1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China 2. Beijing Laboratory of Food Quality and Safety, China Agricultural University, Beijing 100083, China 3. National Engineering Technology Research Center for Preservation of Agricultural Products, Tianjin 300384, China 4. Tianjin Key Laboratory of Agricultural Products Postharvest Physiology and Storage, Tianjin 300384, China
Abstract：In view of the actual logistics process of table grapes and the situation that fresh keeping agents based on sulfur dioxide are commonly used in table grape logistics, we studied the shelf life prediction method of table grapes under 4 temperatures and constant concentrations of sulfur dioxide based on near infrared spectrum (NIR) and the evolution of texture in this work. Logistics process safety system based on shelf life prediction was designed to reduce the loss of table grapes in the logistics. The change of texture is an important cause of postharvest table grapes to end their shelf life in postharvest logistics. In this work, we used SO2 concentration sensors to control solenoid valves, and obtained the set SO2 concentrations by automatic compensation mechanism. The evolutions of table grape texture under different concentrations of sulfur dioxide were studied as well as the influence of temperature. The NIR pretreatment effects of multiplicative scatter correction and the first S-G derivation were compared. The table grape texture nondestructive testing model built base on NIR and partial least squares regression achieved a determination coefficient of 0.93 and the root mean squared error (RMSE) was 1.70. In full cross-validation, the prediction accuracy reached to 0.81 and got a RMSE of 2.91. Research indicated that the NIR detection combined with the quality change modeling and information technology could be used to improve the logistics process safety management efficiency of postharvest fruits and vegetables.
Key words：Near infrared spectrum;Table grapes;Shelf life prediction;Process safety
[1] TIAN Hai-qing, YING Yi-bin, XU Hui-rong(田海清，应义斌，徐惠荣). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2007, 38(5): 111. [2] SUN Xu-dong, ZHANG Hai-liang, OUYANG Ai-guo, et al(孙旭东，章海亮，欧阳爱国, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2009, 40(7): 129. [3] YAN Run, WANG Xin-zhong, QIU Bai-jing, et al(闫 润，王新忠，邱白晶, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2013, 44(9): 182. [4] CAO Fang, WU Di, HE Yong, et al(曹 芳，吴 迪，何 勇, 等). Acta Optica Sinica(光学学报), 2009, 29(2): 537. [5] Champa W A H, Gill M I S, Mahajan B V C, et al. Postharvest Biology and Technology, 2014, 91: 57. [6] Shiri M A, Bakhshi D, Ghasemnezhad M, et al. Turkish Journal of Agriculture and Forestry, 2013, 37: 48. [7] Smilanick J L, Margosan D M, Gabler F M. Ozone-Science & Engineering, 2002, 24: 343. [8] DENG Yun, WU Ying, LI Yun-fei(邓 云，吴 颖，李云飞). Transactions of the Chinese Society for Agricultural Machinery (农业机械学报), 2006, 37(8): 93. [9] ZHANG Xiu-yuan, HE Kuo, ZHAO Rui-ping, et al(张秀媛, 何 扩, 赵瑞平, 等). Food Science(食品科学), 2015, 36(10)：254. [10] Guzev L, Danshin A, Zahavi T, et al. International Journal of Food Science and Technology, 2008, 43: 1187. [11] Morris J R, Oswald O L, Main G L, et al. American Journal of Enology and Viticulture, 1992, 43: 230. [12] ZHANG Hai-yun, PENG Yan-kun, WANG Wei, et al(张海云，彭彦昆，王 伟, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2013, 44(4): 146. [13] WANG Ting-man, ZHANG Xiao-shuan, CHEN Wei, et al(汪庭满，张小栓，陈 炜, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2011, 27(9): 141. [14] FU Ze-tian, XING Shao-hua, ZHANG Xiao-shuan(傅泽田，邢少华，张小栓). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 44(7): 144. [15] HUANG Hua, ZHU Shi-ping, LIU Bi-zhen(黄 华，祝诗平，刘碧贞). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2014, 45(8): 294.
