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Abstract

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 the 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 represent the chemical quality parameters of meats in the process can be saved to a database. 

Keywords

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

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Authors retain the copyright without restrictions for their published content in this journal. GCTL is a Sherpa Romeo Journal. 

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Author Biographies

Hasan Ibrahim Kozan, Necmettin Erbakan University, SeydiÅŸehir Vocational School, 42090, SeydiÅŸehir, Konya, Turkey

Necmettin Erbakan University, SeydiÅŸehir Vocational School, 42090, SeydiÅŸehir, Konya, Turkey

Cemalettin Sariçoban, Selçuk University, Agricultural Faculty, Department of Food Engineering, Selçuklu, Konya, Turkey

Selçuk University, Agricultural Faculty, Department of Food Engineering, Selçuklu, Konya, Turkey

Hasan Ali Akyürek, Necmettin Erbakan University, Department of Management Information Systems, Selçuklu, Konya, Turkey

Necmettin Erbakan University, Department of Management Information Systems, Selçuklu, Konya, Turkey

Ahmet Ãœnver, Necmettin Erbakan University, Department of Food Engineering, Meram, Konya, Turkey

Necmettin Erbakan University, Department of Food Engineering, Meram, Konya, Turkey
How to Cite
Kozan, H. I., Sariçoban, C., Akyürek, H. A., & Ãœnver, A. (2016). HYPERSPECTRAL IMAGING TECHNIQUE AS A STATE OF ART TECHNOLOGY IN MEAT SCIENCE. Green Chemistry & Technology Letters, 2(3), 127–137. https://doi.org/10.18510/gctl.2016.232
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References

