, Vol.33, No.24

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1 , Vol.33, No , * ( ) nm (WBSF) (PLS-DA) 100% % 90% Discrimination of Meat from Young Bulls and Culled Cows by Near Infrared Reflectance (NIR) Spectroscopy LIU Xiao-ye 1 TANG Xiao-yan 1, * SUN Bao-zhong 2 MAO Xue-fei 1 WANG Min 1 (1. Key Laboratory of Agrifood Safety and Quality, Ministry of Agriculture, Institute of Quality Standards and Testing Technology for Agri-Products, Chinese Academy of Agricultural Sciences, Beijing 81, China 2. Institute of Animal Science for Agri-Products, Chinese Academy of Agricultural Science, Beijing , China) Abstract Meat was sampled from different parts of carcasses of young bulls and culled cows of the same breed from Inner Mongolia and Xinjiang autonomous regions, China. A total 224 and 130 beef samples were obtained from cattle carcasses from Inner Mongolia and Xinjiang autonomous regions, respectively. Fresh and frozen-thawed samples were minced and tested using a portable NIR spectrometer in the range of 1799 nm. Meanwhile, the contents of protein, fat and moisture and Warner-Bratzler shear force (WBSF) were measured. A total of 154 and 91 samples from Inner Mongolia and Xinjiang autonomous regions were selected as modeling sets. Two models for discriminating meat from young bulls and culled cows from both regions were established using partial least squares discriminant analysis (PLS-DA). The established models showed 100% accuracy for discriminating calibration set and validation set and 96% and 90% accuracy for the remaining 70 and 39 beef samples from Inner Mongolia and Xinjiang, respectively. Key words beef classification near infrared reflectance spectroscopy discrimination TS251.7 A (2012) [1-3] [4-13] DNA [8] (2011-G5)()( ) (2012DFA31140) (1986 ) xiaoye.liu86@gmail.com *(1976 ) txycaas@126.com

2 2012, Vol.33, No SupNIR- Kjeltec TM 2300Soxtec TM 2050 FossGM2000 Retsch Model 235 G-R Manufacturing h14( ) h13 ( ) nm 10nm GB/T GB/T GB/T NY/T Unscrambler(version 9.8 CAMO)(partial least-squares discriminant analysis PLS-DA) SAS/PC(8.0) PLS-DA(PLS) PLS1 Y X 1 2(12) (predictive residual error sum of squares PRESS) PLSR (1 2) Table 1 Fat, protein and moisture contents and WBSF of meat from young bulls and culled cows /% /% /% /kg a a a a a b b b (P 01) (P 5) 1 (P 5) (P 01) (P 01) (P 01) [14] 2.2 Fig Average NIR spectra [Log(1/R)] of beef samples obtained from young bulls and culled cows of the same breed from Inner Mongolia Mongolia

3 , Vol.33, No nm 12 Fig Average NIR spectra [Log(1/R)] of beef samples obtained from young bulls and culled cows of the same breed from Xinjiang (Savitzky-Golay 11 ) (Savitzky-Golay 9 ) SNVD ( 3 4) nm Fig.3 3 1D Average first-order derivative spectra of beef samples obtained from young bulls and culled cows of the same breed from Inner Mongolia Fig.4 4 1D Average first-order derivative spectra of beef samples obtained from young bulls and culled cows of the same breed from Xinjiang 2.4 (principal component analysis PCA) PRESS ( 5 6)5 6 PC1 PC1 PC3PC PC PC3 04 PC X Y Z 123 Fig.5 3D scatter plot of scores corresponding to principal component 1, principal component 2 and principal component 3 for beef samples obtained from young bulls and culled cows of the same breed from Inner Mongolia X Y PC PC PC Z Fig.6 3D scatter plot of scores corresponding to principal component 1, principal component 2 and principal component 3 for beef samples obtained from young bulls and culled cows of the same breed from Xinjiang 2.5 (PLS-DA) [15] (Savitzky-Golay 5911 ) (multiplication scatter correction MSC) (SNV De-trending)PLS-DA (Savitzky-Golay 11) ( 2)100% 96%(64/70)( 7) R 2 RMSECV RMSEP

