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| Blood Identification Based on AFSA-SVM Dynamic Spectra |
| MA Huan-zhen1, 4, YAN Xin-ru1, 4, XIN Ying-jian3, 4, FANG Pei-pei1, 3, 4, WANG Hong-peng3, WANG Yi-an1, 4, DUAN Ming-kang3, 4, JIA Jian-jun3, HE Ji-ye2*, WAN Xiong1, 3* |
1. Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
2. Department of Orthopedics, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
3. Key Laboratory of Space Active Opto-Electronics Technology of the Chinese Academy of Sciences, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
4. University of the Chinese Academy of Sciences, Beijing 100049, China
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Abstract Blood is a regulated exceptional genetic biological resource. In response to the issue of easy oxidation and deterioration in traditional blood spectral detection, dynamic confocal Raman fluorescence spectroscopy technology based on biomimetic blood vessels was used to conduct blood species identification research on six types of poultry and livestock, including pigs, horses, pigeons, chickens, ducks, and geese. The preprocessing process of the original spectrum includes baseline removal, smoothing, and normalization. Linear discriminant analysis is used to reduce the dimensionality of spectral data, and then support vector machines are used to establish recognition models. Gaussian kernel functions are selected, and the parameters C and γ Make their classification accuracy the highest, the optimal C and γ 0.2 and 0.134, respectively. The recognition accuracy of the artificial fish school support vector machine model reaches 97.2%. The dynamic confocal Raman fluorescence spectrum based on biomimetic blood vessels used in this article can meet the requirements of blood safety and efficiency detection, and the algorithm model optimized by the artificial fish school algorithm for support vector machine parameters shows good classification performance.
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Received: 2022-07-25
Accepted: 2023-10-24
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Corresponding Authors:
HE Ji-ye, WAN Xiong
E-mail: wanxiong@mail.sitp.ac.cn；doctorandy@163.com
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