光谱学与光谱分析 |
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A Rapid Nondestructive Measurement Method for Assessing the Total Plate Count on Chilled Pork Surface |
TAO Fei-fei, WANG Wei, LI Yong-yu, PENG Yan-kun*,WU Jian-hu, SHAN Jia-jia, ZHANG Lei-lei |
College of Engineering, China Agricultural University, Beijing 100083, China |
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Abstract The present paper proposed a method based on the hyperspectral technology for rapidly, nondestructively quantify the total plate count on chilled pork surface. In the research, 50 chilled pork samples stored at 4 ℃ for 1-14 days were used to study the relationship between the total plate count on chilled pork surface and their hyperspectral images collected in 400-1 100 nm. Two models were established using MLR and PLSR methods,and the prediction showed that they can both give satisfactory results with Rv=0.886 and 0.863 respectively. The overall research demonstrates that the hyperspectral technology can well quantify the total plate count on chilled pork surface, and so indicates that it is a valid tool to assess the quality and safety properties of chilled pork in the future.
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Received: 2010-03-02
Accepted: 2010-06-06
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Corresponding Authors:
PENG Yan-kun
E-mail: ypeng@cau.edu.cn
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[1] LIU Xue-hao, SUN Lian-fu(刘学浩, 孙连富). Meat Research(肉类研究), 2003,(1): 16. [2] LU Zhi, ZHU Jun-ling, MA Li-zhen(卢 智, 朱俊玲, 马俪珍). Meat Industry(肉类工业),2004,(3): 8. [3] ZHANG Zi-ping(张子平). Food Science(食品科学),2001, 22(1): 83. [4] Philippe R, Marie C, Veronique N, et al. International Journal of Food Microbiology,2004,96: 49. [5] LI Xiao-bo(李晓波). Meat Research(肉类研究),2008,(9): 41. [6] WANG Chang-yuan, MA Wan-long, JIANG Yu-nan(王长远, 马万龙, 姜昱男). Academic Periodical of Farm Products Processing(农产品加工·学刊),2007,(10): 75. [7] ZHANG Jie-mei(张洁梅). Modern Food Science and Technology(现代食品科技),2005, 21(2): 221. [8] Ellis D I, Broadhurst D, Kell D B, et al. Applied and Environment Microbiology, 2002,68(6): 2822. [9] Ellis D I, Broadhurst D, Goodacre R. Analytica Chimica Acta, 2004, 514(2): 193. [10] Ellis D I, Broadhurst D, Clarke S J, et al. Analyst, 2005, 130(12): 1648. [11] Herrero A M. Food Chemistry, 2008,107(4): 1642. [12] Ammor M S, Argyri A, George-John E. Meat Science, 2009, 81(3): 507. [13] Peng Y, Zhang J, Wu J, et al. Hyperspectral Scattering Profiles for Prediction the Microbial Speilage of Beef. in: SPIE Conference, Orlando, 13-17 April, 2009. 73150Q1. [14] Argyri A A , Panagou E Z, Tarantilis P A, et al. Sensors and Actuators B: Chemical, 2009, 145(1): 146. [15] Peng Y, Lu R. Journal of Food Engineering, 2007, 82: 142. [16] Peng Y, Wang W. Prediction of Pork Meat Total Viable Bacteria Count Using Hyperspectral Imaging System and Support Vector Mathines. in: ASABE, St. Joseph, Mich., June 29-July 2, 2008. 085438. [17] Peng Y, Wu J. Hyperspectral Scattering Profiles for Prediction of Beef Tenderness. in: ASABE, St. Joseph, Mich., June 29-July 2, 2008, 080004. [18] XUE Li-hong, YANG Lin-zhang(薛利红, 杨林章). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2009, 29(4): 926. [19] Ministry of Public Health of P. R. of China(中华人民共和国卫生部). GB/T 4789.2(食品卫生微生物学检验菌落总数测定). Beijing: Standards Press of China(北京:中国标准出版社),2009. 1. [20] LI Miao-yun, SUN Ling-xia, ZHOU Guang-hong, et al(李苗云,孙灵霞,周光宏, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2008,24(4):235. [21] George-John E N, Panos N S, Chrysoula C T. Meat Science, 2008,78: 77.
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