Applications of emerging imaging techniques for meat quality and safety detection and evaluation: A review

References

• Adedeji, A. A., Liu, L. and Ngadi, M. O. (2011). Microstructural evaluation of deep-fat fried chicken nugget batter coating using confocal laser scanning microscopy. J. Food Eng. 102(1):49–57.
   Web of Science ®Google Scholar
• Aursand, I. G., Erikson, U. and Veliyulin, E. (2010). Water properties and salt uptake in Atlantic salmon fillets as affected by ante-mortem stress, rigor mortis, and brine salting: A low-field 1H NMR and 1H 23Na MRI study. Food Chem. 120(2):482–489.
   Web of Science ®Google Scholar
• Aursand, I. G., Veliyulin, E., Böcker, U., Ofstad, R., Rustad, T. and Erikson, U. (2008). Water and salt distribution in Atlantic salmon (Salmo salar) studied by low-field 1H NMR, 1H and 23Na MRI and light microscopy: Effects of raw material quality and brine salting. J. Agric. Food Chem. 57(1):46–54.
   Web of Science ®Google Scholar
• Ayuso, D., González, A., Hernández, F., Corral, J. and Izquierdo, M. (2013). Prediction of carcass composition, ham and foreleg weights, and lean meat yields of Iberian pigs using ultrasound measurements in live animals. J. Anim. Sci. 91(4):1884–1892.
   PubMed Web of Science ®Google Scholar
• Aaslyng, M. D., Bejerholm, C., Ertbjerg, P., Bertram, H. C. and Andersen, H. J. (2003). Cooking loss and juiciness of pork in relation to raw meat quality and cooking procedure. Food Qual. Prefer. 14(4):277–288.
   Web of Science ®Google Scholar
• Ballerini, L., Hogberg, A., Borgefors, G., Bylund, A.-C., Lindgard, A., Lundstrom, K., Rakotonirainy, O. and Soussi, B. (2002). A segmentation technique to determine fat content in NMR images of beef meat. Nucl. Sci. IEEE Trans. 49(1):195–199.
   Web of Science ®Google Scholar
• Barbin, D. F., ElMasry, G., Sun, D.-W. and Allen, P. (2012a). Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging. Anal. Chim. Acta. 719:30–42.
   PubMed Web of Science ®Google Scholar
• Barbin, D., Elmasry, G., Sun, D.-W. and Allen, P. (2012b). Near-infrared hyperspectral imaging for grading and classification of pork. Meat Sci. 90(1):259–268.
   PubMed Web of Science ®Google Scholar
• Barbin, D. F., ElMasry, G., Sun, D.-W. and Allen, P. (2013a). Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging. Food Chem. 138(2):1162–1171.
   PubMed Web of Science ®Google Scholar
• Barbin, D. F., Sun, D.-W. and Su, C. (2013b). NIR hyperspectral imaging as non-destructive evaluation tool for the recognition of fresh and frozen-thawed porcine longissimus dorsi muscles. Innov. Food Sci. Emerg. Technol. 18:226–236.
   Web of Science ®Google Scholar
• Barbin, D. F., Valous, N. A. and Sun, D.-W. (2013c). Tenderness prediction in porcine longissimus dori muscles using instrumental measuremnt along with NIR hyperpsectral imaging and computer vision imagery. Innov. Food Sci. Emerg. Technol. 20:335–342.
   Web of Science ®Google Scholar
• Barbin, D. F., ElMasry, G., Sun, D.-W., Allen, P. and Morsy, N. (2013d). Non-destructive assessment of microbial contamination in porcine meat using NIR hyperspectral imaging. Innov. Food Sci. Emerg. Technol. 17:180–191.
   Web of Science ®Google Scholar
• Berry, B. (2000). Use of infrared thermography to assess temperature variability in beef patties cooked from the frozen and thawed states. Foodserv. Res. Int. 12(4):255–262.
   Google Scholar
• Bonny, J. M., Laurent, W., Labas, R., Taylor, R., Berge, P. and Renou, J. P. (2001a). Magnetic resonance imaging of connective tissue: A non-destructive method for characterising muscle structure. J. Sci. Food Agric. 81(3):337–341.
   Web of Science ®Google Scholar
• Bonny, J., Laurent, W. and Renou, J. (2001b). Characterisation of meat structure by NMR imaging at high field. Spec. Publ. R. Soc. Chem. 262:17–21.
   Google Scholar
• Bouhrara, M., Clerjon, S., Damez, J.-L., Chevarin, C., Portanguen, S., Kondjoyan, A. and Bonny, J.-M. (2011). Dynamic MRI and thermal simulation to interpret deformation and water transfer in meat during heating. J. Agric. Food Chem. 59(4):1229–1235.
   PubMed Web of Science ®Google Scholar
• Bouhrara, M., Lehallier, B., Clerjon, S., Damez, J.-L. and Bonny, J.-M. (2012). Mapping of muscle deformation during heating: in situ dynamic MRI and nonlinear registration. Magn. Reson. Imaging. 30(3):422–430.
   PubMed Web of Science ®Google Scholar
• Brix, O., Apablaza, P., Baker, A., Taxt, T. and Grüner, R. (2009). Chemical shift-based MR imaging and gas chromatography for quantification and localization of fat in Atlantic mackerel. J. Exp. Mar. Biol. Ecol. 376(2):68–75.
   Web of Science ®Google Scholar
• Burfoot, D., Tinker, D., Thorn, R. and Howell, M. (2011). Use of fluorescence imaging as a hygiene indicator for beef and lamb carcasses in UK slaughterhouses. Biosyst. Eng. 109(3):175–185.
   Web of Science ®Google Scholar
• Cluff, K., Naganathan, G. K., Subbiah, J., Lu, R., Calkins, C. R. and Samal, A. (2008). Optical scattering in beef steak to predict tenderness using hyperspectral imaging in the VIS-NIR region. Sensing Inst. Food Qual. Saf. 2(3):189–196.
   Google Scholar
• Cernadas, E., Carrión, P., Rodríguez, P. G., Muriel, E. and Antequera, T. (2005). Analyzing magnetic resonance images of Iberian pork loin to predict its sensorial characteristics. Comput. Vis. Image Understanding. 98(2):344–360.
   Web of Science ®Google Scholar
• Chao, K., Yang, C. C., Chen, Y. R., Kim, M. S. and Chan, D. E. (2007). Hyperspectral-multispectral line-scan imaging system for automated poultry carcass inspection applications for food safety. Poul. Sci. 86 (11):2450–2460.
