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Hasan Ibrahim Kozan
Cemalettin Sariçoban
Hasan Ali Akyürek
Ahmet Ünver


non-destructive method, hyperspectral imaging, meat science, rapid method, food safety


Nowadays, the concern of meat consumption, safety and quality has been popular due to some health risks such coronary heart disease, stroke and diabetes caused by the content as saturated fat, cholesterol content and carcinogenic compounds, for consumers. The importance of the need of new non-destructive and fast meat analyze methods are increasing day by day.  For this, researchers have developed some methods to objectively measure the meat quality and meat safety as well as illness sources. Hyperspectral imaging technique is one of the most popular technology which combines imaging and spectroscopic technology. This technique is a non-destructive, real-time and easy-to-use detection tool for meat quality and safety assessment. It is possible to determine chemical structure and related physical properties of meat.

It is clear that hyperspectral imaging technology can be automated for manufacturing in meat industry and all of data’s obtained from the hyperspectral images which represents the chemical quality parameters of meats in the process can be saved to database. 


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1. Antonio, J. et al., 2016. Lamb muscle discrimination using hyperspectral imaging : Comparison of various machine learning algorithms. Journal of Food Engineering, 174, pp.92–100. Available at: http://dx.doi.org/10.1016/j.jfoodeng.2015.11.024.
2. Abouelkaram, S., Berge, P., & Culioli, J. (1997). Application of ultrasonic data to classify bovine muscles. Proceedings of IEEE Ultrasonics Symposium, 2, 1197–1200.
3. Abouelkaram, S., Chauvet, S., Strydom, P., Bertrand, D., & Damez, J. L. (2006). Muscle study with multispectral image analysis. In Declan Troy (Ed.), Proceedings of the 52nd International Congress of Meat Science and Technology (pp. 669–670). Wagening: Wageningen Academic Publishers.
4. Alomar, D., Gallo, C., Castan˜eda, M., & Fuchslocher, R. (2003). Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS). Meat Science, 63, 441–450.
5. Bannon, D. 2009. Hyperspectral imaging: Cubes and slices. Nat. Photon. 3, 627.
6. Banović, M., Aguiar Fontes, M., Barreira, M. M., & Grunert, K. G. (2012). Impact of product familiarity on beef quality perception. Agribusiness, 28, 157–172.
7. Banović, M., Grunert, K. G., Barreira, M. M., & Aguiar Fontes, M. (2009). Beef perception at the point of purchase: A study fromPortugal. Food Quality and Preference, 20, 335–342.
8. Bredahl, L., Grunert, K. G., & Fertin, C. (1998). Relating consumer perception of pork quality to physical product characteristics. Food Quality and Preference, 9, 273–281.
9. Chao, K.; Chen, Y.R.; Early, H.; Park, B. (1999) Color image classification systems for poultry viscera inspection. Appl. Eng. Agric., 15, 363–369.
10. Chau, A., Whitworth, M., Leadley, C., & Millar, S. (2009). Innovative sensors to rapidly and non-destructively determine fish freshness. Seafish Industry Authority. Report No. CMS/REP/110284/1. References 233
11. Cheng, J. et al., 2016. Developing a multispectral imaging for simultaneous prediction of freshness indicators during chemical spoilage of grass carp fish fillet. Journal of Food Engineering. Available at: http://dx.doi.org/10.1016/j.jfoodeng.2016.02.004.
12. Cimander, C., Carlsson, M., & Mandenius, C. F. (2002). Sensor fusion for on-line monitoring of yoghurt fermentation. Journal of biotechnology, 99(3), 237-248.
13. Cluff, K., Naganathan, G. K., Subbiah, J., Lu, R., Calkins, C. R., & Samal, A. (2008). Optical scattering in beef steak to predict tenderness using hyper- spectral imaging in the VIS-NIR region. Sensing and Instrumentation for Food Quality and Safety, 2, 189–196.
14. Cruz, J.; Bautista, M.; Amigo, J.M.; Blanco, M. (2009), Nir-chemical imaging study of acetylsalicylic acid in commercial tablets. Talanta, 80, 473–478.
15. Davies, S. (2014). Measurement of crispness in food products using acoustic-mechanical techniques: a literature review.
16. Delgado, C. L. (2003). Rising consumption of meat and milk in developing countries has created a new food revolution. The Journal of Nutrition, 133(11), 3907S-3910S.
17. ElMasry, G. & Sun, D., 2010. Principles of Hyperspectral Imaging Technology. Hyperspectral Imaging for Food Quality Analysis and Control, pp.3–43.
