Nondestructive Spectroscopic and Imaging Techniques for Quality Evaluation and Assessment of Fish and Fish Products

REFERENCES

• Adams, A. and Thompson, K. (1990). Development of an enzyme-linked immunosorbent assay (ELISA) for the detection of Aeromonas salmonicida in fish tissue. J. Aquat. Anim. Health. 2(4):281–288.
   Google Scholar
• Andrés, S., Silva, A., Soares-Pereira, A.L., Martins, C., Bruno-Soares, A.M. and Murray, I. (2008). The use of visible and near infrared reflectance spectroscopy to predict beef M. longissimus thoracis et lumborum quality attributes. Meat Sci. 78:217–224.
   PubMed Web of Science ®Google Scholar
• Arechavala-Lopez, P., Sanchez-Jerez, P., Bayle-Sempere, T.J., Sfakianakis, D.G. and Somarakis, S. (2012). Discriminating farmed gilthead sea bream Sparus aurata and European sea bass Dicentrarchus labrax from wild stocks through scales and otoliths. J. Fish Biol. 80:2159–2175.
   PubMed Web of Science ®Google Scholar
• Ashton, T.J., Michie, I. and Johnston, I.A. (2010). A novel tensile test method to assess texture and gaping in salmon fillets. J. Food Sci. 75(4):S182–S190.
   PubMed Web of Science ®Google Scholar
• Bader, J.A., Shoemaker, C.A. and Klesius, P.H. (2003). Rapid detection of columnaris disease in channel catfish (Ictalurus punctatus) with a new species-specific 16-S rRNA gene-based PCR primer for Flavobacterium columnare. J. Microbiol. Methods 52:209–220.
   PubMed Web of Science ®Google Scholar
• Balaban, M.O., Şengör, G.F. Ü., Soriano, M.G. and Ruiz, E.G. (2010b). Using image analysis to predict the weight of Alaskan salmon of different species. J. Food Sci. 75(3):E157–E162.
   PubMed Web of Science ®Google Scholar
• Balaban, M.O., Şengör, G.F. Ü., Soriano, M.G. and Ruiz, E.G. (2011a). Quantification of gaping, bruising, and blood spots in salmon fillets using image analysis. J. Food Sci. 76:E328–E331.
   PubMed Web of Science ®Google Scholar
• Balaban, M.O., Chombeau, M., Cırban, D. and Gümüş, B. (2010a). Prediction of the weight of Alaskan pollock using image analysis. J. Food Sci. 75(8):E552–E556.
   PubMed Web of Science ®Google Scholar
• Balaban, M.O., Chombeau, M., Gümüş, B. and Cırban, D. (2011b). Determination of volume of Alaska pollock (Theragra chalcogramma) by image analysis. J. Aquat. Food Prod. Technol. 20:45–52.
   Web of Science ®Google Scholar
• Balaban, M.O., Kristinsson, H.G. and Otwell, W.S. (2005) Evaluation of color parameters in a machine vision analysis of carbon monoxide-treated fish. Part I. J. Aquat. Food Prod. Technol. 14:5–24.
   Google Scholar
• Barbin, D.F., ElMasry, G., Sun, D.-W. and Allen, P. (2012). Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging. Anal. Chim. Acta 719:30–42.
   PubMed Web of Science ®Google Scholar
• Bardon, A., Vandeputte, M., Dupont-Nivet, M., Chavanne, H., Haffray, P., Vergnet, A. and Chatain, B. (2009). What is the heritable component of spinal deformities in the European sea bass (Dicentrarchus labrax)?. Aquaculture 294:194–201.
   Web of Science ®Google Scholar
• Bechmann, I.E. and Jørgensen, B.M. (1998). Rapid assessment of quality parameters for frozen cod using near infrared spectroscopy. Lebensm-Wiss Technol. 31:648–652.
   Web of Science ®Google Scholar
• Boglione, P. and Costa, C. (2011). Skeletal deformities and juvenile quality. In: Sparidae: Biology and Aquaculture of Gilthead Sea Bream and Other Species, pp. 233–294. Pavlidis, M. and Mylonas, C., Eds., Wiley-Blackwell, Oxford, UK. ISBN: 978-1-4051-9772-4.
   Google Scholar
• Boglione, P., Costa, C., Di Dato, P., Ferzini, G., Scardi, M. and Cataudella, S. (2003). Skeletal quality assessment of reared and wild sharpsnout sea bream and pandora juveniles. Aquaculture 227:373–394.
