Nondestructive Measurement of Sugar Content of Hami Melon Based on Diffuse Reflectance Hyperspectral Imaging Technique
MA Ben-xue1,2, XIAO Wen-dong1,2, QI Xiang-xiang1,2, HE Qing-hai1,2, LI Feng-xia1,2
1. College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China 2. Agricultural Machinery Key Laboratory of Xinjiang BINGTUAN, Shihezi 832003, China
Abstract：The research on nondestructive test for detecting the sugar content of Hami melon by the technology of hyperspectral imaging was put forward. The research used the hyperspectral imaging system to get the diffuse reflective spectrum information (400～1 000 nm) of anilox class Hami melon sugar content, chose effective whole wavelength(500～820 nm)to do the modeling regression analysis the sugar content of Hami melon. The research compared the correction method of MSC and SNV, and also compared the influence of accuracy of modeling in terms of the spectrum pretreatment methods of original spectrum, first order differential, second order differential; Using the methods of PLS, SMLR and PCR, the comparative analysis of sugar content detection model effect with skin Hami melon and peel Hami melon was conducted. The results showed that after the original spectrum being processed by MSC and first order differential spectrum, modeling effect could be very good using the method of PLS and SMLR. Synthesizing correction set correlation coefficient and forecast modeling effect, it’s feasible to detect the sugar content of skin Hami melon by the PLS method, with a correction sample correlation coefficient (Rc) of 0.861 and the lower root mean square errors of correction (RMSEC) of 0.627, and a prediction sample correlation coefficient (Rp) of 0.706 and root mean square errors of prediction (RMSEP) of 0.873. The best effect to detecti the sugar content of peel Hami melon was obtained by the SMLR method with a correction sample correlation coefficient (Rc) of 0.928 and the lower root mean square errors of correction (RMSEC) of 0.458, with a Prediction sample correlation coefficient (Rp) of 0.818 and root mean square errors of prediction (RMSEP) of 0.727. The results of this study indicate that the technology of hyperspectral imaging can be used to predict the sugar content of Hami melon.
[1] Dull G G, Birth G S, Smittle D A, et al. Journal of Food Science, 1989, 54(2): 393. [2] Dull G G, Leffler R G, Birth G S. HortScience, 1990, 25: 1132. [3] Dull G G, Leffler R G, Birth G S, et al. Transactions of the ASAE, 1992, 35(2): 735. [4] Tsuta M, Sugiyama J, Sagara Y. Journal of Agricultural and Food Chemistry, 2002, 50: 48. [5] Maruo T, Ito T. Acta Horticulture, 2002, 588: 373. [6] TIAN Hai-qing, YING Yi-bin, XU Hui-rong, et al(田海清, 应义斌, 徐惠荣, 等). Transaction of the Chinese Society for Agricultural Machinery(农业机械学报), 2007, 38(5): 111. [7] TIAN Hai-qing, WANG Chun-guang, WU Gui-fang(田海清, 王春光, 吴桂芳). Transaction of the Chinese Society for Agricultural Machinery(农业机械学报), 2010, 41(12): 130. [8] Zhao Jiewen, Vittayapadung Saritporn, Chen Quansheng, et al. Maejo International Journal of Science and Technologr, 2009, 3(01): 130. [9] MIAO Fu-sheng, MA Yi, WANG Xi-yuan, et al(苗福生, 马 毅, 汪西原, 等). Journal of Ningxia University·Natural Science Edition(宁夏大学学报·自然科学版), 2011, 32(2): 130. [10] LIU Yan-de, CHEN Xing-miao, OUYANG Ai-guo(刘燕德, 陈兴苗, 欧阳爱国). Acta Optica Sinica(光学学报), 2008, 28(3): 478. [11] WANG Jia-hua, LI Peng-fei, CAO Nan-ning, et al(王加华, 李鹏飞, 曹楠宁, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2009, 28(5): 386. [12] PENG Yan-kun, HUANG Hui, WANG Wei, et al(彭彦昆, 黄 慧, 王 伟, 等). Journal of Jiangsu University·Natural Science Edition(江苏大学学报·自然科学版), 2011, 32(2): 1671. [13] XU Hui-rong, CHEN Xiao-wei, YING Yi-bin(徐惠荣, 陈晓伟, 应义斌). Transaction of the Chinese Society for Agricultural Machinery(农业机械学报), 2010, 41(12): 126. [14] YUE Rong, GUO Wen-chuan, LIU Hui(岳 绒, 郭文川, 刘 卉). Food Science(食品科学), 2011, 32(10): 141.