   PubMed Web of Science ®Google Scholar
• Chao, K., Yang, C., Kim, M. and Chan, D. (2008). High throughput spectral imaging system for wholesomeness inspection of chicken. Appl. Eng. Agric. 24(4):475–485.
   Web of Science ®Google Scholar
• Chao, K. L., Yang, C. C. and Kim, M. S. (2010). Spectral line-scan imaging system for high-speed non-destructive wholesomeness inspection of broilers. Trend. Food Sci. Technol. 21(3):129–137.
   Web of Science ®Google Scholar
• Chen, Y.-R., Meyer, G. E. and Tu, S.-I. Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality. 23 October, Boston, MA, USA.
   Google Scholar
• Chen, Y.-R., Meyer, G. E. and Tu, S.-I. Optics for Natural Resources, Agriculture, and Foods. 1 October, Boston, MA, USA.
   Google Scholar
• Cheng, J. H. and Sun, D. W. (2015). Rapid quantification analysis and visualization of Escherichia coli loads in grass carp fish flesh by hyperspectral imaging method. Food Bioproc. Technol. 8(5):951–959.
   Web of Science ®Google Scholar
• Cheng, J. H., Sun, D. W., Pu, H. and Zeng, X. A. (2014). Comparison of visible and long-wave near-infrared hyperspectral imaging for colour measurement of grass carp (Ctenopharyngodon idella). Food Bioproc. Technol. 7(11):3109–3120.
   Web of Science ®Google Scholar
• Cho, B., Kim, M. S., Chao, K., Lawrence, K., Park, B. and Kim, K. (2009). Detection of fecal residue on poultry carcasses by laser-induced fluorescence imaging. J. Food Sci. 74(3):E154–E159.
   PubMed Web of Science ®Google Scholar
• Cho, B. K. and Irudayaraj, J. (2003). Foreign object and internal disorder detection in food materials using noncontact ultrasound imaging. J. Food Sci. 68(3):967–974.
   Web of Science ®Google Scholar
• Clark, C. J. and MacFall, J. S. (2003). Quantitative magnetic resonance imaging of “Fuyu”persimmon fruit during development and ripening. Magn. Reson. Imaging. 21(6):679–685.
   PubMed Web of Science ®Google Scholar
• Collewet, G., Bogner, P., Allen, P., Busk, H., Dobrowolski, A., Olsen, E. and Davenel, A. (2005). Determination of the lean meat percentage of pig carcasses using magnetic resonance imaging. Meat Sci. 70(4):563–572.
   PubMed Web of Science ®Google Scholar
• Cordeiro, A. D. S., Nääs, I. D. A. and Neves, D. (2012). The use of thermal images for identifying stress condition in piglets. In: The Ninth International Livestock Environment Symposium (ILES IX). International Conference of Agricultural Engineering -- CIGR-AgEng 2012: Agriculture and Engineering for a Healthier Life, Valencia, Spain, 8–12 July 2012, p. C–0908; CIGR-EurAgEng.
   Google Scholar
• Costa, C., D'Andrea, S., Russo, R., Antonucci, F., Pallottino, F. and Menesatti, P. (2011). Application of non-invasive techniques to differentiate sea bass (Dicentrarchus labrax, L. 1758) quality cultured under different conditions. Aquac. Int. 19(4):765–778.
   Web of Science ®Google Scholar
• Costa, L. N., Stelletta, C., Cannizzo, C., Gianesella, M., Fiego, D. L. and Morgante, M. (2010). The use of thermography on the slaughter-line for the assessment of pork and raw ham quality. Ital. J. Anim. Sci. 6(1s):704–706.
   Google Scholar
• Cui, Z.-W., Sun, L.-J., Chen, W. and Sun, D.-W. (2008). Preparation of dry honey by microwave-vacuum drying. J. Food Engin. 84(4):582–590.
   Web of Science ®Google Scholar
• Dissing, B. S., Nielsen, M. E., Ersboll, B. K. and Frosch, S. (2011). Multispectral imaging for determination of astaxanthin concentration in salmonids. PLoS One. 6(5):e19032.
   PubMed Web of Science ®Google Scholar
• Du, Z., Jeong, M. K. and Kong, S. G. (2007). Band selection of hyperspectral images for automatic detection of poultry skin tumors. Autom. Sci. Eng. IEEE Tran. 4(3):332–339.
   Web of Science ®Google Scholar
• Dai, Q., Cheng, J.-H., Sun, D.-W. and Zeng, X.-A. (2014). Potential of hyperspectral imaging for non-invasive determination of mechanical properties of prawn (Metapenaeus ensis). J. Food Eng. 136:64–72.
   Web of Science ®Google Scholar
• Damez, J., Clerjon, S., Labas, R., Danon, J., Peyrin, F. and Bonny, J. (2012). Microstructure characterization of meat by quantitative MRI. In: 58th International Congress of Meat Science and Technology, pp. 25–28, 12–17 August 2012, Montreal, Canada.
   Google Scholar
• Damez, J. L. and Clerjon, S. (2013). Quantifying and predicting meat and meat products quality attributes using electromagnetic waves: An overview. Meat Sci. 95(4):879–896.
   PubMed Web of Science ®Google Scholar
• Davenel, A., Bazin, C., Quellec, S., Challois, S., Gispert, M., Mercat, M. and Muller, N. (2012). High throughput determination of intramuscular fat content by magnetic resonance imaging. J. de la Recherche Porcine en France. 44:53–54.
   Google Scholar
• Delgado, A, E. and Sun, D.-W. (2002). Desorption isotherms and glass transition temperature for chicken meat. J. Food Engin. 55(1):1–8. Article Number: PII S0206-8774(01)00222-9
   Web of Science ®Google Scholar
• Desmond, E. M., Kenny, T. A., Ward, P. and Sun, D.-W. (2000). Effect of rapid and conventional cooling methods on the quality of cooked ham joints. Meat Science 56(3):271–277.
   PubMed Web of Science ®Google Scholar
• do Prado Paim, T., Borges, B. O., Lima, P. D. M. T., Gomes, E. F., Dallago, B. S. L., Fadel, R., de Menezes, A. M., Louvandini, H., Canozzi, M. E. A. and Barcellos, J. O. J. (2013). Thermographic evaluation of climatic conditions on lambs from different genetic groups. Int. J. Biometeorol. 57(1):59–66.
