| 光谱学与光谱分析 |
|
|
|
|
|
| The Determination of Beef Tenderness Using Near-Infrared Spectroscopy |
| ZHAO Jie-wen1,ZHAI Jian-mei1*,LIU Mu-hua2,CAI Jian-rong1 |
1. School of Biological and Environmental Engineering, Jiangsu University, Zhenjiang 212013, China
2. Industry College of Jiangxi Agricultural University, Nanchang 330045, China |
|
|
|
|
Abstract The prediction of beef tenderness was studied using near-infrared spectroscopy. The absorption spectra of beef samples were collected between 4 000 and 10 000 cm-1,the maximum shear force of these samples was obtained using the Warner-Bratzler attachment, and subjective judgment for the tenderness grade of beef was studied. Beef samples with the maximum shear force less than 6 kg were regarded as tender, and their tenderness grade was defined as the value of 1. Those with the maximum shear force greater than 9 kg were regarded as tough, and their tenderness grade was defined as the value of 3. And those with the maximum shear force between 6 and 9 kg were regarded as medium, and their tenderness grade was defined as the value of 2. The study shows that the absorption value of tougher beef is generally higher than that of tender beef. Multiple linear regression was used to build the model between the absorption value and tenderness grade. The results give the correlation coefficient r is 0.806. The accuracy of the model for predicting tenderness grade of beef was 84.21% for a validation set including 19 samples. This result indicates that NIR spectroscopy is capable of predicting tenderness grade of beef.
|
|
Received: 2004-12-26
Accepted: 2005-05-08
|
|
|
|
Corresponding Authors:
ZHAI Jian-mei
|
|
| Cite this article: |
|
ZHAO Jie-wen,ZHAI Jian-mei,LIU Mu-hua, et al. The Determination of Beef Tenderness Using Near-Infrared Spectroscopy [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2006, 26(04): 640-642.
|
|
|
|
| URL: |
|
https://www.gpxygpfx.com/EN/Y2006/V26/I04/640 |
[1] ZHOU Guang-hong(周光宏). Meat Science(肉品学). Beijing: China Agriculture Press(北京: 农业出版社)，1999.
[2] Naes Tormod, Hildrum Kjell Ivar. Applied Spectroscopy, 1997, 51(3): 350.
[3] Park B, Chen Y R, Hruschka W R, et al. American Society of Animal Science, 1998, 76: 2115.
[4] Park Bosoon, Chen Yud-Ren Hruschka William R, et al. Assessment of Beef Tenderness Using Near-Infrared Spectroscopy. Ｐroceedings of the 1997 ASAAE Annual International Meeting. Part 3. Aug 1-14，1997. Minneapolis, USA, 1997.
[5] Park B, Chen Y R, Hruschka W R, et al. Transactions of the American Society of Agricultural Engineers, 2001, 44(3): 609.
[6] Byrne C E, Downey G, Troy D J. Meat Science, 1998, 49(4): 399.
[7] Rùdbotten R, Nilsen B N, Hildrum K I. Food Chemistry, 2000, 69: 427.
[8] Liu Yongliang, Lyon Bsenda G, Windham William R, et al. Meat Science, 2003, 65: 1107.
[9] LU Wan-zhen, YUAN Hong-fu, XU Guang-tong, et al(陆婉珍，袁洪福, 徐广通, 等). Modern NIR Spectroscopic Analysis Techniques(现代近红外光谱分析技术). Beijing: China Petrochemistry Press(北京: 中国石化出版社), 2000.
[10] BAI Qi-lin, CHEN Shao-jiang, DONG Xiao-ling, et al(白琪林, 陈绍江, 董晓玲, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2004, 24(11): 1345.
|
| [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] |
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. |
| [3] |
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. |
| [4] |
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. |
| [5] |
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. |
| [6] |
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. |
| [7] |
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. |
| [8] |
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. |
| [9] |
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. |
| [10] |
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. |
| [11] |
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. |
| [12] |
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. |
| [13] |
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. |
| [14] |
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. |
| [15] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
|
|
|
|
