| 光谱学与光谱分析 |
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| Study on Online Detection Modeling Parameters of Jujube Internal Quality of Southern Xinjiang with Near Infrared Spectrometric Techniques |
| LUO Hua-ping1, 2, LU Qi-peng2*，DING Hai-quan2, GAO Hong-zhi2，GUO Ling3 |
1. College of Mechanic and Electrical Engineering, Tarim University, Alar 843300，China
2. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, State Key Laboratory of Applied Optics，Changchun 130033, China
3. College of Plant Science，Tarim University，Alar 843300, China |
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Abstract To establish the on-line near infrared spectral correction model for the jujube quality of Southern Xinjiang, the main influence factors of online testing results were analyzed, and the corresponding parameters were studied. First of all, the collecting conditions of different jujube were set, such as measurement condition, spectral region, and the parameters of the apparatus. With near infrared spectrometer and self-designed jujube batch collecting attachment, the quality spectrum of jujube was obtained, and combining spectral preprocessing and detection precision, condition parameters were selected. Secondly, through PLS spectrum correction with different modeling parameters and two-dimensional correlation spectroscopy analysis, Brix characteristic spectral parameters were selected. The results showed that with sugar degree central wavelength 9 116, 9 418 and 10 500 cm-1, acquisition resolution 16 cm-1, and scan number 8, the sugar degree relative error was 8%～10%, the size of single grain jujube spectra was reduced to 1/10 of the original, and the time was reduced to 3 seconds. It was concluded that with the experimental parameters, the spectra were compressed, a primary online correction model was established, and the jujube quality on-line detection with near infrared spectroscopy was basically realized.
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Received: 2011-10-13
Accepted: 2012-01-23
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Corresponding Authors:
LU Qi-peng
E-mail: luqipeng@126.com
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[1] WANG Dong-min, JIN Shang-zhong, CHEN Hua-cai, et al(王动民, 金尚忠, 陈华才，等). Opt. Precision Eng.(光学精密工程), 2008，16: 2051.
[2] GUO Zhi-ming, ZHAO Jie-wen, CHEN Quan-sheng, et al(郭志明, 赵杰文, 陈全胜, 等). Opt. Precision Eng.(光学精密工程), 2009, 17: 1839.
[3] HUANG Fu-rong, PAN Tao, ZHANG Gan-lin, et al(黄富荣, 潘 涛, 张甘霖, 等). Opt. Precision Eng.(光学精密工程), 2010, 18: 586.
[4] Lin H, Ying Y. Sensing and Instrumentation for Food Quality and Safety, 2009, 3(2): 130.
[5] Antonucci F, Pallottino F, Paglia G, et al. Food and Bioprocess Technology, 2010, 4(5): 809.
[6] JI Shu-juan, BAI Lan, LI Dong-hua, et al(纪淑娟, 柏 兰, 李东华, 等). Science and Technology of Food Industry(食品工业科技), 2008, 29: 281, 286.
[7] HE Dong-jian, Takaa kiMaekawa, HiroshiMorishima(何东健, 前川孝昭, 森岛博). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2001, 17: 146.
[8] ZHANG Xiao-yu, WANG Ting-xin, XIE Fei, et al(张晓瑜, 王庭欣, 谢 飞, 等). Science and Technology of Food Industry(食品工业科技), 2010, 31: 111.
[9] Wang J, Nakano K, Ohashi S. LWT Food Science and Technology, 2011, 44(4): 1119.
[10] Wang J, Nakano K, Ohashi S. Postharvest Biology and Technology, 2011, 59(3): 272.
[11] LU Wan-zhen, YUAN Hong-fu, XU Guang-tong, et al(陆婉珍, 袁洪福, 徐广通，等). Modern Near Infrared Spectroscopy Analytical Technology(2nd Ed)(现代近红外光谱分析技术, 第2版). Beijing: China Petrochemical Press(北京: 中国石化出版社), 2007.
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