   PubMed Web of Science ®Google Scholar
• Du, C. J. and Sun, D.-W. (2004). Recent developments in the applications of image processing techniques for food quality evaluation. Trend. Food Sci. Technol. 15(5):230–249.
   Web of Science ®Google Scholar
• ElMasry, G., Barbin, D. F., Sun, D.-W. and Allen, P. (2012a). Meat quality evaluation by hyperspectral imaging technique: an overview. Crit. Rev. Food Sci. Nutr. 52(8):689–711.
   PubMed Web of Science ®Google Scholar
• ElMasry, G., Kamruzzaman, M., Sun, D.-W. and Allen, P. (2012). Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review. Critical Reviews in Food Science and Nutrition. 52:999–1023.
   PubMed Web of Science ®Google Scholar
• ElMasry, G., Sun, D.-W. and Allen, P. (2012b). Near-infrared hyperspectral imaging for predicting colour, pH, and tenderness of fresh beef. J. Food Eng. 110(1):127–140.
   Web of Science ®Google Scholar
• ElMasry, G., Sun, D.-W. and Allen, P. (2013). Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging. J. Food Eng. 117(2):235–246.
   Web of Science ®Google Scholar
• Erikson, U., Veliyulin, E., Singstad, T. and Aursand, M. (2004). Salting and desalting of fresh and frozen-thawed cod (Gadus morhua) fillets: A comparative study using 23Na NMR, 23Na MRI, low-field 1H NMR, and physicochemical analytical methods. J. Food Sci. 69(3):FEP107–FEP114.
   Web of Science ®Google Scholar
• Feng, Y.-Z., ElMasry, G., Sun, D.-W., Scappell, A. G. M., Walsh, D. and Morcy, N. (2013). Near-infrared hyperspectral imaging and partial least squares regression for rapid and reagentless determination of Enterobacteriaceae on chicken fillets. Food Chemistry. 138(2-3):1829–1836.
   PubMed Web of Science ®Google Scholar
• Feng, Y.-Z. and Sun, D.-W. (2012). Application of Hyperspectral Imaging in Food Safety Inspection and Control: A Review. Critical Reviews in Food Science and Nutrition. 52:1039–1058.
   PubMed Web of Science ®Google Scholar
• Feng, Y. Z. and Sun, D.-W. (2013a). Determination of total viable count (TVC) in chicken breast fillets by near-infrared hyperspectral imaging and spectroscopic transforms. Talanta. 105:244–249.
   PubMed Web of Science ®Google Scholar
• Feng, Y. Z. and Sun, D.-W. (2013b). Near-infrared hyperspectral imaging in tandem with partial least squares regression and genetic algorithm for non-destructive determination and visualization of pseudomonas loads in chicken fillets. Talanta. 109:74–83.
   PubMed Web of Science ®Google Scholar
• Fletcher, J. and Kong, S. (2003). Principal component analysis for poultry tumor inspection using hyperspectral fluorescence imaging. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 149–153. IEEE, 20–24 July 2003, Washington, DC.
   Google Scholar
• Font i Furnols, M., Teran, M. F. and Gispert, M. (2009). Estimation of lean meat content in pig carcasses using X-ray computed tomography and PLS regression. Chemometr. Intell. Lab. Sys. 98(1):31–37.
   Web of Science ®Google Scholar
• Fortin, A., Tong, A., Robertson, W., Zawadski, S., Landry, S., Robinson, D., Liu, T. and Mockford, R. (2003). A novel approach to grading pork carcasses: Computer vision and ultrasound. Meat Sci. 63(4):451–462.
   PubMed Web of Science ®Google Scholar
• Fukuda, O., Nabeoka, N. and Miyajima, T. (2013). Estimation of marbling score in live cattle based on ICA and a neural network. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2013, pp. 1622–1627; IEEE, Washington, DC.
   Google Scholar
• Gowen, A. A., Taghizadeh, M. and O'Donnell, C. P. (2009). Identification of mushrooms subjected to freeze damage using hyperspectral imaging. J. Food Eng. 93(1):7–12.
   Web of Science ®Google Scholar
• Galed, G., Fernández-Valle, M., Martınez, A. and Heras, A. (2004). Application of MRI to monitor the process of ripening and decay in citrus treated with chitosan solutions. Magn. Reson. Imaging. 22(1):127–137.
   PubMed Web of Science ®Google Scholar
• GuÐjónsdóttir, M., Belton, P. and Webb, G. (2009). Sodium MRI as a tool for optimization of salting processes. Magnetic Resonance in Food Science : Challenges in a Changing World. 4:3–11.
   Google Scholar
• Håseth, T., Høy, M., Kongsro, J., Kohler, A., Sørheim, O. and Egelandsdal, B. (2008). Determination of sodium chloride in pork meat by computed tomography at different voltages. J. Food Sci. 73(7), E333–E339.
   PubMed Web of Science ®Google Scholar
• Hansen, C. L., van der Berg, F., Ringgaard, S., Stødkilde-Jørgensen, H. and Karlsson, A. H. (2008). Diffusion of NaCl in meat studied by 1H and 23Na magnetic resonance imaging. Meat Sci. 80(3):851–856.
   PubMed Web of Science ®Google Scholar
• Harron, W. and Dony, R. (2009). Predicting quality measures in beef cattle using ultrasound imaging. In: IEEE Symposium on Computational Intelligence for Image Processing, 2009 (CIIP’09), pp. 96–103; IEEE, Washington, DC.
   Google Scholar
• Hassen, A., Wilson, D., Amin, V., Rouse, G. and Hays, C. (2001). Predicting percentage of intramuscular fat using two types of real-time ultrasound equipment. J. Anim. Sci. 79(1):11–18.
   PubMed Web of Science ®Google Scholar
• He, H.-J., Wu, D. and Sun, D.-W. (2012). Application of hyperspectral imaging technique for non-destructive pH prediction in salmon fillets. Biosys. Eng. Res. Rev. 17:5.
   Google Scholar
• He, H.-J., Wu, D. and Sun, D.-W. (2014). Potential of hyperspectral imaging combined with chemometric analysis for assessing and visualising tenderness distribution in raw farmed salmon fillets. J. Food Eng. 126:156–164.
   Web of Science ®Google Scholar
• Haralick, R. M., Shanmugam, K. and Dinstein, I. H. (1973). Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6:610–621.
