Research on Nondestructive and Noninvasive Detection Technology of Cells Based on Supercontinuum Spectrum
WANG Hong-peng2,3, FANG Pei-pei1,6, MA Huan-zhen1,6, WAN Xiong1,2,3*, JIA Jian-jun2,3,4*, HE Zhi-ping2,3,4*, LING Zong-cheng5
1. School of Life Science, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
2. Key Laboratory of Space Active Opto-Electronics Technology of the Chinese Academy of Sciences, Shanghai 200083, China
3. Shanghai Institute of Technical Physics of the Chinese Academy of Sciences, Shanghai 200083, China
4. Shanghai Research Center for Quantum Sciences, Shanghai 201315, China
5. School of Space Science and Physics, Shangdong University, Weihai, Weihai 264209, China
6. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract：China has abundant genetic resources under the huge population base. Foreign countries may illegally plunder these resources to obtain benefits. There may be some security risks in the process of illegal plunder, such as the spread of infectious diseases. It has become a new problem of biosafety to strengthen the protection of Chinese citizens’ genetic resources and promote normal and legal international information sharing and scientific research cooperation. In order to strengthen the entry and exit management of human cells and their products, prevent the loss of genetic resources and the introduction of harmful substances, and promote medical scientific research and international exchange and cooperation among various countries, a non-invasive, fast and safe cell spectral identification technology is proposed. In this paper, the physicochemical mechanism of cell supercontinuum is described, the effect of cell concentration on the supercontinuum is discussed, and the non-invasive detection and extraction of the supercontinuum fingerprint spectrum of biological cells is realized. The experimental results show that the supercontinuum fingerprint spectrum of cells is mainly concentrated in the visible region of 500~700 nm. The cell samples in the experiment are all cultured separately, so there is no influence between the samples. The subjects of the experiment are 293T cells, HCC827 cells and HT29 cells. The medium of the three types of cells in PBS medium, each type of cell has three concentrations (5×105, 5×106, 5×107 cells·mL-1) and three samples are cultured independently under each concentration, a total of 27 samples are obtained individual cell samples. The supercontinuum spectra of 24 cell samples were tested and a prediction model was established. Another three samples were used as unknown samples for model prediction. The principal component analysis is used to reduce the dimension of the test samples’ original data, and the reduced dimension data are classified by support vector machine regression. The root mean square error of the training set is 0.097 2, R2=0.995 1, and the root mean square error of the verification set is 0.097 2, R2=0.931 4. It is found that cell concentration affects the extraction of the supercontinuum fingerprint spectrum. In this paper, when building the model, considering the universality of the application of the technology and the limited concentration parameters of the experimental samples, the influence of cell concentration on the recognition rate of the prediction model is not considered. In the later stage, if a certain concentration threshold is taken as the concentration starting point of cell detection, the recognition rate of the model will be more accurate and scientific. Under controlled experimental conditions, supercontinuum spectroscopy can be applied to noninvasive and noninvasive identification of biological cells.
王泓鹏，方沛沛, 马焕臻, 万 雄, 贾建军, 何志平, 凌宗成. 超连续光谱细胞无损伤非侵入式检测技术的研究[J]. 光谱学与光谱分析, 2021, 41(04): 1011-1015.
WANG Hong-peng, FANG Pei-pei, MA Huan-zhen, WAN Xiong, JIA Jian-jun, HE Zhi-ping, LING Zong-cheng. Research on Nondestructive and Noninvasive Detection Technology of Cells Based on Supercontinuum Spectrum. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(04): 1011-1015.
[1] SI Fang, CAO Na, LÜ Deng-fei（司 访, 曹 娜, 吕登飞). Journal of Chifeng University·Natural Science Edition（赤峰学院学报·自然科学版）, 2018，(7): 104.
[2] Zhang Linna, Sun Meixiu, WANG Zhennan, et al. Infrared Physics & Technology, 2017, 85: 32.
[3] Cascio D, Taormina V, Cipolla M, et al. Pattern Recognition Letters, 2016, 82: 56.
[4] Rodriguez Luna J C, Cooper J M, Neale S L. Automated Particle Identification Through Regression Analysis of Size, Shape and Colour. Proc SPIE, 2016, 9711: 97110R.
[5] YANG Jing, WANG Cheng, XIE Cheng-ying, et al(杨 静，王 成，谢成颖，等). Journal of Biomedical Engineering(生物医学工程学杂志)，2017，34(2)：246.
[6] Martin F L, Kelly J G, Llabjani V, et al. Nature Protocols, 2010, 5(11): 1748.
[7] Siddiqi A M, Li H, Faruque F, et al. Cancer, 2008, 114(1): 13.
[8] Kelp G, Arju N, Lee A, et al. Analyst, 2019, 144: 1115.
[9] Verma R S, Ahlawat S, Uppal A. Analyst, 2018, 143: 2648.
[10] Cao L J, Chua K S, Chong W K, et al. Neurocomputing, 2003, 55(1-2): 321.
[11] Moon H, Phillips P J. Perception, 2001, 30(3): 303.
[12] Misra M, Yue H H, Qin S J, et al. Computers & Chemical Engineering, 2002, 26(9): 1281.
[13] WANG Ding-cheng, WANG Mao-hua(王定成，汪懋华). Control and Decision(控制与决策), 2004, 19(9)：1067.
[14] Brereton R G, Lloyd G R. Analyst, 2010, 135(2): 230.
