Browse > Article
http://dx.doi.org/10.5851/kosfa.2020.e57

Quality Assessment of Beef Using Computer Vision Technology  

Rahman, Md. Faizur (Department of Animal Science, Bangladesh Agricultural University)
Iqbal, Abdullah (Department of Food Technology and Rural Industries, Bangladesh Agricultural University)
Hashem, Md. Abul (Department of Animal Science, Bangladesh Agricultural University)
Adedeji, Akinbode A. (Department of Biosystems and Agricultural Engineering, University of Kentucky)
Publication Information
Food Science of Animal Resources / v.40, no.6, 2020 , pp. 896-907 More about this Journal
Abstract
Imaging technique or computer vision (CV) technology has received huge attention as a rapid and non-destructive technique throughout the world for measuring quality attributes of agricultural products including meat and meat products. This study was conducted to test the ability of CV technology to predict the quality attributes of beef. Images were captured from longissimus dorsi muscle in beef at 24 h post-mortem. Traits evaluated were color value (L*, a*, b*), pH, drip loss, cooking loss, dry matter, moisture, crude protein, fat, ash, thiobarbituric acid reactive substance (TBARS), peroxide value (POV), free fatty acid (FFA), total coliform count (TCC), total viable count (TVC) and total yeast-mould count (TYMC). Images were analyzed using the Matlab software (R2015a). Different reference values were determined by physicochemical, proximate, biochemical and microbiological test. All determination were done in triplicate and the mean value was reported. Data analysis was carried out using the programme Statgraphics Centurion XVI. Calibration and validation model were fitted using the software Unscrambler X version 9.7. A higher correlation found in a* (r=0.65) and moisture (r=0.56) with 'a*' value obtained from image analysis and the highest calibration and prediction accuracy was found in lightness (r2c=0.73, r2p=0.69) in beef. Results of this work show that CV technology may be a useful tool for predicting meat quality traits in the laboratory and meat processing industries.
Keywords
beef quality; computer vision technology; correlation; calibration; validation;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Valous NA, Mendoza F, Sun DW, Allen P. 2009. Colour calibration of a laboratory computer vision system for quality evaluation of pre-sliced hams. Meat Sci 81:132-141.   DOI
2 Weglarz A. 2010. Meat quality defined based on pH and colour depending on cattle category and slaughter season. Czech J Anim Sci 55:548-556.   DOI
3 Yagiz Y, Balaban MO, Kristinsson HG, Welt BA, Marshall MR. 2009. Comparison of Minolta colorimeter and machine vision system in measuring colour of irradiated Atlantic salmon. J Sci Food Agric 89:728-730.   DOI
4 Zhang B, Huang W, Gong L, Li J, Zhao C, Liu C, Huang D. 2015. Computer vision detection of defective apples using automatic lightness correction and weighted RVM classifier. J Food Eng 146:143-151.   DOI
5 Chmiel M, Slowinski M, Dasiewicz K, Florowski T. 2016. Use of computer vision system (CVS) for detection of PSE pork meat obtained from m. semimembranosus. LWT-Food Sci Technol 65:532-536.   DOI
6 Chmiel M, Slowinski M, Dasiewicz K. 2011. Lightness of the color measured by computer image analysis as a factor for assessing the quality of pork meat. Meat Sci 88:566-570.   DOI
7 ElMasry G, Sun DW, Allen P. 2011. Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging. Food Res Int 44:2624-2633.   DOI
8 CIE. 1976. Recommendations of uniform color spaces color difference equations. Psychometric color terms. Commission International d'Eclairage, Paris, France.
9 De Marchi M, Berzaghi P, Boukha A, Mirisola M, Gallo L. 2007. Use of near infrared spectroscopy for assessment of beef quality traits. Italian J Anim Sci 6:421-423.
10 Du CJ, Sun DW, Jackman P, Allen P. 2008. Development of a hybrid image processing algorithm for automatic evaluation of intramuscular fat content in beef M. longissimus dorsi. Meat Sci 80:1231-1237.   DOI
11 ElMasry G, Sun DW, Allen P. 2012. Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef. J Food Eng 110:127-140.   DOI
12 Fatih T, Murat O, Mehmet M. 2016. Computer vision system approach in colour measurement of foods: Part I. development of methodology. Food Sci Technol 36:382-388.   DOI
13 Girolami A, Napolitano F, Faraone D, Braghieri A. 2013. Measurement of meat color using a computer vision system. Meat Sci 93:111-118.   DOI
14 Gumus B, Balaban MO, Unlusayin M. 2011. Machine vision applications to aquatic foods: A review. Turk J Fish Aqua Sci 11:171-181.
