Abstract：Black Goji berry contains various nutrients such as cyanidin, polysaccharides, trace elements and so on, and has extremely high economic and medical value, the similar Nitraria Tanggu impersonates in the market. The market price of Nitraria Tangguis low. Hyperspectral image technology combines image and spectrum in one, commonly used in food detection and recognition. This study combined with hyperspectral image technology to non-destructively identify Black Goji Berry and nitraria tanggu. Hyperspectral reflection spectra of Black Goji Berry (180) and nitraria Tanggu (180) in the range of 900~1 700 nm were collected respectively, a total of 254 bands. Removing the first 22 abnormal bands and using the last 232 bands as model inputs. Kennard-Stone method is used to divide samples, correction set∶prediction set=2∶1. The successive projections algorithm (SPA) method is used for spectral dimensionality reduction, setting the characteristic wavelength range to 0~30, which extracts 20 characteristic wavelengths. The full spectrum and 20 characteristic wavelengths extracted by SPA are used as model inputs to establish support vector machine (SVM) and extreme learning machine (ELM) models to identify Black Goji Berry and nitraria Tanggu. The results show that the recognition rates of the SVM model based on FS and SPA are both 100%, the recognition rates of the ELM model based on FS and SPA are both 100%, the SPA method can reduce model input without reducing the accuracy of model recognition. The input is only 8.62% of FS, which greatly reduces the number of model calculations. This study provides a theoretical basis for identifying Black Goji Berry and nitraria Tanggu.
Key words：Hyperspectral; Model; Identify; Black Goji Berry; Nitraria Tanggu
赵 凡，闫昭如，宋海燕. 应用高光谱鉴别黑枸杞和唐古特白刺果[J]. 光谱学与光谱分析, 2021, 41(07): 2240-2244.
ZHAO Fan, YAN Zhao-ru, SONG Hai-yan. Hyperspectra Used to Recognize Black Goji Berry and Nitraria Tanggu. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 2240-2244.
[1] ZHAO Xu, WANG Xin-ru，DUAN Chang-qing, et al(赵 旭, 王新茹, 段长青, 等). Food Science(食品科学), 2019, 40(8): 202.
[2] Yahui L, Xiaobo Z, Tingting S, et al. Food Analytical Methods, 2016, 10(4): 1.
[3] Tian Z, Aierken A, Pang H, et al. Journal of Liquid Chromatography & Related Technologies, 2016: 10826076.2016.1179201.
[4] SUN Xiao-hong, WANG Tong, LÜ Kang-wen, et al(孙晓红, 王 潼, 吕康文，等). Journal of Nutrition(营养学报)，2016, 38(5): 509.
[5] Liu Q, Sun K, Peng J, et al. Food Analytical Methods, 2018, 11(5): 1518.
[6] Dong J, Guo W, Zhao F, et al. Food Analytical Methods, 2017, 10(2): 477.
[7] Yang D, He D, Lu A, et al. Applied Spectroscopy, 2017，71(10): 2286.
[8] ZHAO Fan, DONG Jin-lei, GUO Wen-chuan(赵 凡，董金磊，郭文川). Modern Food Science and Technology(现代食品科技)，2016, 32(4): 223.
[9] Dong J L, Guo W C, Wang Z W, et al. Food Analytical Methods, 2016, 9(5): 1087.
[10] ZHU Xiao-lin, LI Guang-hui, ZHANG Meng(朱晓琳，李光辉，张 萌). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(11)：3547.
[11] D’Archivio A A, Foschi M, Aloia R, et al. Food Chemistry, 2019, 275: 333.
[12] GUO Wen-chuan, ZHU De-kuan, ZHANG Qian, et al(郭文川, 朱德宽，张 乾，等). Transactions of the Chinese Society for Agricultural Machinery（农业机械学报），2020，51(9): 350.
[13] DONG Jin-lei, GUO Wen-chuan(董金磊，郭文川). Food Science(食品科学)，2015，36(16)：101.
[14] FENG Yu, CUI Ning-bo, et al(冯 禹, 崔宁博，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(S1): 153.
[15] LEI Jian-gang, LIU Dun-hua（雷建刚，刘敦华）. Food Science（食品科学）, 2013, 34(20): 148.