   Web of Science ®Google Scholar
• Boglione, P., Gagliardi, F., Scardi, M. and Cataudella, S. (2001). Skeletal descriptors and quality assessment in larvae and post-larvae of wild-caught and hatchery-reared gilthead sea bream (Sparus aurata L. 1758). Aquaculture 192:1–22.
   Web of Science ®Google Scholar
• Borderías, A.J., Gómez-Guillén, M.C., Hurtado, O. and Montero, P. (1999). Use of image analysis to determine fat and connective tissue in salmon muscle. Eur. Food Res. Technol. 209:104–107.
   Web of Science ®Google Scholar
• Boziaris, I.S., Proestos, C., Kapsokefalou, M. and Komaitis, M. (2011). Antimicrobial effect of filipendula ulmaria plant extract against selected eoodborne pathogenic and spoilage bacteria in laboratory media, fish flesh and fish roe product. Food Technol. Biotechnol. 49(2):263–270.
   Web of Science ®Google Scholar
• Brosnan, T. and Sun, D.-W. (2004). Improving quality inspection of food products by computer vision-a review. J. Food Eng. 61:3–16.
   Web of Science ®Google Scholar
• Bublitz, C.G. and Choudhury, G.S. (1992). Effect of light intensity and color on worker productivity and parasite detection efficiency during candling of cod fillets. J. Aquat. Food Prod. Technol. 1(2): 75–89.
   Google Scholar
• Bøknæs, N., Jensen, K.N., Andersen, C.M. and Martens, H. (2002). Freshness assessment of thawed and chilled cod fillets packed in modified atmosphere using near-infrared spectroscopy. Lebensm.-Wiss Technol. 35:628–634.
   Web of Science ®Google Scholar
• Carrillo, J., Koumoundouros, G., Divanach, P. and Martinez, J. (2001). Morphological malformations of the lateral line in reared gilthead sea bream (Sparus aurata L. 1758). Aquaculture 192:281–290.
   Web of Science ®Google Scholar
• Cen, H.Y. and He, Y. (2007). Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends Food Sci. Technol. 18:72–83.
   Web of Science ®Google Scholar
• Corrales, J., Nye, L.B., Baribeau, S., Gassman, N.J. and Schmale, M.C. (2000). Characterization of scale abnormalities in pinfish, Lagodon rhomboides, from Biscayne Bay, Florida. Environ. Biol. Fishes. 57:205220.
   Web of Science ®Google Scholar
• Costa, C., Antonucci, F., Boglione, C., Menesatti, P., Vandeputte, and Chatain, M.B. (2012a). Automated sorting for size, sex and skeletal anomalies of cultured seabass using external shape analysis. Aquacul. Eng. Doi: 10.1016/j.aquaeng.2012.09.001.
   Web of Science ®Google Scholar
• Costa, C., Antonucci, F., Menesatti, P., Pallottino, F., Boglione, C. and Cataudella, S. (2012b). An advanced colour calibration method for fish freshness assessment: A comparison between standard and passive refrigeration modalities. Food Bioprocess. Technol. 6:21902195.
   Web of Science ®Google Scholar
• Costa, C., Antonucci, F., Pallottino, F., Aguzzi, J., Sun, D.-W. and Menesatti, P. (2011b). Shape analysis of agricultural products: A review of recent research advances and potential application to computer vision. Food Bioprocess Tech. 4:673–692.
   Web of Science ®Google Scholar
• Costa, C., D’Andrea, S., Russo, R., Antonucci, F., Pallottino, F. and Menesatti, P. (2011a). Application of non-invasive techniques to differentiate sea bass (Dicentrarchus labrax, L. 1758) quality cultured under different conditions. Aquacul. Int. 19:765–778.
   Web of Science ®Google Scholar
• Cozzolino, D., Kwiatkowski, M.J., Parker, M., Cynkar, W.U., Dambergs, R.G., Gishen, M. and Herderich, M.J. (2010). Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy. Anal. Chim. Acta 513:73–80.
   Google Scholar
• Cozzolino, D., Martins, V. and Murray, I. (2002a). Visible and near infrared spectroscopy of beef longissimus dorsi muscle as a means of discriminating between pasture and corn silage feeding regimes. J. Near Infrared Spec. 10:187–93.
   Web of Science ®Google Scholar
• Cozzolino, D., Murray, I. and Scaife, J.R. (2002b). Near infrared reflectance spectroscopy in the prediction of chemical characteristics of minced raw fish. Aquacul. Nutr. 8:1–6.
   Web of Science ®Google Scholar
• Cui, Z.W., Xu, S.Y. and Sun, D.-W. (2004). Effect of microwave-vacuum drying on the carotenoids retention of carrot slices and chlorophyll retention of chinese chive leaves. Drying Technology. 22(3):563–575.
