Research on Prediction Chemical Composition of Beef by Near Infrared Reflectance Spectroscopy
SUN Xiao-ming1, 2, LU Ling2, ZHANG Jia-cheng1, ZHANG Song-shan2, SUN Bao-zhong2*
1.Institute of Animal Husbandry and Veterinary, Chinese Academy of Agricultural Sciences，Beijing 100193, China 2.Food Science Department of Qingdao Agricultural University, Qingdao 266109, China
Abstract：This study established a near infrared reflectance spectroscopy models for exactly predicting the fat, protein and moisture of the ground and mince beef on line. Using our country’ SupNIR-1000 near infrared spectrometer, the models were set up by artificial neural network (ANN). Related coefficient of calibration (rC) of fat model of mince was 0.971 and related coefficient of prediction (rP) was 0.972.The protein’ rC and RP were 0.952 and 0.949, respectively. The moisture’ rC and rP were 0.938 and 0.927, respectively.Using ground beef established models, the fat’ rC and rP were 0.935 and 0.810; the protein’ rC and rP were 0.954 and 0.868; the moisture’ rC and rP were 0.930 and 0.913, respectively. So near infrared reflectance spectroscopy can better detect the fat, protein and moisture of mince than ground beef. But basically the ground beef model also can be used to quickly predict the chemical composition on line.
孙晓明1, 2，卢 凌2，张佳程1，张松山2，孙宝忠2* . 牛肉化学成分的近红外光谱检测方法的研究[J]. 光谱学与光谱分析, 2011, 31(02): 379-383.
SUN Xiao-ming1, 2, LU Ling2, ZHANG Jia-cheng1, ZHANG Song-shan2, SUN Bao-zhong2* . Research on Prediction Chemical Composition of Beef by Near Infrared Reflectance Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31(02): 379-383.
[1] Prieto N, Ross D W, Navajas E A, et al. Meat Science, 2009, 83: 96. [2] Bowling M B, Vote D J, Belk K E, et al. Meat Science, 2009, 82: 1. [3] NIU Zhi-you, HAN Lu-jia, SU Xiao-ou, et al(牛智有, 韩鲁佳, 苏晓鸥, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2006, 37(8): 126. [4] Prieto N, Andrés S, Giráldez F J, et al. Meat Science, 2008, 79: 692. [5] Sierra V, Aldai N, Castro P, et al. Meat Science, 2008, 78: 248. [6] Ripoll G, Albertí P, Panea B, et al. Meat Science, 2008, 80: 697. [7] Prieto N, Roehe R, Lavin P. Meat Science, 2009，83: 175. [8] De Marchi M, Berzaghi. Italian Journal of Animal Science, 2007, 6: 421. [9] HU Yao-hua, GUO Kang-quan, Noguchi Gou, et al(胡耀华, 郭康权, 野口刚，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2009, 29(12): 3259. [10] LIU Wei, YU Xiang-lin, SUN Dong-dong, et al(刘 炜, 俞湘麟, 孙东东, 等). Swine Production(养猪), 2005, 4(3): 47. [11] Prevolnik M, Candek-Potokar M, Skorjanc D, et al. Journal of Near Infrared Spectroscopy, 2005，13: 77. [12] Prieto N, Andres S, Giraldez F J, et al. Meat Science, 2006,(74): 487. [13] ZHU Da-qi, SHI Hui(朱大奇，史 慧). Principle and Applications of Artificial Neural Networks(人工神经网络原理及应用). Beijing: Science Press(北京：科学出版社)，2006. 1. [14] Cozzolino D, Murray I. Journal of Near Infrared Spectroscopy, 2002, 10: 37.
