Feature Extraction and Classification of Animal Blood Spectra with Support Vector Machine

文献类型： 外文期刊

第一作者： Lu Peng-fei

作者： Lu Peng-fei;Fan Ya;Zhou Lin-hua;Gao Bin;Qian Jun;Liu Lin-na;Zhao Si-yan;Kong Zhi-feng

作者机构：

关键词： Animal blood;Fluorescence spectrum;Classification;Feature extraction;Support vector machine

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2017 年 37 卷 12 期

页码：

收录情况： SCI

摘要： 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 1001% 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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