光谱学与光谱分析 |
|
|
|
|
|
NIRS Method for Determination of Meat and Bone Meal Content in Ruminant Concentrates |
YANG Zeng-ling1,2,HAN Lu-jia1,2*,LI Qiong-fei2,3,LIU Xian1,2 |
1. College of Engineering, China Agricultural University, Beijing 100083, China 2. Key Laboratory of Modern Precision Agriculture System Integration, Ministry of Education, Beijing 100083, China 3. Shanghai Finance University, Shanghai 201209, China |
|
|
Abstract Feed contaminated with MBM is commonly accepted as the main transmission carrier of bovine spongiform encephalopathy (BSE). To prevent BSE many countries have banned MBM as a feed ingredient. In the People’s Republic of China, the ban was first applied to ruminant feed. In order to investigate the feasibility of near infrared diffuse reflectance spectroscopy method for rapidly quantitative determination of meat and bone meal content in ruminant concentrates, 225 representatively commercial ruminant concentrates samples and 75 meat and bone meal (including cattle, sheep, pig and poultry meat and bone meal) samples were collected in the People’s Republic of China. Two hundred twenty five ruminant concentrates samples of adulterated meat and bone meal (0.5%-35%) were prepared including 135 calibration samples and 90 independent validation samples. For the calibration set samples, 3 samples were prepared at each concentration. For validation set samples, 2 samples were prepared at each concentration. Any one commercial ruminant concentrates was used once only. The spectra were scanned by raster near infrared diffuse reflectance spectroscopy instrument, and the effect of spectrum pretreatment methods (mathematic pretreatments and scatter correction) and spectrum region (visible and NIR) on the calibration results was considered. The calibration equation was established by modified partial least squares method. The result showed that the calibration gave r2 of 0.979, a standard error of calibration (SEC) of 1.522% and a standard error of cross validation (SECV) of 1.582%. The 90 independent validation samples were used to validate the quantitative equation. The r2, a standard error of prediction (SEP) and ratio of performance to standard deviation (RPD) were 0.972, 1.764% and 5.99 respectively. The results of this study indicated that near infrared diffuse reflectance spectroscopy method could provide rapidly quantitative prediction for meat and bone meal percent in ruminant concentrates. This method was significant in practice for enriching the rapidly quantitative methods of determining animal feed materials.
|
Received: 2007-01-19
Accepted: 2007-04-22
|
|
Corresponding Authors:
HAN Lu-jia
E-mail: hanlj@cau.edu.cn
|
|
[1] European Communities, Bovine Spongiform Encephalopathy(BSE), 3<sup>rd</sup> ed. Vademecum, Brussels 16 October 1998. [2] Gizzi G, van Raamsdonck LWD, Baeten V, et al. Sci. Tech. Rev., 2003, 22(1): 311. [3] Ansfield M, Reaney S D, Jackman R. Food Agri Immun., 2000, 12: 285. [4] Gibson U E M, Heid C A, Williams P M. Genome Res., 1996, 6: 995. [5] Hofmann K, Muller E, Fischer K. Fleischwirtschaft, 1999, 79(10);92. [6] LI Jun-xia, MIN Shun-geng, ZHANG Hong-liang, et al(李君霞,闵顺耕,张洪亮, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2006,26(5):833. [7] LIU Yan-de, YING Yi-bin(刘燕德,应义斌). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2006,26(8):1454. [8] WANG Jia-jun, WANG Fan, MA Ling(王家俊,汪 帆,马 玲). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2006,26(10):1858. [9] Murray I, Aucott L S, Pike I H. Journal of Near Infrared Spectroscopy, 2001, 9: 297. [10] Piraux F, Dardenne P. In: Davies A M C., Giangiacomo R. (Eds.), Proceedings of the Ninth International Conference on Near Infrared Spectroscopy. West Sussex: NIR Publications, Chichester, 2000. 535. [11] Fernandez M, Martinez A, Modrono S, et al. In: Davies A M C., Cho, R.K. (Eds.), Proceedings of the 10th International Conference on Near Infrared Spectroscopy. Chichester: NIR Publications, 2002. 307. [12] Garrido A, Marin M D, Guerrero J E, et al. Proceedings of the 10th International Conference on Near Infrared Spectroscopy. West Sussex: NIR Publications, Chichester, 2002. 145. [13] Dolores C, Ana Garrido-Varo, Guerrero J E, et al. Animal Feed Science and Technology, 2004, 116: 333. [14] 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 of Argricultural Engineering(CSAE)(农业工程学报),2005,21(4):155. [15] Garrido-Varo A, Pérez-Marín D, Guerrero J E, et al. Biotechnol. Agron. Soc. Environ., 2005, 9(1): 3. [16] CHU Xiao-li, YUAN Hong-fu, WANG Yan-bin, et al(褚小立,袁洪福,王艳斌, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2004,24(6):666. |
