光谱学与光谱分析 |
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Applied Research in Grade Estimation of Surimi by Near Infrared Spectroscopy |
WU Hao, CHEN Wei-hua, WANG Xi-chang*, LIU Yuan* |
College of Food Science and Technology, Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, Shanghai Ocean University, Shanghai 201306, China |
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Abstract The feasibility of utilizing near infrared spectroscopy for estimating frozen and thawed white croaker surimi with different grades was presented in the research. First-derivative and standard normal variable transformation were used as pretreatment method, then principal component analysis was carried out on the processed datas. Establish grade estimation model on white croaker surimi with different grades by principal component analysis-mahalanobis distance pattern recognition method. Seven kinds of physicochemical indexes (moisture, protein, crude fat, salt-soluble protein, gel strength, water-holding ability and whiteness) of white croaker surimi with different grades were determinated. We came to the following conclusions. Firstly, white croaker surimi with three grade could be distinguished effectively by principal component analysis. Secondly, the model of grade estimation established by principal component analysis-mahalanobis distance pattern recognition method had better performance on frozen white croaker surimi than thawed ones, the former’s comprehensive accuracy was 96.3% with the latter’s is 83.3%. Thirdly, the physicochemical indexes of white croaker surimi with different grades had some distinctions. The research indicated that near infrared spectroscopy could estimate the grade of white croaker surimi rapidly and nondestructively.
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Received: 2013-10-04
Accepted: 2014-06-14
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Corresponding Authors:
WANG Xi-chang, LIU Yuan
E-mail: xcwang@shou.edu.cn; yliu@shou.edu.cn
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[1] SHEN Yue-xin(沈月新). Aquatic Food Science(水产食品学). Beijing: China Agriculture Press(北京:中国农业出版社), 2001. [2] LU Ye, WANG Xi-chang, LIU Yuan(陆 烨,王锡昌,刘 源). Journal of Fisheries of China(水产学报), 2011, 35(8): 1273. [3] The Ministry of Agriculture(农业部). China Statistical Yearbook of Fishery in 2012(2012中国渔业统计年鉴). Beijing: China Agriculture Press(北京:中国农业出版社), 2012. [4] HUANG Jian-lian, ZHOU Wen-guo, CHEN Mei-mei, et al(黄建联,周文果,陈梅妹,等). Journal of Fujian Fisheries(福建水产), 2013, 35(1): 58. [5] LI Ang, GAO Tian-xiang, SUN Dian-rong(李 昂,高天翔,孙典荣). Journal Fishery Sciences of China(中国水产科学), 2010,17(6):1166. [6] LI Kai-ge, HAN Dong-hai, SUN Ming(李凯歌,韩东海,孙 明). Journal of Agricultural Mechanization Research(农机化研究), 2008,8: 145. [7] CHENG Fang, FAN Yu-xia, LIAO Yi-tao(成 芳,樊玉霞,廖宜涛). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2012, 32(2): 354. [8] ZHANG Jing-ya, ZHANG Jian-xin, YU Xiu-zhu(张静亚,张建新,于修烛). Food Science(食品科学), 2012, 33(4): 200. [9] CUI Yan-na, DAI Zhi-yuan, ZHANG Zhi-guang, et al(崔雁娜,戴志远,张志广,等). Science and Technology of Food Industry(食品工业科技), 2009, 30(3): 68. [10] CHEN Hai-hua, XUE Chang-hu(陈海华,薛长湖). Chinese Society of Agricultural Engineering(农业工程学报), 2009, 25(5): 293. [11] ZHOU Rui, ZENG Qing-xiao, ZHU Zhi-wei, et al(周 蕊,曾庆孝,朱志伟,等). Modern Food Science and Technology(现代食品科技), 2008, 24(8): 759. [12] LU Jian-feng, SHAO Ming-shuan, LIN Lin, et al(陆剑锋,邵明栓,林 琳,等). Chinese Society of Agricultural Engineering(农业工程学报), 2011, 27(11): 372. [13] WANG Xi-chang, LU Ye, LIU Yuan(王锡昌,陆 烨,刘 源). Food Science(食品科学), 2010, 31(16): 168. [14] LU Wan-zhen(陆婉珍). Morden Near Infrared Spectroscopy Analytical Technology(现代近红外光谱分析技术). Beijing: China Petrochemical Press(北京:中国石化出版社), 2007. [15] Malley D F, Mcclure C, Martin P D. Communication in Soil Science and Plant Analysis, 2005, 36(4): 455. [16] ZHOU Zi-li, ZHANG Yi-fang, HE Yong, et al(周子立,张怡芳,何 勇,等). Software Engineer(软件工程师), 2010, 5: 53. [17] Shimamoto Junji, Hiratsuka Seiichi, Hasegawa Kaoru, et al. Fisheries Science, 2003, 69: 856. [18] Shimamoto Junji, Hasegawa Kaoru, Sato Minoru, et al. Fisheries Science, 2004, 70: 345. |
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