| 光谱学与光谱分析 |
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| The Quantified Analysis of Fresh Mutton Tenderness Using PLS Methods and Fourier Transform Near-Infrared Spectroscopy |
| ZHANG De-quan1,CHEN Xiao-na2,SUN Su-qin3,LI Chun-hong1,ZHANG Bo-lin2,LI Yong1,LI Shu-rong1, LI Qing-peng1,ZHOU Hong-jie1 |
1. Institute of Agro-Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100094, China
2. College of Bioscience and Biotechnology, Beijing Forestry University, Beijing 100083, China
3. Department of Chemistry, Tsinghua University, Beijing 100084, China |
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Abstract Ninety eight representative fresh mutton samples from Neimeng, Ningxia, Gansu, Xinjiang province were selected for this study, the nondestructive measurement of the fresh mutton tenderness by Fourier transform near infrared (FT-NIR) spectroscopy was discussed. Partial least squares(PLS) algorithm was used to build the model between the shear force value of the fresh mutton tenderness measured by the texture machine and the FT-NIR spectra. The influence of different processing method of spectra, factors and wave regions on the determination coefficients (r2), root mean square error of cross validation (RMSECV) and root mean square error of prediction (RMSEP) was studied. The result showed that the shear force value of ninety eight representative fresh mutton samples was 1.673-6.631 kg, and the shear force value above 75% samples was 2-5 kg, almost covering the fresh mutton tenderness of our country’s sheep, the r2 of the calibration could reach 86.2% and the RMSECV was up to 0.445 in the wave number range 11 995-5 446 cm-1 and 4 601-4 246 cm-1 with vector normalization when the PLS factors was ten. The correlation coefficient(R), RMSEP and average bias between value measured by the texture machine and predicted value of model based on validation samples were 0.87, 0.524 and 0.385 respectively. The result indicates that FT-NIR spectroscopy is capable of predicting tenderness value of fresh mutton.
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Received: 2007-09-21
Accepted: 2007-12-26
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Corresponding Authors:
ZHANG De-quan
E-mail: dqzhang0118@126.com
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[1] Shackelford S D, Wheeler T L, Meade M K. J. Animal Science, 2001, 79: 2605.
[2] ZHANG Ying-hua(张英华). Food Research and Development(食品研究与开发), 2005, 26(1): 39.
[3] HUANG Hong-bing, XU Xing-lian(黄鸿兵, 徐幸莲). Meat Research(肉类研究), 2003, (3): 12.
[4] WANG Yu-ning, LUO Xin(王玉宁, 罗 欣). Food and Fermentation Industries(食品与发酵工业), 2006, 32(5): 147.
[5] ZHAO Jie-wen, ZHAI Jian-mei, LIU Mu-hua, et al(赵杰文, 翟剑妹, 刘木华, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(4)：640.
[6] Park B, Chen Y R, Hruschka W R. American Society of Animal Science, 1998, 76: 2115.
[7] Isakssoq T, Nilsen B N, Togersn G. Meat Science, 1996, 43: 245.
[8] Park Bosoon, Chen Yud-Ren, Hruschka William R. Proceedings of the 1997 ASAAE Annual International Meeting. Part 3. Minneapolis, USA, 1997.
[9] Geesinka G H, Schreutelkamp F H, Frankhuizen R. Meat Science, 2003, 65: 661.
[10] ZHU Xun-tao(朱迅涛). Meat Research(肉类研究), 2002, (2): 42.
[11] LIU Wei, YU Xiang-lin, SU Dong-dong, et al(刘 炜, 俞湘麟, 孙东东, 等). Swine Production(养猪), 2005, (3): 47.
[12] ZHAO Li-li, ZHANG Lu-da, SONG Zhong-xiang, et al(赵丽丽, 张录达, 宋忠祥, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(1): 46. |
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