[1] |
LIU Jia1, 2, GUO Fei-fei2, YU Lei2, CUI Fei-peng2, ZHAO Ying2, HAN Bing2, SHEN Xue-jing1, 2, WANG Hai-zhou1, 2*. Quantitative Characterization of Components in Neodymium Iron Boron Permanent Magnets by Laser Induced Breakdown Spectroscopy (LIBS)[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 141-147. |
[2] |
LIU Hao-dong1, 2, JIANG Xi-quan1, 2, NIU Hao1, 2, LIU Yu-bo1, LI Hui2, LIU Yuan2, Wei Zhang2, LI Lu-yan1, CHEN Ting1,ZHAO Yan-jie1*,NI Jia-sheng2*. Quantitative Analysis of Ethanol Based on Laser Raman Spectroscopy Normalization Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3820-3825. |
[3] |
LIN Hong-jian1, ZHAI Juan1*, LAI Wan-chang1, ZENG Chen-hao1, 2, ZHAO Zi-qi1, SHI Jie1, ZHOU Jin-ge1. Determination of Mn, Co, Ni in Ternary Cathode Materials With
Homologous Correction EDXRF Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3436-3444. |
[4] |
HUANG Li, MA Rui-jun*, CHEN Yu*, CAI Xiang, YAN Zhen-feng, TANG Hao, LI Yan-fen. Experimental Study on Rapid Detection of Various Organophosphorus Pesticides in Water by UV-Vis Spectroscopy and Parallel Factor Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3452-3460. |
[5] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[6] |
LI Zhong-bing1, 2, JIANG Chuan-dong2, LIANG Hai-bo3, DUAN Hong-ming2, PANG Wei2. Rough and Fine Selection Strategy Binary Gray Wolf Optimization
Algorithm for Infrared Spectral Feature Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3067-3074. |
[7] |
LIU Shu1, JIN Yue1, 2, SU Piao1, 2, MIN Hong1, AN Ya-rui2, WU Xiao-hong1*. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142. |
[8] |
WANG Lin, WANG Xiang*, ZHOU Chao, WANG Xin-xin, MENG Qing-hui, CHEN Yan-long. Remote Sensing Quantitative Retrieval of Chlorophyll a and Trophic Level Index in Main Seagoing Rivers of Lianyungang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3314-3320. |
[9] |
LIU Wen-bo, LIU Jin, HAN Tong-shuai*, GE Qing, LIU Rong. Simulation of the Effect of Dermal Thickness on Non-Invasive Blood Glucose Measurement by Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2699-2704. |
[10] |
KANG Ying1, ZHUO Kun1, LIAO Yu-kun1, MU Bing1, QIN Ping2, LI Qian1, LUAN Xiao-ning1*. Quantitative Determination of Alcohol Concentration in Liquor Based on Polarized Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2768-2774. |
[11] |
KONG De-ming1, LIU Ya-ru1, DU Ya-xin2, CUI Yao-yao2. Oil Film Thickness Detection Based on IRF-IVSO Wavelength Optimization Combined With LIF Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2811-2817. |
[12] |
ZHAO Yu-wen1, ZHANG Ze-shuai1, ZHU Xiao-ying1, WANG Hai-xia1, 2*, LI Zheng1, 2, LU Hong-wei3, XI Meng3. Application Strategies of Surface-Enhanced Raman Spectroscopy in Simultaneous Detection of Multiple Pathogens[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2012-2018. |
[13] |
CHENG Xiao-xiang1, WU Na2, LIU Wei2*, WANG Ke-qing2, LI Chen-yuan1, CHEN Kun-long1, LI Yan-xiang1*. Research on Quantitative Model of Corrosion Products of Iron Artefacts Based on Raman Spectroscopic Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2166-2173. |
[14] |
WU Shu-jia1, 2, YAO Ming-yin2, 3, ZENG Jian-hui2, HE Liang2, FU Gang-rong2, ZENG Yu-qi2, XUE Long2, 3, LIU Mu-hua2, 3, LI Jing2, 3*. Laser-Induced Breakdown Spectroscopy Detection of Cu Element in Pig Fodder by Combining Cavity-Confinement[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1770-1775. |
[15] |
WANG Ke-qing1, 2*, WU Na1, 2, CHENG Xiao-xiang3, ZHANG Ran1, 2, LIU Wei1, 2*. Use of FTIR for the Quantitative Study of Corrosion Products of Iron
Cultural Relics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1846-1853. |
|
|
|
|