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Nondestructive Testing for Yellow Peach Bruise and Soluble Solids Content by Hyperspectral Imaging |
LIU Yan-de, HAN Ru-bing, ZHU Dan-ning, MA Kui-rong, XIAO Huai-chun, SUN Xu-dong |
School of Mechatronics Engineering, East China Jiaotong University, Nanchang 330013, China |
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Abstract The surface damage and soluble solid content were detected simultaneously in online grading of yellow peach, and the damage level and soluble solid content are the important criteria for evaluating the quality of yellow peach. Hyperspectral imaging technology was used to detect the damage level and soluble solid content of yellow peach simultaneously. The principal component analysis was used to obtain the best PC image firstly. Then according to the contribution rate of characteristic wavelength to PC image, the best wavelength of the image (550 and 720 nm) was determined. In the last, the binaryzation, image masking, threshold segmentation and the related image processing technology were combined to qualitatively discriminate the spectral images corresponding to the best characteristic wavelength. Its accuracy was up to 92.9%. At the same time, partial least squares regression model was established to predict the SSC content of normal samples, and by the continuous optimization of the model, online simultaneous detection of yellow peach bruise and soluble solids based on the hyperspectral imaging technology was finally realized. The sorting accuracy of soluble solids was 79.2%. The experimental results show that the yellow peach bruise and SSC can be detected on-line simultaneously by using hyperspectral imaging technology. This research can provide references and basis for the online sorting.
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Received: 2016-09-27
Accepted: 2017-02-05
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[1] Zhang F, Zhao J, Chen F, et al. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27(6): 337.
[2] CHEN Yan-jun, ZHANG Jun-xiong, LI Wei, et al(陈艳军, 张俊雄, 李 伟, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(2): 284.
[3] Blasco J, Aleixos N, Gómez-Sanchis J, et al. Biosystems Engineering, 2009, 103(2): 137.
[4] Zhang B, Fan S, Li J, et al. Food Analytical Methods, 2015, 8(8): 2075.
[5] ZHANG Hai-liang, GAO Jun-feng, HE Yong(章海亮, 高俊峰, 何 勇). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报), 2013,(9): 177.
[6] HUANG Wen-qian, CHEN Li-ping, LI Jiang-bo, et al(黄文倩, 陈立平, 李江波, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(1): 272.
[7] ZHANG Bao-hua, HUANG Wen-qian, LI Jiang-bo, et al(张保华, 黄文倩, 李江波, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(5): 1367.
[8] ElMasry G, Wang N, Vigneault C, et al. LWT-Food Science and Technology, 2008, 41(2): 337.
[9] Baranowski P, Mazurek W, Wozniak J, et al. Journal of Food Engineering, 2012, 110(3): 345.
[10] GUO En-you, LIU Mu-hua, ZHAO Jie-wen, et al(郭恩有, 刘木华, 赵杰文, 等). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报), 2008, 39(5): 91.
[11] Sun D W. Hyperspectral Imaging for Food Quality Analysis and Control. London: Academic Press, 2010. 56.
[12] Siham Mourad, Pierre Valette-Florence. Journal of Business Research, 2016, 69(10): 4675.
[13] Mohamed Amine Atoui, Sylvain Verron, Abdessamad Kobi. IFAC Proceedings Volumes, 2014, 473.
[14] Yang Chen. Chemometrics and Intelligent Laboratory Systems, 2016, 156: 196.
[15] Jan-Michael Becker, Ida Rosnita Ismail. Eurpoean Management Journal, 2016, 34(6): 606.
[16] Kai-Inn Lim, Chia-Kai Liu, Chao-Long Chen, et al. Transplantation Proceedings, 2016, 48(4): 1074.
[17] Luo Yuan, Zhang Tian, Zhang Yi. Optik-International Journal for Light and Electron Optics, 2016, 127(2): 718.
[18] Fabiane Lacerda Francisco, Alessandro Morais Saviano, Túlia de Souza Botelho-Almeida, et al. Journal of Microbiological Methods, 2016, 124: 28.
[19] Ivan Portnoy, Kevin Melendez, Horacio Pinzon, et al. Control Engineering Practice, 2016, 50: 69. |
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