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Study on Nondestructive Identification of Panax Notoginseng Powder Quality Grade Based on Hyperspectral Imaging Technology |
ZHANG Fu-jie, SHI Lei, LI Li-xia*, ZHAO Hao-ran, ZHU Yin-long |
Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
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Abstract Panax notoginseng powder is the main consumption and commodity form of panax notoginseng. There are shoddy or even adulterated phenomena in the market. As panax notoginseng powder is a powdery material, it is not easy to distinguish with the naked eye. In order to identify the quality grade of panax notoginseng powder, visible near-infrared hyperspectral imaging technology was used to identify the panax notoginseng powder with different quality grades. The taproots of panax notoginseng of 30 heads, 40 heads, 60 heads and 80 heads were ground into powder to prepare samples. The hyperspectral image of 384 samples of four quality grades was acquired by using a visible near-infrared hyperspectral imaging system(400.68~1 001.612 nm). Region of interest (ROI) was extracted from the hyperspectral image, and the average spectral value of samples was calculated. 384 samples of panax notoginseng powder were divided into training sets and test sets in a ratio of 2∶1. The original spectra of panax notoginseng powder were preprocessed using multiplication scatter correction (MSC), Savitzky-Golay (SG) and standard normal variable (SNV), and the support vector machine (SVM) was employed to form the classification models based on MSC, SG and SNV. By comparing the classification accuracy of SVM models based on MSC, SG and SNV, it was found that SNV had the best effect on preprocessing. Iterative reserved information variable (IRIV), variable combined cluster analysis (VCPA) and variable combined cluster analysis and iterative reserved information variable (VCPA-IRIV) were adopted to extract feature wavelengths from the spectra after SNV pretreatment, and the SVM was employed to form the classification models based on feature spectra and original spectra. By comparing the range of feature wavelengths and the classification accuracy of SVM models based on IRIV, VCPA and VCPA-IRIV, it was found that VCPA-IRIV, which combines VCPA and IRIV, had the best effect on feature selection. VCPA-IRIV extracted 18 feature wavelengths to participate in the modeling instead of the full spectra, and the algorithm can reduce the complexity of the model while maintaining the model’s classification accuracy. In order to improve the classification accuracy of the model, the gravitational search algorithm (GSA) was introduced to search the optimal parameters(c,g) in the SVM model and compared with Grid Search (GS). The results indicated that the VCPA-IRIV-GSA-SVM model has the best classification effect, and the classification accuracy of the test set reached 100%. Thus, it is feasible to use visible near-infrared hyperspectral imaging technology to identify the quality grade of panax notoginseng powder. This method references the quality grade identification of panax notoginseng powder in the market.
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Received: 2021-06-10
Accepted: 2021-10-12
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Corresponding Authors:
LI Li-xia
E-mail: lilixia2012@kust.edu.cn
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[1] Chinese Pharmacopoeia Commission(国家药典委员会). The Pharmacopoeia of the People’s Republic of China: Part 1(中华人民共和国药典: 一部). Beijing: China Medical Science and Technology Press(北京: 中国医药科技出版社), 2015. 11.
[2] Meng Zhe, Huang Yang, Wang Lijun, et al. Separation Science Plus, 2020, 3(6): 200.
[3] Li Chao, Qin Yunhua, Yang Qianxu, et al. Journal of Pharmaceutical and Biomedical Analysis, 2020,182: 113127.
[4] Yang Xiaodong, Song Jie, Peng Lin, et al. Infrared Physics and Technology, 2019. 103: 103101.
[5] Zhou Yuhou, Zuo Zhitian, Xu Furong, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 226: 117619.
[6] Cong Sunli, Sun Jun, Mao Hanping, et al. Journal of the Science of Food and Agriculture, 2018, 98(4): 1453.
[7] SUN Ting, TIAN Jian-ping, HU Xin-jun, et al(孙 婷, 田建平, 胡新军, 等). Food and Fermentation Industries(食品与发酵工业), 2021, 47(5): 186.
[8] SUN Jun, LU Xin-zi, ZHANG Xiao-dong, et al(孙 俊, 路心资, 张晓东, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2016, 47(6): 215.
[9] Wang Zheli, Tian Xi, Fan Shuxiang, et al. Infrared Physics and Technology, 2021, 112: 103596.
[10] Weng Shizhuang, Tang Peipei, Yuan Hecai, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 234: 118237.
[11] Jennifer Dumont, Tapani Hirvonen, Ville Heikkinen, et al. Computers and Electronics in Agriculture, 2015, 116: 118.
[12] LIU Da-hui, XU Na, GUO Lan-ping, et al(刘大会, 徐 娜, 郭兰萍, 等). China Journal of Chinese Materia Medica (中国中药杂志), 2016, 41(5): 776.
[13] Shrestha S, Matej K, Zibrat U, et al. Sensors and Actuators B: Chemical, 2016, 237: 1027.
[14] YIN Wen-jun, RU Chen-lei, ZHENG Jie, et al(殷文俊, 茹晨雷, 郑 洁, 等). China Journal of Chinese Materia Medica(中国中药杂志), 2021, 46(4): 923.
[15] Yao Kunshan, Sun Jun, Zhou Xin, et al. Journal of Food Process Engineering, 2020, 43(7): e13422.
[16] SUN Zong-bao, WANG Tian-zhen, LIU Xiao-yu, et al(孙宗保, 王天真, 刘小裕, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(10): 3224.
[17] WANG Sheng-lin,YANG Chong-shan,LIU Zhong-yuan, et al(王盛琳, 杨崇山, 刘中原, 等). Journal of Tea Science(茶叶科学), 2021, 41(2): 251.
[18] Li Yating, Sun Jun, Wu Xiaohong, et al. Journal of Food Science, 2019, 84(8): 2234.
[19] CHEN Yu-xin, YIN Xiao-chuan, TAN Ren(陈玉鑫, 殷肖川, 谭 韧). Journal of Air Force Engineering University·Natural Science Edition(空军工程大学学报·自然科学版), 2018, 19(5): 78.
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