   Web of Science ®Google Scholar
• Huang, X., Xin, J., & Zhao, J. (2011). A novel technique for rapid evaluation of fish freshness using colorimetric sensor array. J. Food Eng. 105(4):632–637.
   Web of Science ®Google Scholar
• Ibarra, J. G., Tao, Y. and Xin, H. (2000). Combined IR imaging-neural network method for the estimation of internal temperature in cooked chicken meat. Opt. Eng. 39(11):3032–3038.
   Web of Science ®Google Scholar
• Ivorra, E., Girón, J., Sánchez, A. J., Verdú, S., Barat, J. M. and Grau, R. (2013). Detection of expired vacuum-packed smoked salmon based on PLS-DA method using hyperspectral images. J. Food Eng. 117(3):342–349
   Web of Science ®Google Scholar
• Jackman, P., Sun, D.-W. and Allen, P. (2009). Automatic segmentation of beef longissimus dorsi muscle and marbling by an adaptable algorithm. Meat Science. 83(2):187–194.
   PubMed Web of Science ®Google Scholar
• Jackman, P., Sun, D.-W., Du, C.-J, and Allen, P. (2009). Prediction of beef eating qualities from colour, marbling and wavelet surface texture features using homogenous carcass treatment. Pattern Recognition 42(5):751–763.
   Web of Science ®Google Scholar
• Jensen, T. H., Bottiger, A., Bech, M., Zanette, I., Weitkamp, T., Rutishauser, S., David, C., Reznikova, E., Mohr, J., Christensen, L. B., Olsen, E. V., Feidenhans'l, R. and Pfeiffer, F. (2011). X-ray phase-contrast tomography of porcine fat and rind. Meat Sci. 88(3):379–383.
   PubMed Web of Science ®Google Scholar
• Kamruzzaman, M., ElMasry, G., Sun, D.-W. and Allen, P. (2012a). Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression. Innov. Food Sci. Emerg. Technol. 16:218–226.
   Web of Science ®Google Scholar
• Kamruzzaman, M., ElMasry, G., Sun, D.-W. and Allen, P. (2012b). Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. Anal. Chim. Acta. 714:57–67.
   PubMed Web of Science ®Google Scholar
• Kamruzzaman, M., ElMasry, G., Sun, D.-W. and Allen, P. (2013). Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. Food Chem. 141(1):389–396.
   PubMed Web of Science ®Google Scholar
• Karamichou, E., Richardson, R., Nute, G., McLean, K. and Bishop, S. (2006). Genetic analyses of carcass composition, as assessed by X-ray computer tomography, and meat quality traits in Scottish Blackface sheep. Anim. Sci. Glasg. 82(2):151.
   Google Scholar
• Kandpal, L. M., Lee, H., Kim, M. S., Mo, C. and Cho, B. K. (2013). Hyperspectral reflectance imaging technique for visualization of moisture distribution in cooked chicken breast. Sensors. 13(10):13289–13300.
   PubMed Web of Science ®Google Scholar
• Kiani, H., Zhang, Z., Delgado, A. and Sun, Da-Wen. (2011). Ultrasound assisted nucleation of some liquid and solid model foods during freezing. Food Res. International 44(9): 2915–2921.
   Web of Science ®Google Scholar
• Kim, M. S., Tu, S.-I, Chao, K. Sensing for Agriculture and Food Quality and Safety. 13 April, Orlando, Florida, USA.
   Google Scholar
• Kim, M. S., Tu, S.-I, Chao, K. Sensing for Agriculture and Food Quality and Safety II. 5 April, Orlando, Florida, USA.
   Google Scholar
• Kim, M. S., Lefcourt, A. M. and Chen, Y.-R. (2003). Multispectral laser-induced fluorescence imaging system for large biological samples. Appl. Opt. 42(19):3927–3934.
   PubMed Web of Science ®Google Scholar
• Kim, I., Kim, M., Chen, Y. and Kong, S. (2004). Detection of skin tumors on chicken carcasses using hyperspectral fluorescence imaging.
   Google Scholar
• Kobayashi, K.-I., Matsui, Y., Maebuchi, Y., Toyota, T. and Nakauchi, S. (2010). Near infrared spectroscopy and hyperspectral imaging for prediction and visualisation of fat and fatty acid content in intact raw beef cuts. J. Near Infrared Spec. 18(5):301–315.
   Web of Science ®Google Scholar
• Korver, D., Saunders-Blades, J. and Nadeau, K. (2004). Assessing bone mineral density in vivo: Quantitative computed tomography. Poul. Sci. 83(2):222–229.
   PubMed Web of Science ®Google Scholar
• Kong, S. G., Chen, Y.-R., Kim, I. and Kim, M. S. (2004). Analysis of hyperspectral fluorescence images for poultry skin tumor inspection. Appl. Opt. 43(4):824–833.
   PubMed Web of Science ®Google Scholar
• Kremer, P., Förster, M. and Scholz, A. (2013). Use of magnetic resonance imaging to predict the body composition of pigs in vivo. Animal. 7(06):879–884.
   PubMed Web of Science ®Google Scholar
• Li, Y., Shan, J., Peng, Y. and Gao, X (2011). Nondestructive assessment of beef-marbling grade using hyperspectral imaging technology. In: International Conference on New Technology of Agricultural Engineering (ICAE), 2011, pp 779–783. Zibo, China, 27-29 May, IEEE.
   Google Scholar
• Liu, D., Ma, J., Sun, D. W., Pu, H., Gao, W., Qu, J., and Zeng, X. A. (2014). Prediction of color and pH of salted porcine meats using visible and near-infrared hyperspectral imaging. Food Bioproc. Technol. 7(11):3100–3108.
   Web of Science ®Google Scholar
• Liu, D., Qu, J., Sun, D.-W., Pu, H. and Zeng, X.-A. (2013). Non-destructive prediction of salt contents and water activity of porcine meat slices by hyperspectral imaging in a salting process. Innov. Food Sci. Emerg. Technol. 20:316–323.
   Web of Science ®Google Scholar
• Liu, D., Pu, H., Sun, D.-W., Wang, L. and Zeng, X.-A. (in press). Combination of spectra and texture data of hyperspectral imaging for prediction of pH in salted meat. Food Chem. 160:330–337.
   Web of Science ®Google Scholar
• Liu, D., Sun, D.-W. and Zeng, X.-A. (2014). Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry. Food and bioprocess Technology 7(2):307–323.
