Meat Quality Evaluation by Hyperspectral Imaging Technique: An Overview

被引:220
作者
Elmasry, Gamal [1 ]
Barbin, Douglas F. [1 ]
Sun, Da-Wen [1 ]
Allen, Paul [2 ]
机构
[1] Natl Univ Ireland Univ Coll Dublin, Sch Biosyst Engn, Agr & Food Sci Ctr, Dublin 4, Ireland
[2] TEAGASC, Ashtown Food Res Ctr, Dublin, Ireland
关键词
Beef; poultry; fish; pork; computer vision; spectroscopy; hyperspectral imaging; image processing; near infrared; NIR; spectrometry; INFRARED REFLECTANCE SPECTROSCOPY; VACUUM COOLING PROCESS; INTRAMUSCULAR FAT-CONTENT; RAY COMPUTED-TOMOGRAPHY; WATER-HOLDING CAPACITY; PORK QUALITY; FOOD QUALITY; NIR SPECTROSCOPY; DRIP-LOSS; SENSORY CHARACTERISTICS;
D O I
10.1080/10408398.2010.507908
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
During the last two decades, a number of methods have been developed to objectively measure meat quality attributes. Hyperspectral imaging technique as one of these methods has been regarded as a smart and promising analytical tool for analyses conducted in research and industries. Recently there has been a renewed interest in using hyperspectral imaging in quality evaluation of different food products. The main inducement for developing the hyperspectral imaging system is to integrate both spectroscopy and imaging techniques in one system to make direct identification of different components and their spatial distribution in the tested product. By combining spatial and spectral details together, hyperspectral imaging has proved to be a promising technology for objective meat quality evaluation. The literature presented in this paper clearly reveals that hyperspectral imaging approaches have a huge potential for gaining rapid information about the chemical structure and related physical properties of all types of meat. In addition to its ability for effectively quantifying and characterizing quality attributes of some important visual features of meat such as color, quality grade, marbling, maturity, and texture, it is able to measure multiple chemical constituents simultaneously without monotonous sample preparation. Although this technology has not yet been sufficiently exploited in meat process and quality assessment, its potential is promising. Developing a quality evaluation system based on hyperspectral imaging technology to assess the meat quality parameters and to ensure its authentication would bring economical benefits to the meat industry by increasing consumer confidence in the quality of the meat products. This paper provides a detailed overview of the recently developed approaches and latest research efforts exerted in hyperspectral imaging technology developed for evaluating the quality of different meat products and the possibility of its widespread deployment.
引用
收藏
页码:689 / 711
页数:23
相关论文
共 145 条
[1]  
Abou el Karam S, 1997, 1997 IEEE ULTRASONICS SYMPOSIUM PROCEEDINGS, VOLS 1 & 2, P1197, DOI 10.1109/ULTSYM.1997.661793
[2]  
ABOUELKARAM S, 2006, P 52 INT C MEAT SCI, P669
[3]   DETERMINATION OF TOTAL PIGMENTS IN RED MEATS [J].
AGULLO, E ;
CENTURION, ME ;
RAMOS, V ;
BIANCHI, MA .
JOURNAL OF FOOD SCIENCE, 1990, 55 (01) :250-251
[4]  
AMSA, 2001, AMSA MEAT EV HDB
[5]   The use of visible and near infrared reflectance spectroscopy to predict beef M-longissimus thoracic et lumborum quality attributes [J].
Andres, S. ;
Silva, A. ;
Soares-Pereira, A. L. ;
Martins, C. ;
Bruno-Soares, A. M. ;
Murray, I. .
MEAT SCIENCE, 2008, 78 (03) :217-224
[6]  
Ariana DP, 2008, T ASABE, V51, P705, DOI 10.13031/2013.24367
[7]   Near-infrared hyperspectral. reflectance imaging for detection of bruises on pickling cucumbers [J].
Ariana, Diwan P. ;
Lu, Renfu ;
Guyer, Daniel E. .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2006, 53 (01) :60-70
[8]   ASAS Centennial Paper: A century of pioneers and progress in meat science in the United States leads to new frontiers [J].
Beermann, D. H. .
JOURNAL OF ANIMAL SCIENCE, 2009, 87 (03) :1192-1198
[9]   Hyperspectral imaging applied to complex particulate solids systems [J].
Bonifazi, Giuseppe ;
Serranti, Silvia .
OPTICAL SENSORS 2008, 2008, 7003
[10]   Improving quality inspection of food products by computer vision - a review [J].
Brosnan, T ;
Sun, DW .
JOURNAL OF FOOD ENGINEERING, 2004, 61 (01) :3-16