Evaluation of fresh meat quality by Hyperspectral Imaging (HSI), Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI): A review

被引:69
作者
Antequera, Teresa [1 ]
Caballero, Daniel [1 ]
Grassi, Silvia [2 ]
Uttaro, Bethany [3 ]
Perez-Palacios, Trinidad [1 ]
机构
[1] Univ Extremadura, Food Technol Dept, Meat & Meat Prod Res Inst, Av Ciencias S-N, ES-10003 Caceres, Spain
[2] Univ Milan, Dept Food Environm & Nutrit Sci, Via Mangiagalli 25, IT-20133 Milan, Italy
[3] Agr & Agri Food Canada, Lacombe Res & Dev Ctr, 6000 C & E Trail, Lacombe, AB T4L 1W1, Canada
关键词
Hyperspectral imaging; Nuclear magnetic resonance; Magnetic resonance imaging; Fresh meat; Non-destructive analysis;
D O I
10.1016/j.meatsci.2020.108340
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
The development of non-destructive methodologies based on Hyperspectral Imaging (HSI), Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) techniques to determine quality characteristics of fresh meat has been reviewed in this study. It has been focused primarily on research published in the last decade, and has placed particular attention on the instrumentation, data acquisition and main applications of each technique, finding a wide variety of possibilities for systems and methodologies as well as evidence of accurate and promising results. Most samples analysed were pork and beef, followed by lamb and chicken, with few studies on fresh meat from rabbit and duck. The overall evaluation is that work has been performed primarily in an experimental way but generally still lacks real application in the meat industry. In that respect, these non-destructive techniques should be improved, especially regarding speed, price and influence of external factors.
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页数:12
相关论文
共 83 条
[1]  
[Anonymous], 2013, Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET)
[2]  
[Anonymous], 2001, IASTED INT C SIGN PR
[3]   Magnetic Resonance Imaging, texture analysis and regression techniques to non-destructively predict the quality characteristics of meat pieces [J].
Avila, M. M. ;
Duran, M. L. ;
Caballero, D. ;
Antequera, T. ;
Palacios-Perez, T. ;
Cernadas, E. ;
Fernandez-Delgado, M. .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2019, 82 :110-125
[4]   Applying 3D texture algorithms on MRI to evaluate quality traits of loin [J].
Avila, Mar ;
Caballero, Daniel ;
Antequera, Teresa ;
Luisa Duran, Maria ;
Caro, Andres ;
Perez-Palacios, Trinidad .
JOURNAL OF FOOD ENGINEERING, 2018, 222 :258-266
[5]   MRI-aided texture analyses of compressed meat products [J].
Bajd, Franci ;
Skrlep, Martin ;
Candek-Potokar, Marjeta ;
Sersa, Igor .
JOURNAL OF FOOD ENGINEERING, 2017, 207 :108-118
[6]   Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging [J].
Barbin, Douglas F. ;
ElMasry, Gamal ;
Sun, Da-Wen ;
Allen, Paul .
FOOD CHEMISTRY, 2013, 138 (2-3) :1162-1171
[7]   Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging [J].
Barbin, Douglas F. ;
ElMasry, Gamal ;
Sun, Da-Wen ;
Allen, Paul .
ANALYTICA CHIMICA ACTA, 2012, 719 :30-42
[8]   Evaluation of carcass composition of intact boars using linear measurements from performance testing, dissection, dual energy X-ray absorptiometry (DXA) and magnetic resonance imaging (MRI) [J].
Bernau, M. ;
Kremer, P. V. ;
Lauterbach, E. ;
Tholen, E. ;
Petersen, B. ;
Pappenberger, E. ;
Scholz, A. M. .
MEAT SCIENCE, 2015, 104 :58-66
[9]   Elucidation of the relationship between cooking temperature, water distribution and sensory attributes of pork - a combined NMR and sensory study [J].
Bertram, HC ;
Aaslyng, MD ;
Andersen, HJ .
MEAT SCIENCE, 2005, 70 (01) :75-81
[10]  
Bro R, 1996, J CHEMOMETR, V10, P47, DOI 10.1002/(SICI)1099-128X(199601)10:1<47::AID-CEM400>3.3.CO