   Web of Science ®Google Scholar
• Liu, D., Sun, D.-W., Qu, J., Zeng, X. A., Pu, H. and Ma, J. (2014b). Feasibility of using hyperspectral imaging to predict moisture content of porcine meat during salting process. Food Chem. 152:197–204.
   PubMed Web of Science ®Google Scholar
• Laurent, W., Bonny, J. and Renou, J. (2000). Muscle characterisation by NMR imaging and spectroscopic techniques. Food Chem. 69(4):419–426.
   Web of Science ®Google Scholar
• Lawrence, K., Windham, W., Smith, D., Park, B. and Feldner, P. (2006). Effect of broiler carcass washing on fecal contaminant imaging. Trans. Amer. Soc. Agric. Eng. 49(1):133.
   Web of Science ®Google Scholar
• Lawrence, T., Spire, M., Dikeman, M. E., Hunt, M. C., Hogge, S. and James, B. (2010). Utilizing Infrared Thermography to Predict Pork Quality. Report of Progress, Agricultural Experiment Station and Cooperative Extension Service, Kansas State University; 880.
   Google Scholar
• Liang, H.-D., Noble, J. A. and Wells, P. N. T. (2011). Recent advances in biomedical ultrasonic imaging techniques. Interface Focus 1(4):475–476.
   Web of Science ®Google Scholar
• Lawrence, K. C., Windham, W. R., Park, B. and Buhr, R. J. (2003). A hyperspectral imaging system for identification of faecal and ingesta contamination on poultry carcasses. J. Near Infrared Spec. 11(4):269–281.
   Web of Science ®Google Scholar
• Macfarlane, J., Lewis, R., Emmans, G., Young, M. and Simm, G. (2006). Predicting carcass composition of terminal sire sheep using X-ray computed tomography. Anim. Sci. Glasg. 82(3):289.
   Google Scholar
• MacMillan, B., Hickey, H., Newling, B., Ramesh, M. and Balcom, B. (2008). Magnetic resonance measurements of French fries to determine spatially resolved oil and water content. Food Res. Int. 41(6):676–681.
   Web of Science ®Google Scholar
• Marelli, S., Redaelli, V., Cozzi, M. and Luzi, F. (2012). Thermography: A non-invasive method to investigate thermoregulation as welfare indicator in naked neck broiler chickens. In: Quantitative InfraRed Thermography Conference, Gennaro Cardone. 11th International Conference on Quantitative InfraRed Thermography, 11–14 June 2012, Naples, Italy.
   Google Scholar
• Mehl, P. M., Chen, Y.-R., Kim, M. S. and Chan, D. E. (2004). Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations. J. Food Eng. 61(1):67–81.
   Web of Science ®Google Scholar
• Mery, D., Lillo, I., Loebel, H., Riffo, V., Soto, A., Cipriano, A. and Aguilera, J. M. (2011). Automated fish bone detection using X-ray imaging. J. Food Eng. 105(3):485–492.
   Web of Science ®Google Scholar
• Mohrmann, M., Roehe, R., Susenbeth, A., Baulain, U., Knap, P., Looft, H., Plastow, G. and Kalm, E. (2006). Association between body composition of growing pigs determined by magnetic resonance imaging, deuterium dilution technique, and chemical analysis. Meat Sci. 72(3):518–531.
   PubMed Web of Science ®Google Scholar
• Monziols, M., Collewet, G., Bonneau, M., Mariette, F., Davenel, A. and Kouba, M. (2006). Quantification of muscle, subcutaneous fat and intermuscular fat in pig carcasses and cuts by magnetic resonance imaging. Meat Sci. 72(1):146–154.
   PubMed Web of Science ®Google Scholar
• Naganathan, G. K., Grimes, L. M., Subbiah, J., Calkins, C. R., Samal, A. and Meyer, G. E. (2008a). Partial least squares analysis of near-infrared hyperspectral images for beef tenderness prediction. Sens. Instrum. Food Qual. Saf. 2(3):178–188.
   Google Scholar
• Naganathan, G. K., Grimes, L. M., Subbiah, J., Calkins, C. R., Samal, A. and Meyer, G. E. (2008b). Visible/near-infrared hyperspectral imaging for beef tenderness prediction. Comput. Electron. Agric. 64(2):225–233.
   Web of Science ®Google Scholar
• Nakariyakul, S. and Casasent, D. P. (2009). Fast feature selection algorithm for poultry skin tumor detection in hyperspectral data. J. Food Eng. 94(3):358–365.
   Web of Science ®Google Scholar
• Navajas, E., Lambe, N., Fisher, A., Nute, G., Bünger, L. and Simm, G. (2008). Muscularity and eating quality of lambs: Effects of breed, sex and selection of sires using muscularity measurements by computed tomography. Meat Sci. 79(1):105–112.
   PubMed Web of Science ®Google Scholar
• Navajas, E., Lambe, N., McLean, K., Glasbey, C., Fisher, A., Charteris, A., Bünger, L. and Simm, G. (2007). Accuracy of in vivo muscularity indices measured by computed tomography and their association with carcass quality in lambs. Meat Sci. 75(3):533–542.
   PubMed Web of Science ®Google Scholar
• Newcom, D., Baas, T. and Lampe, J. (2002). Prediction of intramuscular fat percentage in live swine using real-time ultrasound. J. Anim. Sci. 80(12):3046–3052.
   PubMed Web of Science ®Google Scholar
• Pérez-Palacios, T., Antequera, T., Durán, M. L., Caro, A., Rodríguez, P. G. and Palacios, R. (2011). MRI-based analysis of feeding background effect on fresh Iberian ham. Food Chem. 126(3):1366–1372.
   Web of Science ®Google Scholar
• Park, B., Lawrence, K., Windham, W. and Buhr, R. J. (2002). Hyperspectral imaging for detecting fecal and ingesta contaminants on poultry carcasses. Trans. ASAE. 45(6):2017–2026.
   Google Scholar
• Park, B., Lawrence, K. C., Windham, W. R. and Smith, D. P. (2006). Performance of hyperspectral imaging system for poultry surface fecal contaminant detection. J. Food Eng., 75(3):340–348.
   Web of Science ®Google Scholar
• Peng, Y., Zhang, J., Wu, J. and Hang, H. (2009). Hyperspectral scattering profiles for prediction of the microbial spoilage of beef. In: Proceedings of SPIE, Vol, pp. 73150Q–73150Q-1.
