| 光谱学与光谱分析 |
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| Sugar Characterization of Mini-Watermelon and Rapid Sugar Determination by Near Infrared Diffuse Reflectance Spectroscopy |
| WANG Shuo1, YUAN Hong-fu1*, SONG Chun-feng1, XIE Jin-chun1, LI Xiao-yu1, FENG Le-ping2 |
1. College of Material Science and Engineering, Beijing University of Chemical Technology，Beijing 100029，China
2. The Leping Agricultural Products Production and Marketing Co. Ltd., Beijing Daxing District Panggezhuang Town Sigezhuang Village, Beijing 102600, China |
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Abstract In the present paper, the distribution of sugar level within the mini-watermelon was studied, a new sugar characterization method of mini-watermelon using average sugar level, the highest sugar level and the lowest sugar level index is proposed. Feasibility of nondestructive determination of mini-watermenlon sugar level using diffuse reflectance spectroscopy information was investigated by an experiment. PLS models for measuring the 3 sugar levels were established. The results obtained by near infrared spectroscopy agreed with that of the new method established above.
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Received: 2011-12-19
Accepted: 2012-03-20
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[1] Andrew McGlone V, Robert B Jordan, Paul J Martinsen.Post Harvest Biology and Technology, 2002, (25):135.
[2] Manuela Zude, Bernd Herold, Jean-Michel Roger, et al. Journal of Food Engineering, 2006, (77): 254.
[3] G Carlomagno, L Capozzo, G Atolio, et al. Infrared Physics and Technology, 2004, (46):23.
[4] Antihus Hernandez Gomez, Yong He, Annia Garcia Pereira. Journal of Food Engineering, 2006, (77): 313.
[5] ZHANG Fan，WANG Qian，MA Zhi-hong(张 帆，王 倩，马智宏). Food Science(食品科学), 2007，28(1)：258.
[6] TIAN Hai-qing，YING Yi-bin，XU Hui-rong(田海清，应义斌，徐惠荣). Journal of Agricultural Machinery(农业机械学报), 2007，38(5)：111.
[7] YUAN Lin，XU Huai-de，LI Yu-jin(袁 琳, 徐怀德, 李钰金). Journal of Chinese Institute of Food Science and Technology(中国食品学报), 2010，10(1)：272.
[8] Slaugter D C, Transactions of the ASAE, 1995, 38(2): 617.
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