  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. https://doi.org/10.1016/j.jfoodeng.2015.11.024
  4. Abouelkaram, S., Berge, P., & Culioli, J. (1997). Application of ultrasonic data to classify bovine muscles. Proceedings of IEEE Ultrasonics Symposium, 2, 1197-1200.
  6. 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.
  8. Alomar, D., Gallo, C., CastanËœeda, M., & Fuchslocher, R. (2003). Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS). Meat Science, 63, 441-450.
  9. https://doi.org/10.1016/S0309-1740(02)00101-8 DOI: https://doi.org/10.1016/S0309-1740(02)00101-8
  11. Bannon, D. 2009. Hyperspectral imaging: Cubes and slices. Nat. Photon. 3, 627.
  12. https://doi.org/10.1038/nphoton.2009.205 DOI: https://doi.org/10.1038/nphoton.2009.205
  14. Banović, M., Aguiar Fontes, M., Barreira, M. M., & Grunert, K. G. (2012). Impact of product familiarity on beef quality perception. Agribusiness, 28, 157-172.
  15. https://doi.org/10.1002/agr.21290 DOI: https://doi.org/10.1002/agr.21290
  17. 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.
  18. https://doi.org/10.1016/j.foodqual.2009.02.009 DOI: https://doi.org/10.1016/j.foodqual.2009.02.009
  20. 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.
  21. https://doi.org/10.1016/S0950-3293(98)00007-X DOI: https://doi.org/10.1016/S0950-3293(98)00007-X
  23. Chao, K.; Chen, Y.R.; Early, H.; Park, B. (1999) Color image classification systems for poultry viscera inspection. Appl. Eng. Agric., 15, 363-369.
  24. https://doi.org/10.13031/2013.5791 DOI: https://doi.org/10.13031/2013.5791
  26. 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
  28. 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.
  29. https://doi.org/10.1016/j.jfoodeng.2016.02.004 DOI: https://doi.org/10.1016/j.jfoodeng.2016.02.004
  31. Cimander, C., Carlsson, M., & Mandenius, C. F. (2002). Sensor fusion for on-line monitoring of yoghurt fermentation. Journal of biotechnology, 99(3), 237-248.
  32. https://doi.org/10.1016/S0168-1656(03)00121-4 DOI: https://doi.org/10.1016/S0168-1656(03)00121-4
  33. https://doi.org/10.1016/S0168-1656(02)00213-4 DOI: https://doi.org/10.1016/S0168-1656(02)00213-4
  35. 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.
  36. https://doi.org/10.1007/s11694-008-9052-2 DOI: https://doi.org/10.1007/s11694-008-9052-2
  38. Cruz, J.; Bautista, M.; Amigo, J.M.; Blanco, M. (2009), Nir-chemical imaging study of acetylsalicylic acid in commercial tablets. Talanta, 80, 473-478.
  39. https://doi.org/10.1016/j.talanta.2009.07.008 DOI: https://doi.org/10.1016/j.talanta.2009.07.008
  40. PMid:19836506
  42. Davies, S. (2014). Measurement of crispness in food products using acoustic-mechanical techniques: a literature review.
  44. 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.
  45. https://doi.org/10.1093/jn/133.11.3907S DOI: https://doi.org/10.1093/jn/133.11.3907S
  46. PMid:14672289
  48. ElMasry, G. & Sun, D., 2010. Principles of Hyperspectral Imaging Technology. Hyperspectral Imaging for Food Quality Analysis and Control, pp.3-43.
  49. https://doi.org/10.1016/B978-0-12-374753-2.10001-2 DOI: https://doi.org/10.1016/B978-0-12-374753-2.10001-2
  51. FAO, U. (2014). FAOstat. Retrieved Feb, 2014.
  53. FAO 2015, accessed 05.02.2016, http://www.fao.org/ag/againfo/themes/en/meat/home.html
  55. 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.
  56. https://doi.org/10.1016/j.meatsci.2004.09.010 DOI: https://doi.org/10.1016/j.meatsci.2004.09.010
  57. PMid:22062993
  59. 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.
  60. https://doi.org/10.1016/j.meatsci.2014.06.025 DOI: https://doi.org/10.1016/j.meatsci.2014.06.025
  61. PMid:25017317
  63. Food And Agricalture Organization of United States (FAO), Current Worldwide Annual Meat Consumption per capita, Livestock and Fish Primary Equivalent. <http://faostat.fao.org/site/610/DesktopDefault.aspx?PageID=610#ancor>(2013).
  65. 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.
  66. https://doi.org/10.1016/j.snb.2007.09.036 DOI: https://doi.org/10.1016/j.snb.2007.09.036
  68. 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.
  69. https://doi.org/10.1016/j.talanta.2007.04.054 DOI: https://doi.org/10.1016/j.talanta.2007.04.054
  70. PMid:19073095
  72. 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
  73. https://doi.org/10.3390/s90806058 DOI: https://doi.org/10.3390/s90806058