   Google Scholar
• Peng, Y., Tao, F., Li, Y., Wang, W., Chen, J., Wu, J. and Dhakal, S. (2010). Rapid detection of total viable count of chilled pork using hyperspectral scattering technique. In: Proceedings of SPIE, Defense, Security, and Sensing, 2010, pp 76760K–76760K-8; International Society for Optics and Photonics.
   Google Scholar
• Peng, Y. K., Zhang, J., Wang, W., Li, Y. Y., Wu, J. H., Huang, H., Gao, X. D. and Jiang, W. K. (2011). Potential prediction of the microbial spoilage of beef using spatially resolved hyperspectral scattering profiles. J. Food Eng. 102(2):163–169.
   Web of Science ®Google Scholar
• Prieto, N., Navajas, E., Richardson, R., Ross, D., Hyslop, J., Simm, G. and Roehe, R. (2010). Predicting beef cuts composition, fatty acids and meat quality characteristics by spiral computed tomography. Meat Sci. 86(3):770–779.
   PubMed Web of Science ®Google Scholar
• Pallav, P., Hutchins, D. A. and Gan, T. (2009). Air-coupled ultrasonic evaluation of food materials. Ultrasonics. 49(2):244–253.
   PubMed Web of Science ®Google Scholar
• Picouet, P. A., Teran, F., Gispert, M. and Furnols, M. F. I. (2010). Lean content prediction in pig carcass, loin and ham by computed tomography (CT) using a density model. Meat Sci. 86(3):616–622.
   PubMed Web of Science ®Google Scholar
• Pu, H., Xie, A., Sun, D. W., Kamruzzaman, M. and Ma, J. (2015). Application of wavelet analysis to spectral data for categorization of lamb muscles. Food Bioproc. Technol. 8(1):1–16.
   Web of Science ®Google Scholar
• Pu, H., Sun, D. W., Ma, J., Liu, D. and Cheng, J. H. (2014). Using wavelet textural features of visible and near infrared hyperspectral image to differentiate between fresh and frozen–thawed pork. Food Bioproc. Technol. 7(11):3088–3099.
   Web of Science ®Google Scholar
• Qiao, J., Ngadi, M. O., Wang, N., Gariépy, C. and Prasher, S. O. (2007a). Pork quality and marbling level assessment using a hyperspectral imaging system. J. Food Eng. 83(1):10–16.
   Web of Science ®Google Scholar
• Qiao, J., Wang, N., Ngadi, M. O., Gunenc, A., Monroy, M., Gariépy, C. and Prasher, S. O. (2007b). Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique. Meat Sci. 76(1):1–8.
   PubMed Web of Science ®Google Scholar
• Qiao, J., Wang, N., Ngadi, M. and Gunenc, A. (2005). Determination of pork quality attributes using hyperspectral imaging technique, In: Optics East, pp. 59960M–59960M-8; International Society for Optics and Photonics.
   Google Scholar
• Qin, J., Burks, T. F., Ritenour, M. A. and Bonn, W. G. (2009). Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. J. Food Eng. 93(2):183–191.
   Web of Science ®Google Scholar
• Ribeiro, F., Tedeschi, L., Stouffer, J. and Carstens, G. (2008). Technical note: A novel technique to assess internal body fat of cattle by using real-time ultrasound. J. Anim. Sci. 86(3):763–767.
   PubMed Web of Science ®Google Scholar
• Romvári, R., Dobrowolski, A., Repa, I., Allen, P., Olsen, E., Szabó, A. and Horn, P. (2006). Development of a computed tomographic calibration method for the determination of lean meat content in pig carcasses. Acta Vet. Hung. 54(1):1–10.
   PubMed Web of Science ®Google Scholar
• Ruiz-Cabrera, M., Gou, P., Foucat, L., Renou, J. and Daudin, J. (2004). Water transfer analysis in pork meat supported by NMR imaging. Meat Sci. 67(1):169–178.
   PubMed Web of Science ®Google Scholar
• Salinas, Y., Ros-Lis, J. V., Vivancos, J. L., Martínez-Máñez, R., Marcos, M. D., Aucejo, S. and Garcia, E. (2014). A novel colorimetric sensor array for monitoring fresh pork sausages spoilage. Food Control. 35(1):166–176.
   Web of Science ®Google Scholar
• Salinas, Y., Ros-Lis, J. V., Vivancos, J. L., Martínez-Máñez, R., Marcos, M. D., Aucejo, S., and Lorente, I. (2012). Monitoring of chicken meat freshness by means of a colorimetric sensor array. Analyst. 137(16):3635–3643.
   Google Scholar
• Sivertsen, A. H., Heia, K., Stormo, S. K., Elvevoll, E. and Nilsen, H. (2011). Automatic nematode detection in cod fillets (Gadus morhua) by transillumination hyperspectral imaging. J. Food Sci. 76(1):S77–S83.
   PubMed Web of Science ®Google Scholar
• Sivertsen, A. H., Heia, K., Hindberg, K. and Godtliebsen, F. (2012). Automatic nematode detection in codfillets (Gadus morhua L.) by hyperspectral imaging. J. Food Eng. 111(4):675–681.
   Web of Science ®Google Scholar
• Schaefer, A., Cook, N., Bench, C., Chabot, J., Colyn, J., Liu, T., Okine, E., Stewart, M. and Webster, J. (2012). The non-invasive and automated detection of bovine respiratory disease onset in receiver calves using infrared thermography. Res. Vet. Sci. 93(2):928–935.
   PubMed Web of Science ®Google Scholar
• Schaefer, A., Cook, N., Tessaro, S., Deregt, D., Desroches, G., Dubeski, P., Tong, A. and Godson, D. (2004). Early detection and prediction of infection using infrared thermography. Can. J. Anim. Sci. 84(1):73–80.
   Web of Science ®Google Scholar
• Segtnan, V. H., Høy, M., Sørheim, O., Kohler, A., Lundby, F., Wold, J. P. and Ofstad, R. (2009). Noncontact salt and fat distributional analysis in salted and smoked salmon fillets using X-ray computed tomography and NIR interactance imaging. J. Agric. Food Chem. 57(5):1705–1710.
   PubMed Web of Science ®Google Scholar
• Shaarani, S. M., Cardenas-Blanco, A., Amin, M., Soon, N. G. and Hall, L. D. (2010). Monitoring development and ripeness of oil palm fruit (Elaeis guneensis) by MRI and bulk NMR. Int. J. Agric. Biol. 12(1):101–105.
