Spectral Detection Technique of Blood Species Based on Data Driven Model
LI Hong-xiao1, SUN Mei-xiu1, XIANG Zhi-guang2, WANG Yi3, LIN Ling3, QIN Chuan2, LI Ying-xin1*
1. Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, China
2. Institute of Laboratory Animal Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
3. School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
Abstract：This paper proposed a non-contact blood species recognition technique based on spectral detection and data driven model. A total of 649 blood samples were selected from 4 species (monkey 144, rat 203, dog 133, and human 169) as the original samples. The wavelength range of the super continuum laser source was 450～2 400 nm. The backward scattered visible spectrum (294～1 160 nm) and the forward scattered near-infrared spectra of ten different spatial sites were collected from each blood sample contained in anticoagulant tubes. Then the eleven spectra were sequentially connected into one-dimensional data as the original data of each sample. The principal component analysis was used to extract the feature information of the dataset, which retained 99.99% of the original variance information, while compressing the data amount to 1.5% of the original data volume, such to improve the computational efficiency of classification and recognition. Experiments on different numbers of training sets and verification sets showed that the recognition error rate of ten-fold cross-validation decreases with the increase of the number of samples, and the increase of sample bank size can improve the recognition accuracy. Because the data driven model is a data stream processing model based on machine learning algorithms, in which a variety of different classification algorithms can be used to realize this model. By comparing the recognition effects of six algorithms (artificial neural network, support vector machine, partial least-squares regression, multiple linear regression, random forest and Naïve Bayes), it was found that the recognition effects of different algorithms have the category differences, that is, the sort of these algorithms in terms of their correct recognition rate are different for different species. Therefore, when choosing the data driven model as a solution, in addition to considering the overall recognition rate of the algorithm, the scheme should also consider the category differences of the algorithm if there are additional requirements on the recognition effect of some certain categories.
Key words：Non-contact blood species recognition; Data driven model; Classification algorithm
李宏霄，孙美秀，向志光，汪 毅，林 凌，秦 川，李迎新. 数据驱动模型的血液物种光谱检测技术[J]. 光谱学与光谱分析, 2018, 38(08): 2483-2487.
LI Hong-xiao, SUN Mei-xiu, XIANG Zhi-guang, WANG Yi, LIN Ling, QIN Chuan, LI Ying-xin. Spectral Detection Technique of Blood Species Based on Data Driven Model. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(08): 2483-2487.
[1] Jöbsis F F. Science, 1977, 198(4323): 1264.
[2] Vashist S K. Analytica Chimica Acta, 2012, 750: 16.
[3] Ding H, Lu Q, Gao H, et al. Biomedical Optics Express, 2014, 5(4): 1145.
[4] Maruo K, Yamada Y. Journal of Biomedical Optics, 2015, 20(4): 47003.
[5] Bazar G, Eles V, Kovacs Z, et al. Talanta, 2016, 155: 202.
[6] Wang L, Mizaikoff B. Analytical and Bioanalytical Chemistry, 2008, 391(5): 1641.
[7] Tanaka H, Katura T. Journal of Biomedical Optics, 2011, 16(8): 87001.
[8] LIU Ling-yun, ZHENG Guang-mei(刘凌云，郑光美). General Zoology(普通动物学). Beijing: Higher Education Press(北京: 高等教育出版社), 1997.
[9] WANG Dong-ping, SUI Li-hua, HONG Bao-qing, et al(王冬平，隋丽华，洪宝庆, 等). Chinese Journal of Comparative Medicine(中国比较医学杂志), 2007，(7): 400.
[10] DONG Quan, LONG Zhi, JI Mei-rong, et al(董 全，龙 志，稽美容, 等). Acta Zoologica Sinica(动物学报), 1991, 37(1): 64.
[11] Haykin S. Neural Networks: a Comprehensive Foundation. 2nd ed. Singapore: Pearson Education, 1999.
[12] Murphy K P. Machine Learning: a Probabilistic Perspective. Cambridge, Massachusetts: MIT Press, 2012.
