Author(s)

Jianhong Gan^1,2, Linhua Zhou^1,2*, Jian Cui^1,2, Boqi Man^1,2, Xiaoning Jia^1,2, Sanzhi Shi^1,2, Linna Liu³

¹School of Science, Changchun University of Science and Technology, Changchun, China.
²Provincial Demonstration Center for Experimental Mathematics Education (Changchun University of Science and Technology), Changchun, China.
³Chinese Academy of Agricultural Sciences, Changchun Veterinary Research Institute, Changchun, China.

In this work, a deep belief neural network model (DBN) was developed to classify doves, chickens, mice and sheep blood samples, which have many similarities in composition causing their spectra to look almost identical by visual comparison alone. The DBN model was formulated for the feature extraction from the pretreated fluorescence spectroscopy. Then, cross-validation results showed that the application of deep learning method made it possible to classify the blood fluorescence spectroscopy in a more precise way than previous methods. Especially, the classification accuracy of whole blood with 1% of concentration was up to 97.5%.

Neural Network Model, Deep Learning, Classification, Blood Species, Fluorescence Spectroscopy

Gan, J. , Zhou, L. , Cui, J. , Man, B. , Jia, X. , Shi, S. and Liu, L. (2019) Classification of Blood Species Using Fluorescence Spectroscopy Combined with Deep Learning Method. Journal of Applied Mathematics and Physics, 7, 2324-2332. doi: 10.4236/jamp.2019.710158.