   Web of Science ®Google Scholar
• Shaarani, S. M., Nott, K. P. and Hall, L. D. (2006). Combination of NMR and MRI quantitation of moisture and structure changes for convection cooking of fresh chicken meat. Meat Sci. 72(3):398–403.
   PubMed Web of Science ®Google Scholar
• Skjervold, P., Taylor, R., Wold, J., Berge, P., Abouelkaram, S., Culioli, J. and Dufour, E. (2003). Development of intrinsic fluorescent multispectral imagery specific for fat, connective tissue, and myofibers in meat. J. Food Sci. 68(4):1161–1168.
   Web of Science ®Google Scholar
• Sone, I., Olsen, R. L., Sivertsen, A. H., Eilertsen, G. and Heia, K. (2012). Classification of fresh Atlantic salmon (Salmo salar L.) fillets stored under different atmospheres by hyperspectral imaging. J. Food Eng. 109(3):482–489.
   Web of Science ®Google Scholar
• Stewart, M. (2008). Non-invasive measurement of stress and pain in cattle using infrared thermography. Doctoral dissertation, Massey University, Palmerston North, New Zealand.
   Google Scholar
• Singh, C., Jayas, D., Paliwal, J. and White, N. (2009). Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging. J. Stor. Prod. Res. 45(3):151–158.
   Web of Science ®Google Scholar
• Stevik, A. M., Duun, A. S., Rustad, T., O'Farrell, M., Schulerud, H. and Ottestad, S. (2010). Ice fraction assessment by near-infrared spectroscopy enhancing automated superchilling process lines. J. Food Eng. 100(1):169–177.
   Web of Science ®Google Scholar
• Sun, D.-W. and Wang, L. J. (2000). Heat transfer characteristics of cooked meats using different cooling methods. International Journal of Refrigeration-Revue Internationale due Froid 23(7):508–516.
   Web of Science ®Google Scholar
• Tao, F., Peng, Y., Li, Y., Chao, K. and Dhakal, S. (2012a). Simultaneous determination of tenderness and Escherichia coli contamination of pork using hyperspectral scattering technique. Meat Sci. 90(3):851–857.
   PubMed Web of Science ®Google Scholar
• Tao, F., Tang, X., Peng, Y. and Dhakal, S. (2012b). Classification of pork quality characteristics by hyperspectral scattering technique. American Society of Agricultural and Biological Engineers Annual International Meeting, July 29–August 1, 2012, Dallas, Texas, USA. Vol. 7, pp. 5842–5851
   Google Scholar
• Tao, F. F., Wang, W., Li, Y. Y., Peng, Y. K., Wu, J. H., Shan, J. J. and Zhang, L. L. (2010). A rapid nondestructive measurement method for assessing the total plate count on chilled pork surface. Spectr. Spectr. Anal. 30(12):3405–3409.
   Web of Science ®Google Scholar
• Tao, Y., Chen, Z., Jing, H. and Walker, J. (2001). Internal inspection of deboned poultry using X-ray imaging and adaptive thresholding. Trans. ASAE. 44(4):1005–1009.
   Google Scholar
• Tao, Y. and Ibarra, J. (2000). Thickness-compensated X-ray imaging detection of bone fragments in deboned poultry-model analysis. Trans. ASAE. 43(2):453–459.
   Google Scholar
• Toussaint, C., Fauconneau, B., Médale, F., Collewet, G., Akoka, S., Haffray, P. and Davenel, A. (2005). Description of the heterogeneity of lipid distribution in the flesh of brown trout (Salmo trutta) by MR imaging. Aquaculture. 243(1):255–267.
   Web of Science ®Google Scholar
• Valous, N. A., Mendoza, F. and Sun, D.-W. (2010). Emerging non-contact imaging, spectroscopic and colorimetric technologies for quality evaluation and control of hams: a review. Trend. Food Sci. Technol. 21(1):26–43.
   Web of Science ®Google Scholar
• Veberg, A., Sørheim, O., Moan, J., Iani, V., Juzenas, P., Nilsen, A. and Wold, J. (2006). Measurement of lipid oxidation and porphyrins in high oxygen-modified atmosphere and vacuum-packed minced turkey and pork meat by fluorescence spectra and images. Meat Sci. 73(3):511–520.
   PubMed Web of Science ®Google Scholar
• Veliyulin, E. and Aursand, I. G. (2007). 1H and 23Na MRI studies of Atlantic salmon (Salmo salar) and Atlantic cod (Gadus morhua) fillet pieces salted in different brine concentrations. J. Sci. Food Agric. 87(14):2676–2683.
   PubMed Web of Science ®Google Scholar
• Vester-Christensen, M., Erbou, S. G. H., Hansen, M. F., Olsen, E. V., Christensen, L. B., Hviid, M., Ersboll, B. K. and Larsen, R. (2009). Virtual dissection of pig carcasses. Meat Sci. 81(4):699–704.
   PubMed Web of Science ®Google Scholar
• Veliyulin, E., Egelandsdal, B., Marica, F. and Balcom, B. J. (2009). Quantitative 23Na magnetic resonance imaging of model foods. J. Agric. Food Chem. 57(10):4091–4095.
   PubMed Web of Science ®Google Scholar
• Vestergaard, C., Risum, J. and Adler-Nissen, J. (2004). Quantification of salt concentrations in cured pork by computed tomography. Meat Sci. 68(1):107–113.
   PubMed Web of Science ®Google Scholar
• Vestergaard, C., Risum, J. and Adler-Nissen, J. (2005). 23Na-MRI quantification of sodium and water mobility in pork during brine curing. Meat Sci. 69(4):663–672.
   PubMed Web of Science ®Google Scholar
• Wang, W. and Zhang, L.-L. (2010a). A rapid nondestructive measurement method for assessing the total plate count on chilled pork surface. Spectrosc. Spect. Anal. 30(12):3405–3409.
   Web of Science ®Google Scholar
• Wang, L. J. and Sun, D.-W. (2004). Effect of operating conditions of a vacuum cooler on cooling performance for large cooked meat joints. J. Food Engin 61(2):231–240.
   Web of Science ®Google Scholar
• Wang, H. H., and Sun, D.-W. (2002). Melting characteristics of cheese: analysis of effect of cheese dimensions using computer vision techniques. J. Food Engin. 52(3):279–284. Article Number: PII S0260–8774(01)00116–9.
   Web of Science ®Google Scholar
• Wang, W. and Zhang, X.-L. (2010b). Study on modeling method of total viable count of fresh pork meat based on hyperspectral imaging system. Spectrosc. Spect. Anal. 30(2):411–415.
