Nondestructive Discrimination of Waxed Apples Based on Hyperspectral Imaging Technology
GAO Jun-feng1, ZHANG Hai-liang1, KONG Wen-wen1, HE Yong1, 2*
1. College of Biosystems Engineering and Food Science, Zhejiang University,Hangzhou 310058,China 2. Cyrus Tang Center for Sensor Materials and Applications, Zhejiang University,Hangzhou 310058,China
Abstract:The potential of hyperspectral imaging technology was evaluated for discriminating three types of waxed apples. Three types of apples smeared with fruit wax, with industrial wax, and not waxed respectively were imaged by a hyperspectral imaging system with a spectral range of 308~1 024 nm. ENVI software processing platform was used for extracting hyperspectral image object of diffuse reflection spectral response characteristics. Eighty four of 126 apple samples were selected randomly as calibration set and the rest were prediction set. After different preprocess, the related mathematical models were established by using the partial least squares (PLS), the least squares support vector machine (LS-SVM) and BP neural network methods and so on. The results showed that the model of MSC-SPA-LSSVM was the best to discriminate three kinds of waxed apples with 100%, 100% and 92.86% correct prediction respectively.
[1] Els A Veraverbeke, Jeroen Lammertyn, Stijn Saevels, et al. Postharvest Biology and Technology, 2001, 23(3): 197. [2] Stephen J Kenney, Larry R Beuchat. International Journal of Food Microbiology, 2002, 77(3): 257. [3] Glenn G M, Rom C R, Rasmussen H P. Scientia Horticulturae, 1990, 42(4): 289. [4] Gabriel A, Leiva-Valenzuela, Lu Renfu,et al. Journal of Food Engineering, 2013, 115(1): 91. [5] Ariana D, Lu R, Guyer D E. Computers and Electronics in Agriculture, 2006, 53(1): 60. [6] Chao K, Yang C C, Kim M S, et al. Applied Engineering in Agriculture, 2008, 24(4): 475. [7] Roggo Y, Edmond A, Chalus P. Analytica Chimica Acta, 2005, 535(1-2): 79. [8] Douglas Barbin, Gamal Elmasry, Sun Dawen, et al. Meat Science,2012, (90): 259. [9] Masoud Taghizadeh, Aoife A. Gowen, Colm P O’Donnell. Computers and Electronics in Agriculture, 2011, 77(1): 74. [10] SHI Ji-yong, ZOU Xiao-bo,ZHAO Jie-wen, et al(石吉勇, 邹小波, 赵杰文, 等). Journal of Jiangsu University: Natural Science Edition(江苏大学学报·自然科学版),2011, 32(2): 134. [11] Suykens J A K, Vandewalle J. Neural Processing Letters, 1999, 9(3): 293. [12] Li Xiaoli, Nie Pengcheng, He Yong, et al. Expert System with Applications, 2011, 38(9): 11149. [13] CAO Fang, WU Di, ZHENG Jin-tu, et al(曹 芳,吴 迪,郑金土, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2011, 31(4):920. [14] Wu Di, He Yong, Feng Shuijuan, et al. Journal of Food Engineering, 2008, 84(1): 124. [15] HE Yong, LI Xiao-li (何 勇, 李晓丽). Journal of Infrared and Millimeter Waves(红外与毫米波学报),2006, 25(3): 192. [16] WU Di, WU Hong-xi, CAI Jing-bo, et al(吴 迪, 吴洪喜, 蔡景波, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报),2009, 28(6): 423. [17] HUANG Ling-xia, WU Di, JIN Hang-feng, et al(黄凌霞,吴 迪,金航峰, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2010,26(2): 231. [18] ZHANG Xiao-chao,WU Jing-zhu, XU Yun(张小超,吴静珠,徐 云). Near Infrared Speactroscopy Analysis Technology and Its Application in Modern Agriculture(近红外光谱分析技术及其在现代农业中的应用). Beijing: Publishing House of Electronics Industy(北京:电子工业出版社),2012.