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| Feature Extraction and Classification of Animal Blood Spectra with Support Vector Machine |
| LU Peng-fei1, FAN Ya1, ZHOU Lin-hua1*, QIAN Jun2, LIU Lin-na2, ZHAO Si-yan2, KONG Zhi-feng3, GAO Bin1 |
1. School of Science, Changchun University of Science and Technology, Changchun 130022, China
2. Changchun Veterinary Institute, Chinese Academic Agricultural Sciences, Changchun 130122, China
3. School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710048, China |
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Abstract It is of great significance to study how to use spectral detection technology and data mining technology to realize the accurate identification and classification of different animal blood spectral data, and it has not yet seen relevant complete research conclusions and methods on animal blood identification and classification. Therefore, the authors collected fluorescence spectra data of four kinds of animals, including pigeon, chicken, mouse and sheep. Based on the soft threshold denoising method of wavelet transform, the original spectral data were denoised, and the 717 original features were determined. Following the approach of “Distinguish statistic” proposed by the authors, 717 original features were extracted into 2 finally input features. Based on support vector machine, the whole blood solution of different animals were 100% recognized, while the red cell blood solution of different animals were 94.69%~99.12% correctly recognized. Finally, the Monte Carlo cross validation revealed that the method used in this paperhad a great generalization ability for whole blood solution of different animals, which can play an important role in the import and export inspection, food safety, medicine and other fields.
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Received: 2017-01-08
Accepted: 2017-05-14
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Corresponding Authors:
ZHOU Lin-hua
E-mail: zhoulh@cust.edu.cn
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