光谱学与光谱分析 |
|
|
|
|
|
Rapid Evaluation of Beef Quality by NIRS Technology |
YANG Jian-song,MENG Qing-xiang,REN Li-ping*,ZHOU Zhen-ming,XIE Xiang-xue |
State Key Laboratory of Animal Nutrition, College of Animal Science & Technology, China Agricultural University, Beijing 100094, China |
|
|
Abstract The aim of the present study was to develop a near-infrared reflectance (NIR) spectroscopy rapid method for evaluation of beef quality. Partial least squares (PLS) prediction model for the physic-chemical characteristics such as moisture, fat, protein, pH, color and WBSF in beef was established with good veracity. One hundred fourteen samples from five different parts of beef carcass (tenderloin, ribeye, topside, shin, striploin) were collected from meat packer after 48 h aging. Spectra were obtained by scanning sample from 950 to 1 650 nm and pretreated the model by MSC, SNV and first derivative. Predictive correlation coefficients of physic-chemical parameters in beef were 0.947 2(moisture), 0.924 5(fat), 0.934 6(protein), 0.620 2(pH), 0.820 3(L), 0.864 6(a*), 0.753 0(b*) and 0.475 9(WBSF) respectively. Root mean square errors of calibration (RMSEC) were 0.313 3(moisture), 0.221 0(fat), 1.243 2(protein), 0.744 6(pH), 1.778 3(L*), 1.394 2(a*), 1.763 9(b*) and 1.0743(WBSF). They were externally validated with additional 30 beef samples. Statistics showed that there was no significant difference between predicted value and those obtained with conventional laboratory methods. The results showed that NIRS is a rapid, effective technique for evaluating beef quality. The predictions for chemical characteristics gave higher accuracy than prediction for physical characteristics.
|
Received: 2009-03-03
Accepted: 2009-06-06
|
|
Corresponding Authors:
REN Li-ping
E-mail: renlp@cau.edu.cn
|
|
[1] LIU Xu,CHEN Hua-cai,LIU Tai-ang,et al(刘 旭,陈华才,刘太昂,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2007,27(12):2465. [2] Ripoche A, Guillard A S. Meta Science, 2001, 58: 299. [3] Isaksson T, Nilsen B N, Tgersen G, et al. Meat Science, 1996, 43: 245. [4] Prieto N, Andre′s S, Gira′ldez F J, et al. Meat Science, 2008, 79: 692. [5] Qninn P R,Nelsscn J L. Journal of Animal Science, 2000,78(9):2359. [6] Maw S J,Fowler V R,Hamilton M, et al. Livestock Pruduetian Science,2001,68(2-3):119. [7] ZAN Lin-sen, ZHU Gui-ming(昝林森,朱贵明). National Cattle Rising Scientific Symposium (A)(全国养牛科学研讨会论文集),2005. 144. [8] Cozzolino D, Murray I. Lebensm. Wiss. und Technol., 2004, 37: 447. [9] Leroy B, Lambotte S, Dotreppe O, et al. Meat Science, 2003, 66:45.
|
[1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[2] |
LI Yu1, ZHANG Ke-can1, PENG Li-juan2*, ZHU Zheng-liang1, HE Liang1*. Simultaneous Detection of Glucose and Xylose in Tobacco by Using Partial Least Squares Assisted UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 103-110. |
[3] |
LIU Jia, ZHENG Ya-long, WANG Cheng-bo, YIN Zuo-wei*, PAN Shao-kui. Spectra Characterization of Diaspore-Sapphire From Hotan, Xinjiang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 176-180. |
[4] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[5] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[6] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[7] |
HE Qing-yuan1, 2, REN Yi1, 2, LIU Jing-hua1, 2, LIU Li1, 2, YANG Hao1, 2, LI Zheng-peng1, 2, ZHAN Qiu-wen1, 2*. Study on Rapid Determination of Qualities of Alfalfa Hay Based on NIRS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3753-3757. |
[8] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[9] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[10] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[11] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[12] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[13] |
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. |
[14] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[15] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
|
|
|
|