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
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.
Key words：Artificial fish swarm algorithm; Confocal Raman spectroscopy; Support vector machine
马焕臻，闫薪如，辛英健，方沛沛，王泓鹏，王一安，段明康，贾建军，何继业，万 雄. 基于AFSA-SVM动态光谱的血液识别研究[J]. 光谱学与光谱分析, 2024, 44(07): 1877-1882.
MA Huan-zhen, YAN Xin-ru, XIN Ying-jian, FANG Pei-pei, WANG Hong-peng, WANG Yi-an, DUAN Ming-kang, JIA Jian-jun, HE Ji-ye, WAN Xiong. Blood Identification Based on AFSA-SVM Dynamic Spectra. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1877-1882.
[1] Balzerova Anna, Fargasova Ariana, Markova Zdenka, et al. Analytical Chemistry, 2014, 86(22): 11107.
[2] Gurian E, Di Silvestre A, Mitri E, et al. Anal. Bioanal. Chem., 2021, 413(5): 1303.
[3] Lima K M, Gajjar K B, Martin-Hirsch P L, et al. Biotechnol. Prog., 2015, 31(3): 832.
[4] Chen Xue, Li Xiaohui, Yang Sibo, et al. Biomed. Opt. Express, 2018, 9(3): 1057.
[5] Wang Hongpeng, Ma Huanzhen, Fang Peipei, et al. Spectrochim. Acta A: Mol. Biomol. Spectrosc., 2021, 259: 119890.
[6] Wang Hongpeng, Fang Peipei, Yan Xinru, et al. J. Photochem. Photobiol. B, 2022, 232: 112478.
[7] Ning Xiaoran, Selesnick Ivan W, Duval Laurent. Chemometrics and Intelligent Laboratory Systems, 2014, 139: 156.
[8] Lemler P, Premasiri W R, DelMonaco A, et al. Analytical and Bioanalytical Chemistry, 2014, 406(1): 193.
[9] Huang Shan, Wang Peng, Tian Yubing, et al. Biomedical Optics Express, 2019, 10(12): 6129.
[10] Abe M, Kitagawa T, Kyogoku Y. The Journal of Chemical Physics, 1978, 69(10): 4526.
[11] Wood Bayden R, Caspers Peter, Puppels Gerwin J, et al. Analytical and Bioanalytical Chemistry, 2007, 387(5): 1691.
[12] Hu Songzhou, Smith Kevin M, Spiro Thomas G. Journal of the American Chemical Society, 1996, 118(50): 12638.
[13] Yu Hua, Yang Jie. Pattern Recognition, 2001, 34(10): 2067.
[14] Jelodar Hamed, Wang Yongli, Yuan Chi, et al. Multimedia Tools and Applications, 2019, 78(11): 15169.
[15] Yue Shihong, Li Ping, Hao Peiyi. Applied Mathematics-A Journal of Chinese Universities, 2003, 18(3): 332.
[16] Li Xiaolei. Systems Engineering-Theory & Practice, 2002, 22(11): 32.
[17] Myles Anthony J, Feudale Robert N, Liu Yang, et al. Journal of Chemometrics: A Journal of the Chemometrics Society, 2004, 18(6): 275.
[18] Belgiu Mariana, Drǎguţ Lucian. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 114: 24.
[19] Hastie Trevor, Rosset Saharon, Zhu Ji, et al. Statistics and Its Interface, 2009, 2(3): 349.
[20] Chicco Davide, Jurman Giuseppe. BMC Genomics, 2020, 21(1): 1.
