescence spectra, and the test error is much less than allowable variation. The technologies in this paper can play an important role in medical examination, agriculture, and food safety testing.