18. FAO, U. (2014). FAOstat. Retrieved Feb, 2014.
19. FAO 2015, accessed 05.02.2016, http://www.fao.org/ag/againfo/themes/en/meat/home.html
20. Faucitano, L.; Huff, P.; Teuscher, F.; Gariepy, C., 2005, Wegner, J. Application of computer image analysis to measure pork marbling characteristics. Meat Sci., 69, 537–543.
21. Font-i-Furnols, M. & Guerrero, L., 2014. Consumer preference, behavior and perception about meat and meat products: An overview. Meat Science, 98(3), pp.361–371. Available at: http://dx.doi.org/10.1016/j.meatsci.2014.06.025.
22. Food And Agricalture Organization of United States (FAO), Current Worldwide Annual Meat Consumption per capita, Livestock and Fish Primary Equivalent. (2013).
23. Garcia-Allende, P.B.; Conde, O.M.; Mirapeix, J.; Cobo, A., 2008, Lopez-Higuera, J.M. Quality control of industrial processes by combining a hyperspectral sensor and fisher's linear discriminant analysis. Sens. Actuators B Chem., 129, 977–984.
24. Gendrin, C.; Roggo, Y.; Collet, C. 2007. Content uniformity of pharmaceutical solid dosage forms by near infrared hyperspectral imaging: A feasibility study. Talanta, 73, 733–741.
25. Ghasemi-Varnamkhasti, M., Mohtasebi, S. S., Siadat, M., & Balasubramanian, S. (2009). Meat Quality Assessment by Electronic Nose (Machine Olfaction Technology). Sensors (Basel, Switzerland), 9(8), 6058–6083. http://doi.org/10.3390/s90806058
26. Goetz, A.F.H.; Vane, G.; Solomon, J.E.; Rock, B.N., 1985, Imaging spectrometry for earth remote-sensing. Science, 228, 1147–1153.
27. He, H. J., & Sun, D. W. (2015). Hyperspectral imaging technology for rapid detection of various microbial contaminants in agricultural and food products.Trends in Food Science & Technology, 46(1), 99-109.
28. He, H.-J., Wu, D., & Sun, D.-W. (2013). Non-destructive and rapid analysis of moisture distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared hyperspectral imaging. Innovative Food Science & Emerging Technologies, 18, 237e245.
29. He, H.-J.,Wu, D., & Sun, D.-W. (2014a). Potential of hyperspectral imaging combined with chemometric analysis for assessing and visualising tenderness distribution in raw farmed salmon fillets. Journal of Food Engineering, 126, 156e164.
30. He, H.-J., Wu, D., & Sun, D.-W. (2014b). Rapid and non-destructive determination of drip loss and pH distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared (ViseNIR) hyperspectral imaging. Food Chemistry, 156, 394e401.
31. Huang, H., Liu, L. & Ngadi, M., 2014. Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety. Sensors, 14(4), pp.7248–7276. Available at: http://www.mdpi.com/1424-8220/14/4/7248/.
32. Huang, H.; Liu, L.; Ngadi, M.O.; Gariepy, C. 2013a. Prediction of pork marbling scores using pattern analysis techniques. Food Control, 31, 224–229.
33. Huang, M.; Wan, X.; Zhang, M.; Zhu, Q. 2013b. Detection of insect-damaged vegetable soybeans using hyperspectral transmittance image. J. Food Eng., 116, 45–49.
34. Kamruzzaman, M., ElMasry, G., Sun, D. W., & Allen, P. (2012). Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression. Innovative Food Science & Emerging Technologies, 16, 218-226.
35. Kamruzzaman, M., Makino, Y. & Oshita, S., 2016a. LWT - Food Science and Technology Hyperspectral imaging for real-time monitoring of water holding capacity in red meat. LWT - Food Science and Technology, 66, pp.685–691. Available at: http://dx.doi.org/10.1016/j.lwt.2015.11.021.
36. Kamruzzaman, M., Makino, Y. & Oshita, S., 2016b. Parsimonious model development for real-time monitoring of moisture in red meat using hyperspectral imaging. Food Chemistry, 196, pp.1084–1091. Available at: http://dx.doi.org/10.1016/j.foodchem.2015.10.051.
37. Kim, I.; Kim, M.; Chen, Y.; Kong, S. 2004. Detection of skin tumors on chicken carcasses using hyperspectral fluorescence imaging. Trans. Am. Soc. Agric. Eng., 47, 1785–1792.
38. Khulal, U. et al., 2016. Nondestructive quantifying total volatile basic nitrogen ( TVB-N ) content in chicken using hyperspectral imaging ( HSI ) technique combined with different data dimension reduction algorithms. , 197, pp.1191–1199.