   Web of Science ®Google Scholar
• Wang, W., Peng, Y., Huang, H. and Wu, J. (2011). Application of hyper-spectral imaging technique for the detection of total viable bacteria count in pork. Sensor Lett. 9(3):1024–1030.
   Web of Science ®Google Scholar
• Weschenfelder, A. V., Maldague, X., Rocha, L. M., Schaefer, A. L., Saucier, L. and Faucitano, L. (2013). The use of infrared thermography for pork quality prediction. Meat Sci. 96:120–125.
   Google Scholar
• Windham, W., Smith, D. P., Park, B., Lawrence, K. and Feldner, P. W. (2003). Algorithm development with visible/near-infrared spectra for detection of poultry feces and ingesta. Trans. ASAE. 46(6):1733–1738.
   Google Scholar
• Windham, W., Smith, D., Berrang, M., Lawrence, K. and Feldner, P. (2005). Effectiveness of hyperspectral imaging system for detecting cecal contaminated broiler carcasses. Int. J. Poul. Sci. 4(9):657–662.
   Google Scholar
• Wold, J. P. and Kvaal, K. (2000). Mapping lipid oxidation in chicken meat by multispectral imaging of autofluorescence. Appl. Spectros. 54(6):900–909.
   Web of Science ®Google Scholar
• Wold, J. P., Johansen, I.-R., Haugholt, K. H., Tschudi, J., Thielemann, J., Segtnan, V. H., Narum, B. and Wold, E. (2006). Non-contact transflectance near infrared imaging for representative on-line sampling of dried salted coalfish (bacalao). J. Near Infr. Spectrosc. 14(1):59–66.
   Web of Science ®Google Scholar
• Wu, D. and Sun, D.-W. (2013). Colour measurements by computer vision for food quality control - A review. Trends in Food Sci. and Tech. 29(1):5–20.
   Web of Science ®Google Scholar
• Wu, J., Peng, Y., Chen, J., Wang, W., Gao, X. and Huang, H. (2010). Study of spatially resolved hyperspectral scattering images for assessing beef quality characteristics. Spectrosc. Spect. Anal. 30(7):1815–1819.
   Web of Science ®Google Scholar
• Wu, D., Shi, H., Wang, S. J., He, Y., Bao, Y. D. and Liu, K. S. (2012a). Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system. Anal. Chim. Acta. 726:57–66.
   PubMed Web of Science ®Google Scholar
• Wu, D., Sun, D.-W. and He, Y. (2012b). Application of long-wave near infrared hyperspectral imaging for measurement of color distribution in salmon fillet. Innov. Food Sci. Emerg. Technol. 16:361–372.
   Web of Science ®Google Scholar
• Wu, J., Peng, Y., Li, Y., Wang, W., Chen, J. and Dhakal, S. (2012c) Prediction of beef quality attributes using VIS/NIR hyperspectral scattering imaging technique. J. Food Eng. 109(2):267–273.
   Web of Science ®Google Scholar
• Wu, D. and Sun, D.-W. (2013a). Application of visible and near infrared hyperspectral imaging for non-invasively measuring distribution of water-holding capacity in salmon flesh. Talanta. 116:266–276.
   PubMed Web of Science ®Google Scholar
• Wu, D. and Sun, D.-W. (2013b). Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh. Talanta. 111:39–46.
   PubMed Web of Science ®Google Scholar
• Wu, D., Sun, D. -W. and He, Y. (2014). Novel non-invasive distribution measurement of texture profile analysis (TPA) in salmon fillet by using visible and near infrared hyperspectral imaging. Food Chem. 145:417–426.
   PubMed Web of Science ®Google Scholar
• Xiong, Z., Xie, A., Sun, D.-W., Zeng, X.-a. and Liu, D. (accepted). Applications of hyperspectral imaging in chicken meat safety and quality detection and evaluation: A review. Crit. Rev. Food Sci. Nutr. 55(9):1287–1301.
   Web of Science ®Google Scholar
• Xiong, Z., Sun, D.-W., Zeng, X.-A. and Xie, A. (2014b). Recent developments of hyperspectral imaging systems and their applications in detecting quality attributes of red meats: A review. J. Food Eng. 132:1–13.
   Web of Science ®Google Scholar
• Xiong, Z., Sun, D.-W., Dai, Q., Han, Z., Zeng, X. -A. and Wang, L. (2014c). Application of visible hyperspectral imaging for prediction of springiness of fresh chicken meat. Food Anal. Methods 8(2):380–391.
   Web of Science ®Google Scholar
• Yang, C. C., Chao, K., Kim, M. S., Chan, D. E., Early, H. L. and Bell, M. (2010). Machine vision system for on-line wholesomeness inspection of poultry carcasses. Poul. Sci. 89(6):1252–1264.
   PubMed Web of Science ®Google Scholar
• Youssao, A., Verleyen, V. and Leroy, P. (2002). Prediction of carcass lean content by real-time ultrasound in Pietrain and negative stress Pietrain. Anim. Sci. 75(1):25–32.
   Google Scholar
• Yoon, S. C., Lawrence, K. C., Smith, D. P., Park, B. and Windham, W. R. (2006). Bone fragment detection in chicken breast fillets using diffuse scattering patterns of back-illuminated structured light. In: Optics East, pp. 63810G-63810G-10; International Society for Optics and Photonics. Optics for Natural Resources, Agriculture, and Foods. Yud-Ren Chen, George E. Meyer, and Shu-I Tu. 01, October, Boston, MA, USA.
   Google Scholar
• Yoon, S. C., Lawrence, K. C., Smith, D. P., Park, B. and Windham, W. R. (2008). Embedded bone fragment detection in chicken fillets using transmittance image enhancement and hyperspectral reflectance imaging. Sensor Instrum. Food Qual. Saf. 2(3):197–207.
   Google Scholar
• Zheng, L. Y. and Sun, D-W. (2004). Vacuum cooling for the food industry - a review of recent research advances. Trends in Food Sci. and Tech. 15(12):555–568.
   Web of Science ®Google Scholar
• Zhu, F., Zhang, D., He, Y., Liu, F. and Sun, D.-W. (2013). Application of visible and near infrared hyperspectral imaging to differentiate between fresh and frozen–thawed fish fillets. Food Bioproc. Technol. 6(10):2931–2937.
   Web of Science ®Google Scholar