  74. PMid:22454572 PMCid:PMC3312430
  76. Goetz, A.F.H.; Vane, G.; Solomon, J.E.; Rock, B.N., 1985, Imaging spectrometry for earth remote-sensing. Science, 228, 1147-1153.
  77. https://doi.org/10.1126/science.228.4704.1147 DOI: https://doi.org/10.1126/science.228.4704.1147
  78. PMid:17735325
  80. 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.
  81. https://doi.org/10.1016/j.tifs.2015.08.001 DOI: https://doi.org/10.1016/j.tifs.2015.08.001
  83. 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.
  84. https://doi.org/10.1016/j.ifset.2013.02.009 DOI: https://doi.org/10.1016/j.ifset.2013.02.009
  86. 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.
  87. https://doi.org/10.1016/j.jfoodeng.2013.11.015 DOI: https://doi.org/10.1016/j.jfoodeng.2013.11.015
  89. 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.
  90. https://doi.org/10.1016/j.foodchem.2014.01.118 DOI: https://doi.org/10.1016/j.foodchem.2014.01.118
  91. PMid:24629986
  93. 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/.
  94. https://doi.org/10.3390/s140407248 DOI: https://doi.org/10.3390/s140407248
  95. PMid:24759119 PMCid:PMC4029639
  97. Huang, H.; Liu, L.; Ngadi, M.O.; Gariepy, C. 2013a. Prediction of pork marbling scores using pattern analysis techniques. Food Control, 31, 224-229.
  98. https://doi.org/10.1016/j.foodcont.2012.09.034 DOI: https://doi.org/10.1016/j.foodcont.2012.09.034
  100. 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.
  101. https://doi.org/10.1016/j.jfoodeng.2012.11.014 DOI: https://doi.org/10.1016/j.jfoodeng.2012.11.014
  103. 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.
  104. https://doi.org/10.1016/j.ifset.2012.06.003 DOI: https://doi.org/10.1016/j.ifset.2012.06.003
  106. 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.
  107. https://doi.org/10.1016/j.lwt.2015.11.021 DOI: https://doi.org/10.1016/j.lwt.2015.11.021
  109. 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.
  110. https://doi.org/10.1016/j.foodchem.2015.10.051 DOI: https://doi.org/10.1016/j.foodchem.2015.10.051
  111. PMid:26593592
  113. 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.
  114. https://doi.org/10.13031/2013.17595 DOI: https://doi.org/10.13031/2013.17595
  116. 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.
  117. https://doi.org/10.1016/j.foodchem.2015.11.084 DOI: https://doi.org/10.1016/j.foodchem.2015.11.084
  118. PMid:26675857
  120. 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.
  121. https://doi.org/10.1016/j.jfoodeng.2013.06.005 DOI: https://doi.org/10.1016/j.jfoodeng.2013.06.005
  123. Lim, K. S., & Barigou, M. (2004). X-ray micro-computed tomography of cellular food products. Food research international, 37(10), 1001-1012.
  124. https://doi.org/10.1016/j.foodres.2004.06.010 DOI: https://doi.org/10.1016/j.foodres.2004.06.010
  126. 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.
  127. https://doi.org/10.1016/S0309-1740(02)00328-5 DOI: https://doi.org/10.1016/S0309-1740(02)00328-5
  129. Lu, J.; Tan, J.; Shatadal, P.; Gerrard, D.E. 2000. Evaluation of pork color by using computer vision. Meat Sci., 56, 57-60.
  130. https://doi.org/10.1016/S0309-1740(00)00020-6 DOI: https://doi.org/10.1016/S0309-1740(00)00020-6
  132. 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.
  133. https://doi.org/10.1016/j.foodchem.2015.11.023 DOI: https://doi.org/10.1016/j.foodchem.2015.11.023
  134. PMid:26617026
  136. Mann, N. (2000). Dietary lean red meat and human evolution. European Journal of Nutrition, 39(2), 71-79.
  137. https://doi.org/10.1007/s003940050005 DOI: https://doi.org/10.1007/s003940050005
  138. PMid:10918988
  140. 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.
  141. https://doi.org/10.1016/j.foodres.2012.12.046 DOI: https://doi.org/10.1016/j.foodres.2012.12.046
  143. 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. DOI: https://doi.org/10.1016/j.meatsci.2009.08.029
  144. https://doi.org/10.1016/j.jfoodeng.2015.11.024 DOI: https://doi.org/10.1016/j.jfoodeng.2015.11.024
  146. 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.
  147. https://doi.org/10.1016/j.compag.2008.05.020 DOI: https://doi.org/10.1016/j.compag.2008.05.020
  149. 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.
  150. https://doi.org/10.1007/s11694-008-9051-3 DOI: https://doi.org/10.1007/s11694-008-9051-3
  152. 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.