39. Lara, M.A.; Lleó, L.; Diezma-Iglesias, B.; Roger, J.M.; Ruiz-Altisent, M. 2013. Monitoring spinach shelf-life with hyperspectral image through packaging films. J. Food Eng., 119, 353–361.
40. Lim, K. S., & Barigou, M. (2004). X-ray micro-computed tomography of cellular food products. Food research international, 37(10), 1001-1012.
41. Liu, Y., Lyon, B. G., Windham, W. R., Realini, C. B., Pringle, T. D. D., & Duckett, S. (2003). Prediction of color, texture, and sensory characteristics of beef steaks by visible and near infrared reflectance spectroscopy: a feasibility study. Meat Science, 65, 1107–1115.
42. Lu, J.; Tan, J.; Shatadal, P.; Gerrard, D.E. 2000. Evaluation of pork color by using computer vision. Meat Sci., 56, 57–60.
43. Ma, J., Sun, D. & Pu, H., 2016. Spectral absorption index in hyperspectral image analysis for predicting moisture contents in pork longissimus dorsi muscles. , 197, pp.848–854.
44. Mann, N. (2000). Dietary lean red meat and human evolution. European Journal of Nutrition, 39(2), 71-79.
45. Marcone, M. F., Wang, S., Albabish, W., Nie, S., Somnarain, D., & Hill, A. (2013). Diverse food-based applications of nuclear magnetic resonance (NMR) technology. Food Research International, 51(2), 729-747.
46. McAfee, A. J., McSorley, E. M., Cuskelly, G. J., Moss, B. W., Wallace, J. M., Bonham, M. P., & Fearon, A. M. (2010). Red meat consumption: An overview of the risks and benefits. Meat science, 84(1), 1-13.Antonio, J. et al., 2016. Lamb muscle discrimination using hyperspectral imaging : Comparison of various machine learning algorithms. Journal of Food Engineering, 174, pp.92–100. Available at: http://dx.doi.org/10.1016/j.jfoodeng.2015.11.024.
47. Naganathan, G. K., Grimes, L. M., Subbiah, J., Calkins, C. R., Samal, A., & Meyer, G. E. (2008a). Visible/near-infrared hyperspectral imaging for beef tenderness prediction. Computers and Electronics in Agriculture, 64(2), 225–233.
48. Naganathan, G. K., Grimes, L. M., Subbiah, J., Calkins, C. R., Samal, A., & Meyer, G. E. (2008b). Partial least squares analysis of near-infrared hyper- spectral images for beef tenderness prediction. Sensing and Instrumentation for Food Quality and Safety, 2, 178–188.
49. Nagata, M.; Tallada, J.G.; Kobayashi, T. 2006, Bruise detection using nir hyperspectral imaging for strawberry (fragaria x ananassa duch.). Environ. Control Biol., 44, 133.
50. Neumann, C. G., Murphy, S. P., Gewa, C., Grillenberger, M., & Bwibo, N. O. (2007). Meat supplementation improves growth, cognitive, and behavioral outcomes in Kenyan children. the Journal of Nutrition, 137(4), 1119-1123.
51. Osborne, B. G. 2006. Near-Infrared Spectroscopy in Food Analysis. Encyclopedia of Analytical Chemistry (Book), John Wiley & Sons, Ltd, 2006
52. O'sullivan, M.G.; Byrne, D.V.; Martens, H.; Gidskehaug, L.H.; Andersen, H.J.; Martens, M. 2003. Evaluation of pork colour: Prediction of visual sensory quality of meat from instrumental and computer vision methods of colour analysis. Meat Sci., 65, 909–918.
53. Park, B., Lawrence, K. C., Windham, W. R., & Smith, D. P. (2006). Performance of hyperspectral imaging system for poultry surface fecal contaminant detection. Journal of Food Engineering, 75(3), 340-348.
54. Patel KK, Kar A, Jha SN, Khan MA. 2012, Machine vision system: a tool for quality inspection of food and agricultural products. Journal of food science and technology.;49(2):123-141. doi:10.1007/s13197-011-0321-4.
55. Qiao, J., Ngadi, M. O., Wang, N., Gariepy, C., & Prasher, S. O. (2007a). Pork quality and marbling level assessment using a hyperspectral imaging system. Journal of Food Engineering, 83(1), 10–16.
56. Qiao, J., Ngadi, M.,Wang, N., Gunenc, A., Monroy, M., Gariepy, C., & Prasher, S. (2007b). Pork quality classification using a hyperspectral imaging system and neural network. International Journal of Food Engineering, 3(1). Article No. 6.