4 2012, Vol.33, No (Savitzky-Golay 9) SNVD) ( 2) 100%90%(35/39)( 8) R 2 RMSECV RMSEP Tab 2 2 Effect of different pretreatments on modeling /% S-G(5) 1D S-G(9) 1D S-G(11) 1D S-G(5) 2D S-G(9) 2D S-G(11) 2D S-G(11) 1D MSC S-G(11) 1D SNVD S-G(5) 1D S-G(9) 1D S-G(11) 1D S-G(5) 2D S-G(9) 2D S-G(11) 2D S-G(9) 1D MSC S-G(9) 1D SNVD D. 2D. MSC. SNVD. SNV De-trending S-G(5). 5 7 Fig Distribution of true and predicted values for the prediction clusters from beef samples obtained from young bulls and culled cows of 8 Fig the same breed from Inner Mongolia 1 2 Distribution of true and predicted values for the prediction clusters from beef samples obtained from young bulls and culled cows of the same breed from Xinjiang 3 (P 01) Aberle [16] (P 01) Warris [17] ( nm 1395nm) [18] Prieto [8] McDevitt [9] (PLS-DA) R RMSECV % 90% RMSEP Cozzolino [4] 85%Mamani-Linares [5] 95% Alomar [6] 79% 98% Andres [7] Sun Shumin [10] 100% Chen Quansheng [11] 96% [1] PREVOLNIK M, CANDEK-POTOKAR M, SKORJANC D, et al. Predicting intramuscular fat content in pork and beef by near infrared spectroscopy[j]. Journal of Near Infrared Spectroscopy, 2005, 13(2):

5 , Vol.33, No.24 [2] PRIETO N, ANDRES S, GIRALDEZ F J, et al. Potential use of near infrared reflectance spectroscopy (NIRS) for the estimation of chemical composition of oxen meat samples[j]. Meat Science, 2006, 74(3): [3] ORTIZ-SOMOVILLA V, ESPANA-ESPANA F, GAITAN-JURADO A J, et al. Proximate analysis of homogenized and minced mass of pork sausages by NIRS[J]. Food Chemistry, 2007, 101(3): [4] COZZOLINO D, MURRAY I. Identification of animal meat muscles by visible and near infrared reflectance spectroscopy[j]. Lebensm.- Wiss. u.-technol, 2004, 37(4): [5] MAMANI-LINARES L W, GALLO C, ALOMAR D. Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy[j]. Meat Science, 2010, 90(2): [6] ALOMAR D, GALLO C, CASTANEDA M, et al. Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS)[J]. Meat Science, 2003, 63(4): [7] ANDRES S, MURRAY I, NAVAJAS E A, et al. Prediction of sensory characteristics of lamb meat samples by near infrared reflectance spectroscopy[j]. Meat Science, 2007, 76(3): [8] PRIETO N, ANDRES S, GIRALDEZ F J, et al. Discrimination of adult steers (oxen) and young cattle ground meat samples by near infrared reflectance spectroscopy (NIRS)[J]. Meat Science, 2008, 79(1): [9] MCDEVITT R M, GAVIN A J, ANDRES S, et al. The ability of visible and near infrared reflectance spectroscopy (NIRS) to predict the chemical composition of ground chicken carcasses and to discriminate between carcasses from different genotypes[j]. Journal of Near Infrared Spectroscopy, 2005, 13(3): [10] SUN Shumin, GUO Boli, WEI Yimin, et al. Classification of geographical origins and prediction of δ 13 C and δ 15 N values of lamb meat by near infrared reflectance spectroscopy[j]. Food Chemistry, 2012, 135(2): [11] CHEN Quansheng, CAI Jianrong, WAN Xinmin, et al. Application of linear/non-linear classification algorithms in discrimination of pork storage time using Fourier transform near infrared (FT-NIR) spectroscopy[j]. LWT - Food Science and Technology, 2011, 44(10): [12] ORTIZ-SOMOVILLA V, ESPANA-ESPANA F, PEDRO-SANZ E J, et al. Meat mixture detection in Iberian pork sausages[j]. Meat Science, 2005, 71(3): [13] DING H B, XU R J. Near infrared spectroscopic technique for detection of beef hamburger adulteration[j]. Journal of Agricultural and Food Chemistry, 2000, 48(6): [14],. [M]. :, 1999: [15],,,. [J]., 2011, 31(4): [16] ABERLE E D, FORREST J C, GERRARD D E, et al. Principles of Meat Science[M]. 4th ed. Dubuque, IA: Kendall Hunt Pub Co, 2001: [17] WARRIS P D. Meat Science: an introductory text[m]. Walling ford, UK: CABI Publishing, [18],,,. [M]. 2. :, 2006: 30.