Abstract:In order to realize rapid identification of dried red jujubes, this paper proposes a method based on near-infrared hyperspectral imaging technology. The near-infrared hyperspectral images (1 000~1 600 nm) of 240 samples in total from 4 cultivars of dried red jujubes will be acquired. The samples are to be divided into the calibration set and the prediction set in the ratio of 2∶1. 7, 8, 10 effective wavelengths are to be selected by principal component analysis(PCA), x-loading weight(x-LW)and successive projection algorithm(SPA) respectively. The dimensionality of original hyperspectral images will be reduced with PCA, and texture features of the first principal component image are to be extracted with gray-level co-occurrence matrix(GLCM).The partial least squares-discriminant analysis(PLS-DA), back propagation neural network(BPNN)and least square support vector machine(LS-SVM) are to be applied to build identification models with the selected effective wavelengths, texture features and fusion of the former two features. The identification rates of the models based on fusion features will be higher than those of models based on the spectral features or texture features respectively. The BPNN models based on the fusion features will obtain the best results, whose identification rates of prediction set are to be 100%. The results in this paper indicate that the near-infrared hyperspectral imaging technology has great potential to identify the dried red jujubes rapidly.
Key words:Near-infrared hyperspectral imaging;Dried red jujube;Identification;Texture features;Features fusion
[1] CHENG Shu-xi, KONG Wen-wen, ZHANG Chu, et al(程术希, 孔汶汶, 张 初, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014,34(9): 2519. [2] Ashabahebwa Ambrose, Lalit Mohan Kandpal, Moon S Kim, et al. Infrared Physics & Technology, 2016,(75): 173. [3] Wu D, Yang H Q, Chen X Y, et al. Journal of Food Engineering, 2008, 88(4): 474. [4] Arvin R Yadav, R S Anand R S, Dewal M L, et al. Applied Soft Computing 2015, (32): 101. [5] Alireza Pourreza, Hamidreza Pourreza, Mohammad-Hossein Abbaspour-Fard, et al. Computers and Electronics in Agriculture, 2012,(83): 102. [6] Wang Lu, sun Dawen, Pu Hongbin, et al. Food Analytical Methods, 2016, (9): 225. [7] Macho S, Iusa R, Callao M P, et al. Analytica Chimica Acta, 2001, 445(2): 213. [8] Kamruzzaman M, Sun D W, EIMasry G, et al. Talanta, 2013, (103): 130. [9] Liu F, He Y, Wang L. Analytica Chimica Acta, 2008, 615(1): 10. [10] Wu D, Nie P C, He Y, et al. Analytica Chimica Acta, 2010, 659(1-2): 229. [11] ZHU Xiao-li(褚小立). Molecular Spectroscopy Analytical Technology Combined with Chemometrics and Its Applications(化学计量学方法与分子光谱分析技术). Beijing: Chemical Industry Press(北京:化学工业出版社),2011.