Abstract：Biological detection is a common method in biomedicine based on the change of specific biological activity of cytokines. It is mainly applied in biomedicine, agriculture and forestry, etc., and plays an important role in the study of medical pathology and the law of crop diseases. Traditional detection methods mainly make judgments by observing the reaction of test samples to different chemical reagents. Although the detection accuracy is high, problems include cumbersome operation and long detection cycle. Hyperspectral imaging technology combines optical imaging and spectral analysis, which can simultaneously obtain the image data and spectral information of the detected samples. The image data in hyperspectral images reflect the samples' external characteristics and surface texture, while the spectral information can analyze detected samples' the internal physical structure and chemical composition. Micro-hyperspectral imaging technology is a fast, nondestructive and accurate optical imaging analysis technology that combines hyperspectral imaging technology with the biological microscope to analyze the detected samples by observing the microscopic world's image data and spectral information. In recent years, because of its high resolution and continuous data, microscopic hyperspectral imaging has attracted more and more attention in biological detection and has become one of the important means of biological detection. Based on the basic principle of spectral imaging, data processing and application of biological detection, this paper reviews the research status of microscopic hyperspectral imaging technology in biological detection in recent ten years. It puts forward some problems existing in the research process of microscopic hyperspectral imaging technology based on summarizing the research achievements. The future development trend of microhyperspectral imaging in biological detection prospects to provide a reference for the research and application of microhyperspectral imaging in biological detection.
张红涛，赵鑫涛，谭 联，王龙杰. 显微高光谱成像技术在生物学检测中的研究与进展[J]. 光谱学与光谱分析, 2023, 43(08): 2348-2353.
ZHANG Hong-tao, ZHAO Xin-tao, TAN Lian, WANG Long-jie. Research and Development of Microscopic Hyperspectral Imaging in
Biological Detection. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2348-2353.
[1] HUANG Jin, LIANG Tao-bo, XU Heng-yi（黄 瑾, 梁涛波, 许恒毅）. Chinese Bulletin of Life Sciences（生命科学）, 2021, 33(2): 255.
[2] XU Shi-ying, SUN Jia-di, ZHANG Yin-zhi, et al（徐诗荧, 孙嘉笛, 张银志, 等）. Food Research and Development（食品研究与开发）, 2021, 42(8): 160.
[3] Chen T, Zeng R, Guo W, et al. Sensors, 2018, 18(9): 2798.
[4] Pandey Piyush, Ge Yufeng, Stoerger Vincent, et al. Frontiers in Plant Science, 2017, 8(3): 1348.
[5] Adäo Telmo, Hruska Jonas, Pádua Luis, et al. Remote Sensing, 2017, 9(11): 1110.
[6] Knight Edward, Kvaran Geir. Remote Sensing, 2014, 6(11): 10286.
[7] HOU Bang-huan, YAO Min-li, WANG Rong, et al（侯榜焕, 姚敏立, 王 榕, 等）. Acta Optica Sinica（光学学报）, 2017, 37(7): 314.
[8] XIAO Gong-hai, SHU Rong, XUE Yong-qi（肖功海, 舒 嵘, 薛永祺）. Optics and Precision Engineering(光学精密工程), 2004, 12(4): 367.
[9] LIU Jia-qing, HAN Shun-li, LIU Lei, et al（刘加庆, 韩顺利, 刘 磊, 等）. Infrared（红外）, 2017, 38(7): 22.
[10] Zhou P, Zhao H, Jin S, et al. Optics Communications, 2016, 367(1): 192.
[11] ZHENG Yu-quan, YU Bing-xi（郑玉权, 禹秉熙）. National Remote Sensing Bulletin（遥感学报）, 2002，(1): 75.
[12] Goetz A F H. Remote Sensing of Environment, 2007, 113: S5.
[13] Huebschman Michael L, Schultz Roger A, Garner Harold R. IEEE Engineering in Medicine and Biology Magazine, 2002, 21(4): 104.
