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| Identification for Different Growth Years of Plastrum Testudinis via Hyperspectral Imaging Technique and Heterogeneous Ensemble Learning |
| WEI Yun-peng1, HU Hui-qiang1, MAO Xiao-bo1*, ZHAO Yu-ping2*, ZHANG Lei3, SHENG Wen-tao4 |
1. Department of Biomedical Engineering, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
2. National Resource Center for Chinese Materica Medica, China Academy of Chinese Medical Sciences, Beijing 100020, China
3. School of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang 330004, China
4. Hubei Jingshan Shengchang Turtle Breeding Farm, Jinmen 431800, China
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Abstract Plastrum Testudinis is a popular traditional Chinese medicine (TCM) with abundant medicinal and edible value, and it is widely applied to clinical medical treatment and medicinal slice preparation. Studies show that the contents of trace elements in Plastrum Testudinis are directly proportional to its growth years. However, due to inexperience and nonstandard breeding, adulterated Plastrum Testudinis medicines are on the market. Because of the limitation of empirical and chemical-based methods, a heterogeneous ensemble learning (HEL) method based on a hyperspectral imaging technique is proposed to identify the growth years of Plastrum Testudinis. First, the Plastrum Testudinis samples with different growth years are taken as research objects. The original hyperspectral images of visible near-infrared ray (VNIR) and short-wave infrared ray (SWIR) lenses are captured on the hyperspectral imaging system. Then, the heterogenous ensemble learning (HEL) model is constructed based on support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN). Results show the fused hyperspectral images of VNIR and SWIR include more abundant spectral information. The HEL model can achieve satisfactory prediction ability by identifying the different growth years of Plastrum Testudinis samples. In addition, considering the detection efficiency, an unsupervised band selection is employed to reduce the complexity, eliminate the redundant bands in hyperspectral images, and improve the classification performance further. When the number of selected spectral bands is 32, the classification accuracy reaches 96.35%. Experimental results demonstrate that the HEL model based on hyperspectral imaging can accurately and rapidly identify the different growth years of Plastrum Testudinis samples and provide a novel technique reference for the attributes identification of TCM.
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Received: 2023-05-08
Accepted: 2023-12-20
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Corresponding Authors:
MAO Xiao-bo, ZHAO Yu-ping
E-mail: mail-mxb@zzu.edu.cn；18810084632@163.com
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[1] National Pharmacopoeia Committee(国家药典委员会). Pharmacopoeia of the People's Republic of China(中华人民共和国药典). Beijing: China Traditional Chinese Medicine Publishing House(北京：中国中医药出版社), 2020, 347.
[2] Tang Q, Wang X, Chen F, et al. Journal of Pharmaceutical and Biomedical Analysis, 2013, 77: 29.
[3] TANG Yu, XIAO Dan, LIU Zi-yu, et al(唐 宇, 肖 丹, 刘子毓, 等). China Journal of Traditional Chinese Medicine and Pharmacy(中华中医药杂志), 2019, 34(6): 2593.
[4] Li X, Cui Y, Lin Q, et al. Frontiers in Molecular Biosciences, 2021, 8: 502.
[5] Zhang P, Chen H, Shen G, et al. Journal of Ethnopharmacology, 2021, 276(3): 114198.
[6] TANG Yu, XIAO Dan, HE Qing-hu, et al(唐 宇, 肖 丹, 何清湖, 等). China Journal of Traditional Chinese Medicine and Pharmacy(中华中医药杂志), 2021, 36(8): 4681.
[7] Ye S, Zhong J, Huang J P, et al. Biomedicine & Pharmacotherapy, 2021, 141: 111832.
[8] CHEN Qian-jin, YU Dong-fang, FENG Dan-kai(陈前进, 余东方, 冯淡开). Guiding Journal of Traditional Chinese Medicine and Pharmacy(中医药导报), 2009, 15(2): 79.
[9] YUE Shi-yan, ZHOU Rong-rong, NAN Tie-gui, et al(岳世彦, 周荣荣, 南铁贵, 等). China Journal of Chinese Materia Medica(中国中药杂志), 2022, 47(10): 2689.
[10] ZHOU Li-shi, PAN Xiao-yan, QIU Wen-xi, et al(周礼仕, 潘小燕, 邱雯曦, 等). Journal of Chinese Medicinal Materials(中药材), 2021，(10): 2382.
[11] Chen M, Xuan H, Jiao M, et al. Revista Brasileira De Farmacognosia, 2018, 28(3): 267.
[12] Tulshidas S P, Ashwini S D, Shirish D. Critical Reviews in Analytical Chemistry, 2018, 48(6): 492.
[13] Wieme J, Mollazade K, Malounas I, et al. Biosystems Engineering, 2022, 222: 156.
[14] ZHOU Cong, WANG Hui, YANG Jian, et al(周 聪, 王 慧, 杨 健, 等). China Journal of Chinese Materia Medica(中国中药杂志), 2022, 47(22): 6027.
[15] WANG Guang-lai, WANG En-feng, WANG Cong-cong, et al(王广来, 王恩凤, 王聪聪, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2022, 42(11): 3626.
[16] Huang H, Hu X, Tian J, et al. Food Chemistry, 2021, 359(8): 129954.
[17] Zhang C, Wu W Y, Zhou L, et al. Food Chemistry, 2020, 319: 126536.
[18] Liu Y, Zhou S, Han W, et al. Analytica Chimica Acta, 2019, 1086: 46.
[19] Zhang X, Zhao H. Information Fusion, 2021, 74: 132.
[20] Tan W, Sun L, Yang F, et al. Optik, 2018, 154: 581.
[21] FENG Zhe, LI Wei-hao, CUI Di(冯 喆, 李卫豪, 崔 笛). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2021, 52(S1): 466.
[22] YAN Jing-wen, CHEN Hong-da, LIU Lei(闫敬文, 陈宏达, 刘 蕾). Optics and Precision Engineering(光学精密工程), 2019, 27(3): 680.
[23] LI Yu, ZHEN Chang, SHI Xue, et al(李 玉, 甄 畅, 石 雪, 等). Control and Decision(控制与决策), 2021, 36(5): 1119.
[24] ZHAO Liang, WANG Li-guo, LIU Dan-feng(赵 亮, 王立国, 刘丹凤). National Remote Sensing Bulletin(遥感学报), 2019, 23(5): 904.
[25] Wang Q, Zhang F, Li X. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56: 5910.
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