57. Qin, J., Ying, Y., & Xie, L. (2013). The detection of agricultural products and food using terahertz spectroscopy: A review. Applied Spectroscopy Reviews,48(6), 439-457.
58. Segtnan, V. H., Høy, M., Lundby, F., Narum, B., & Wold, J. P. (2009). Fat distribution analysis in salmon using non-contact near infrared interactance imaging: a sampling and calibration strategy. Journal of Near Infrared Spectroscopy, 17, 247e253.
59. Shackelford, S. D., Wheeler, T. L., & Koohmaraie, M. (2004). Development of optimal protocol for visible and near-infrared reflectance spectroscopic evaluation of meat quality. Meat Science, 68, 371–381.
60. Shackelford, S. D., Wheeler, T. L., & Koohmaraie, M. (2005). On-line classification of US select beef carcasses for longissimus tenderness using visible and near-infrared reflectance spectroscopy. Meat Science, 69(3), 409–415.
61. Sun, D.-W. (2010). Hyperspectral imaging for food quality analysis and control. San Diego, California, USA: Academic Press/Elsevier.
62. Tan, J.L. 2004. Meat quality evaluation by computer vision. J. Food Eng., 61, 27–35.
63. Todd, E. C. (1996). Epidemiology of foodborne diseases: a worldwide review. World health statistics quarterly. Rapport trimestriel de statistiques sanitaires mondiales, 50(1-2), 30-50.
64. Verbeke, W., De Smet, S., Vackier, I., Van Oeckel, M. J., Warnants, N., & Van Kenhove, P. (2005). Role of intrinsic search cues in the formation of consumer preferences and choice for pork chops. Meat Science, 69, 343–354.
65. Vote, D. J., Belk, K. E., Tatum, J. D., Scanga, J. A., & Smith, G. C. (2003). Online prediction of beef tenderness using a computer vision system equipped with a BeefCam module. Journal of Animal Science, 81, 457–465.
66. Webb, K., Rutishauser, I., Katz, T., Knezevic, N., Lahti‐Koski, M., Peat, J., & Mihrshahi, S. (2005). Meat consumption among 18‐month‐old children participating in the Childhood Asthma Prevention Study. Nutrition & Dietetics, 62(1), 12-20.
67. West, G. E., Larue, B., Touil, C., & Scott, S. L. (2001). The perceived importance of veal meat attributes in consumer choice decisions. Agribusiness, 17, 365–382.
68. WHO 2015, World Health Organization, Global and regional food consumption patternsand trends. Available at: http://www.who.int/nutrition/topics/3foodconsumption/en/index4.html (2015).
69. WHO 2016http://www.who.int/mediacentre/factsheets/fs399/en/.» Food Safety. December 2015. http://www.who.int/mediacentre/factsheets/fs399/en/ (accessed: 02 01, 2016).
70. Wold, J. P., Johansen, T., Haugholt, K. H., Tschudi, J., Thielemann, J., Segtnan, V. H., Narum, B., & Wold, E. (2006). Non-contact transflectance near infrared imaging for representative on-line sampling of dried salted coalfish (bacalao). Journal of Near Infrared Spectroscopy, 14(1), 59–66.
71. Wu, Di, Sun, Da-Wen, & He, Yong (Oct 2012). Application of long-wave near infrared hyperspectral imaging for measurement of color distribution in salmon fillet. Innovative Food Science & Emerging Technologies, 16, 361e372.
72. Xing, J.; Bravo, C.; Jancsók, P.T.; Ramon, H.; de Baerdemaeker, J. 2005. Detecting bruises on ‘golden delicious’ apples using hyperspectral imaging with multiple wavebands. Biosyst. Eng., 90, 27–36.
73. Yao, H.; Hruska, Z.; Kincaid, R.; Brown, R.L.; Bhatnagar, D.; Cleveland, T.E. 2013. Detecting maize inoculated with toxigenic and atoxigenic fungal strains with fluorescence hyperspectral imagery. Biosyst. Eng., 115, 125–135.
74. Zaragozá, P. et al., 2012. Fish freshness decay measurement with a colorimetric array. Procedia Engineering, 47, pp.1362–1365. Available at: http://dx.doi.org/10.1016/j.proeng.2012.09.409.
75. Zheng, J., & He, L. (2014). Surface‐Enhanced Raman Spectroscopy for the Chemical Analysis of Food. Comprehensive Reviews in Food Science and Food Safety, 13(3), 317-328.