[14] Van Benthem Mark H, Keenan M R, Davis R, et al. Journal of Chemometrics, 2008, 22(9): 491.
[15] FAN Shi-fu, LI Yun, ZHAO You-quan（范世福, 李 昀, 赵友全）. Life Science Instruments（生命科学仪器）, 2004，(4): 24.
[16] WU Yong-qing, LI Ming, ZHANG Bo, et al（吴永清, 李 明, 张 波, 等）. Journal of The Chinese Cereals and Oils Association（中国粮油学报）, 2021, 36(5): 165.
[17] ZHOU Zhao-lu, LI Jie, HUANG Sheng-quan, et al（周昭露, 李 杰, 黄生权, 等）. Chemical Industry and Engineering Progess（化工进展）, 2016, 35(6): 1627.
[18] SHEN Guang-hui, CAO Yao-yao, LIU Xin, et al（沈广辉, 曹瑶瑶, 刘 馨, 等）. Jiangsu Journal of Agricultural Sciences（江苏农业学报）, 2021, 37(2): 509.
[19] LIU Teng-fei, LIU Wei（刘腾飞, 刘 威）. Electronic Design Engineering（电子设计工程）, 2021, 9(6): 1.
[20] LIU Yao, LI Zi-nan, WU Tao, et al（刘 瑶, 李梓楠, 吴 涛, 等）. Soybean Science（大豆科学）, 2018, 37(4): 596.
[21] ZHANG Ting-ting, XIANG Ying-ying, YANG Li-ming, et al（张婷婷, 向莹莹, 杨丽明, 等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2019, 39(5): 1556.
[22] Akbari Hamed, Halig Luma V, et al. Proc SPIE, 2012, 8317: 831711.
[23] Akbari Hamed, Uto Kuniaki, Kosugi Y, et al. Cancer Science, 2011, 102(4): 852.
[24] Akbari H, Kosugi Y, Kojima K, et al. IEEE Transactions on Biomedical Engineering, 2010, 57(8): 2011.
[25] Fabelo Himar, Ortega Samuel, Ravi Daniele, et al. PLOS ONE, 2018, 13(3): e0193721.
[26] Zheludev V, Pölönen I, Neittaanmki-Perttu N, et al. Biomedical Signal Processing and Control, 2015, 16(1): 48.
[27] Kiyotoki S, Nishikawa J, Okamoto T, et al. Journal of Biomedical Optics, 2013, 18(2): 26011.
[28] Giannoni Luca, Lange Frédéric, Tachtsidis Ilias. Journal of Optics, 2018, 20(4): 044009.
[29] FANG Juan, LIU Hong-ying, CHEN Zeng-gan, et al（房 娟, 刘洪英, 陈增淦, 等）. Photographic Science and Photochemistry（影像科学与光化学）, 2015, 33(3): 203.
[30] Li Qingli, Wang Yiting, Liu Hongying, et al. Computerized Medical Imaging and Graphics, 2014, 38(3): 171.
[31] Li Qingli, Li Chang, Liu Hongying, et al. Optics & Laser Technology, 2015, 74: 79.
[32] YU Cui-rong, WANG Tian-ran, LI Sheng, et al（于翠荣, 王天然, 李 胜, 等）. Science Technology and Engineering（科学技术与工程）, 2015, 15(27): 106.
[33] Li X, Li W, Xu X D, et al. Cell Classification Using Convolutional Neural Networks in Medical Hyperspectral Imagery, 2017 2nd International Conference on Image, Vision and Computing (ICIVC). IEEE, 2017. doi: 10.1109/ICIVC.2017.7984606.
[34] Wang Q, Wang J B, Zhou M, et al. Biomedical Optics Express, 2017, 8(6): 3017.
[35] Li Q, Liu H, Wang Y, et al. Optics & Laser Technology, 2014, 64: 337.