  153. https://doi.org/10.2525/ecb.44.133 DOI: https://doi.org/10.2525/ecb.44.133
  155. 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.
  156. https://doi.org/10.1093/jn/137.4.1119 DOI: https://doi.org/10.1093/jn/137.4.1119
  157. PMid:17374691
  159. Osborne, B. G. 2006. Near-Infrared Spectroscopy in Food Analysis. Encyclopedia of Analytical Chemistry (Book), John Wiley & Sons, Ltd, 2006
  161. 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.
  162. https://doi.org/10.1016/S0309-1740(02)00298-X DOI: https://doi.org/10.1016/S0309-1740(02)00298-X
  164. 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.
  165. https://doi.org/10.1016/j.jfoodeng.2005.03.060 DOI: https://doi.org/10.1016/j.jfoodeng.2005.03.060
  167. 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.
  168. https://doi.org/10.1007/s13197-011-0321-4 DOI: https://doi.org/10.1007/s13197-011-0321-4
  169. PMid:23572836 PMCid:PMC3550871
  171. 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.
  172. https://doi.org/10.1016/j.jfoodeng.2007.02.038 DOI: https://doi.org/10.1016/j.jfoodeng.2007.02.038
  174. 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.
  175. https://doi.org/10.2202/1556-3758.1089 DOI: https://doi.org/10.2202/1556-3758.1089
  177. 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.
  178. https://doi.org/10.1080/05704928.2012.745418 DOI: https://doi.org/10.1080/05704928.2012.745418
  180. 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.
  181. https://doi.org/10.1255/jnirs.851 DOI: https://doi.org/10.1255/jnirs.851
  183. 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.
  184. https://doi.org/10.1016/j.meatsci.2004.01.013 DOI: https://doi.org/10.1016/j.meatsci.2004.01.013
  185. PMid:22062405
  187. 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.
  188. https://doi.org/10.1016/j.meatsci.2004.08.011 DOI: https://doi.org/10.1016/j.meatsci.2004.08.011
  189. PMid:22062978
  191. Sun, D.-W. (2010). Hyperspectral imaging for food quality analysis and control. San Diego, California, USA: Academic Press/Elsevier.
  193. Tan, J.L. 2004. Meat quality evaluation by computer vision. J. Food Eng., 61, 27-35.
  194. https://doi.org/10.1016/S0260-8774(03)00185-7 DOI: https://doi.org/10.1016/S0260-8774(03)00185-7
  196. 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.
  198. 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.
  199. https://doi.org/10.1016/j.meatsci.2004.08.005 DOI: https://doi.org/10.1016/j.meatsci.2004.08.005
  200. PMid:22062827
  202. 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.
  203. https://doi.org/10.2527/2003.812457x DOI: https://doi.org/10.2527/2003.812457x
  204. PMid:12643490
  206. Webb, K., Rutishauser, I., Katz, T., Knezevic, N., Lahtiâ€Koski, M., Peat, J., & Mihrshahi, S. (2005). Meat consumption among 18â€monthâ€old children participating in the Childhood Asthma Prevention Study. Nutrition & Dietetics, 62(1), 12-20.
  207. https://doi.org/10.1111/j.1747-0080.2005.tb00004.x DOI: https://doi.org/10.1111/j.1747-0080.2005.tb00004.x
  209. 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.
  210. https://doi.org/10.1002/agr.1022 DOI: https://doi.org/10.1002/agr.1022
  212. 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).
  214. 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).
  216. 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.
  217. https://doi.org/10.1255/jnirs.587 DOI: https://doi.org/10.1255/jnirs.587
  219. 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.
  220. https://doi.org/10.1016/j.ifset.2012.08.003 DOI: https://doi.org/10.1016/j.ifset.2012.08.003
  222. 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.
  223. https://doi.org/10.1016/j.biosystemseng.2004.08.002 DOI: https://doi.org/10.1016/j.biosystemseng.2004.08.002
  225. 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.
  226. https://doi.org/10.1016/j.biosystemseng.2013.03.006 DOI: https://doi.org/10.1016/j.biosystemseng.2013.03.006
  228. 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.
  229. https://doi.org/10.1016/j.proeng.2012.09.409 DOI: https://doi.org/10.1016/j.proeng.2012.09.409
  231. Zheng, J., & He, L. (2014). Surfaceâ€Enhanced Raman Spectroscopy for the Chemical Analysis of Food. Comprehensive Reviews in Food Science and Food Safety, 13(3), 317-328.
  232. https://doi.org/10.1111/1541-4337.12062 DOI: https://doi.org/10.1111/1541-4337.12062