15 Iqbal A, Valous NA, Mendoza F, Sun DW, Allen P. 2010. Classification of pre-sliced pork and Turkey ham qualities based on image colour and textural features and their relationships with consumer responses. Meat Sci 84:455-465.   DOI
16 Hocquette JF, Chatellier V. 2011. Prospects for the European beef sector over the next 30 years. Anim Front 1:20-28.   DOI
17 Ikhlas B, Huda N, Ismail N. 2012. Effect of cosmos caudatus, polygonum minus and bht on physical properties, oxidative process, and microbiology growth of quail meatball during refrigeration storages. J Food Process Preserv 36:55-66.   DOI
18 Iqbal A, Sun DW, Allen P. 2013. Prediction of moisture, color and pH in cooked, pre-sliced turkey hams by NIR hyperspectral imaging system. J Food Eng 117:42-51.   DOI
19 Jackman P, Sun DW, Allen P. 2011. Recent advances in the use of computer vision technology in the quality assessment of fresh meats. Trends Food Sci Technol 22:185-197.   DOI
20 Kamruzzaman M, Yoshio M, Seiichi O. 2016. Online monitoring of red meat colour using hyperspectral imaging. Meat Sci 116:110-117.   DOI
21 Lee YS, Saha A, Xiong R, Owens CM, Meullenet JF. 2008. Changes in broiler breast fillet water-holding capacity, and color attributes during long-term frozen storage. J Food Sci 73:E162-E168.   DOI
22 Luo L, Guo D, Zhou G, Chen K. 2018. An investigation on the relationship among marbling features, physiological age and Warner-Bratzler shear force of steer longissimus dorsi muscle. J Food Sci Technol 55:1569-1574.   DOI
23 Mello R, Vaz FN, Pacheco PS, Pascoal LL, Prestes RC, Costa PB, Kipper DK. 2015. Predictive efficiency of distinct color image segmentation methods for measuring intramuscular fat in beef. Cienc Rural 45:1865-1871.   DOI
24 Qiao J, Wang N, Ngad MO, Gunenc A, Monroy M, Gariepy C, Prasher SO. 2007. Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique. Meat Sci 76:1-8.   DOI
25 Monroy M, Prasher S, Ngadi MO, Wang N, Karimi Y. 2010. Pork meat quality classification using Visible/Near-Infrared spectroscopic data. Biosys Engineer 107:271-276.   DOI
26 Peng Y, Dhakal S. 2015. Optical methods and techniques for meat quality inspection. Trans ASABE 58:1371-1386.
27 Penman DW. 2001. Determination of stem and calyx location on apples using automatic visual inspection. Comput Electron Agric 33:7-18.   DOI
28 Rahman MH, Hossain MM, Rahman SME, Amin MR, Deog-Hwan Oh. 2015. Evaluation of physicochemical deterioration and lipid oxidation of beef muscle affected by freeze-thaw cycles. Korean J Food Sci Anim Resour 35:772-782.   DOI
29 Rukunudin IH, White PJ, Bern CJ, Bailey TB. 1998. A modified method for determining free fatty acids from small soybean sample sizes. J Am Oil Chem Soc 75:563-568.   DOI
30 Saba NA, Hashem MA, Azad MAK, Hossain MA and Khan M. 2018. Effect of bottle gourd leaf (Lagenaria siceraria) extract on the quality of beef meatball. Bangladesh J Anim Sci 47:105-113.   DOI
31 Sallam KI, Ishioroshi M, Samejima K. 2004. Antioxidants and antimicrobial effects of garlic in chicken sausage. LWT-Food Sci Technol 37:849-855.   DOI
32 Schmedes A, Homer G. 1989. A new thiobarbituric acid (TBA) method for determining free malondialdehyde (MDA) and hydroperoxides selectively as a measure of lipid peroxidation. J Am Oil Chem Soc 66:813-817.   DOI
33 Bertelsen G, Jakobsen M, Juncher D, Molle J, Kroger-ohlsen M, Weber C, Skibsted LH. 2000. Oxidation, shelf-life and stability of meat and meat products. Proceedings of the 46th International Congress of Meat Science and Technology, Buenos Aires, Argentina. pp 516-524.
34 Afrin S, Hossain MM, Khan M, Hossain MD. 2017. Microbial assessment of beef in selected areas of Mymensingh district in Bangladesh. Bangladesh J Anim Sci 46:244-248.   DOI
35 Alam J, Murshed HM, Rahman SME, Oh DH. 2017. Effect of chitosan on quality and shelf-life of beef at refrigerated storage. Bangladesh J Anim Sci 46:230-238.   DOI
36 Sun X, Young J, Liu JH, Bachmeier L, Somers RM, Chen K, Newman D. 2016. Prediction of pork color attributes using computer vision system. Meat Sci 113:62-64.   DOI
37 Tan J. 2004. Meat quality evaluation by computer vision. J Food Eng 61:27-35.   DOI
38 Turgut SS, Karacabey E, Kucukoner E. 2014. Potential of image analysis based systems in food quality assessments and classifications. 9th Baltic Conference of Food Science and Technology, Jelgava, Latvia. pp 8-12.
39 Animal Science and Production Association [ASPA]. 1996. Metodiche per la determinazione delle caratteristiche qualitative della carne. University of Perugia, Perugia, Italy.
40 AOAC. 2005. Official methods of analysis. 15th ed. Association of Official Analytical Chemists. Washington, DC, USA.
41 Bertram HC, Andersen HJ, Karlsson AH. 2001. Comparative study of low-field NMR relaxation measurements and two traditional methods in the determination of water holding capacity of pork. Meat Sci 57:125-132.   DOI
42 Brosnan T, Sun DW. 2004. Improving quality inspection of food products by computer vision: A review. J Food Eng 61:3-16.   DOI
43 Chmiel M, Slowinski M, Dasiewicz K, Florowski T. 2012. Application of a computer vision system to classify beef as normal or dark, firm, and dry. J Anim Sci 90:4126-4130.   DOI