   Web of Science ®Google Scholar
• Debevere, J. and Boskou, G. (1996). Effect of modified atmosphere packaging on the TVB/TMA-producing microflora of cod fillets. Int. J. Food Microbiol. 31:221–229.
   PubMed Web of Science ®Google Scholar
• Delgado, A.E., Zheng, L.Y. and D.-W. Sun. (2009). Influence of ultrasound on freezing rate of immersion-frozen apples. Food and Bioprocess Technology 2(3):263–270.
   Web of Science ®Google Scholar
• Dissing, B.S., Nielsen, M.E., Ersbøll, B.K. and Frosch, S. (2011). Multispectral imaging for determination of astaxanthin concentration in salmonids. PLoS One. 6(5):1–8.
   Web of Science ®Google Scholar
• Dissing, B.S., Papadopoulou, O.S., Tassou, C., Ersbøll, B.K., Carstensen, J.M., Panagou, E.Z. and Nychas, G.-J. (2012). Using multispectral imaging for spoilage detection of pork meat. Food Bioprocess Tech. 6:22682279.
   Web of Science ®Google Scholar
• Divanach, P., Boglione, C., Menu, B., Koumoundouros, G., Kentouri, M. and Cataudella, S. (1996). Abnormalities in finfish mariculture: An overview of the problem, causes and solutions. In: Seabass and Seabream Culture: Problems and Prospects, pp. 45–66. Chatain, B., Saroglia, M., Sweetman, J. and Lavens, P., Eds., European Aquaculture Society, Oostende, Belgium.
   Google Scholar
• Dixon, B., Kimmins, W. and Pohajdak, B. (1993). Variation in color of pseudoterranova-decipiens (nematoda, anisakidae) larvae correlates with hemoglobin concentration in the pseudocoelomic fluid. Can. J. Fish. Aquat. Sci. 50(4): 767–771.
   Web of Science ®Google Scholar
• Downey, G. (1996). Non-invasive and non-destructive percutaneous analysis of farmed salmon flesh by near infra-red spectroscopy. Food Chem. 55(3):305–311.
   Web of Science ®Google Scholar
• Downey, G., Fouratier, V. and Kelly, J.D. (2003). Detection of honey adulteration by addition of fructose and glucose using near infrared transflectance spectroscopy. J. Near Infrared Spec. 11:447–456.
   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 evaluations. Trends in Food Sci. Technol. 15:230–249.
   Web of Science ®Google Scholar
• Du, C.J. and Sun, D.-W. (2005). Comparison of three methods for classification of pizza topping using different colour space transformations. Journal of Food Engineering 68(3):277–287.
   Web of Science ®Google Scholar
• Ellis, D.I, Broadhurst, D., Kell, D.B, Rowland, J.J, and Goodacre, R. (2002). Rapid and quantitative detection of the microbial spoilage of meat by Fourier transform infrared spectroscopy and machine learning. Appl. Environ. Microbiol. 68:2822–2828.
   PubMed 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. Cri. Rev. Food Sci. Nutr. 52(8):689–711.
   PubMed Web of Science ®Google Scholar
• ElMasry, G., Kamruzzaman, M., Sun, D.-W. and Allen, P. (2012b). Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: A review, Cri. Rev. Food Sci. Nutr. 52(11):999–1023.
   PubMed Web of Science ®Google Scholar
• ElMasry, G. and Sun, D.-W. (2010). Principles of hyperspectral imaging technology. In: Hyperspectral Imaging for Food Quality Analysis and Control, pp. 3–44. Sun, D.-W., Eds., Academic Press/Elsevier, San Diego, California, USA.
   Google Scholar
• ElMasry, G. and Wold, P. (2008). High-speed assessment of fat and water content distribution in fish fillets using online imaging spectroscopy. J. Agric. Food Chem. 56:7672–7677.
   PubMed Web of Science ®Google Scholar
• Erikson, U. and Misimi, E. (2008). Atlantic salmon skin and fillet color changes effected by perimortem handling stress, rigor mortis, and ice storage. J. Food Sci. 73:C50–C59.
   PubMed Web of Science ®Google Scholar
• Erikson, U., Misimi, E. and Gallart-Jornet, L. (2011). Superchilling of rested Atlantic salmon: Different chilling strategies and effects on fish and fillet quality. Food Chem. 127:1427–1437.
   Web of Science ®Google Scholar
• FAO. (2009). Safety of fish and fish products. Available from: http://www.fao.org/fishery/topic/1522/en, last accessed 1 June, 2009.
   Google Scholar
• Fishcler, C. (2002). Food selection and risk perception. In: Food Selection, from Genes to Culture, pp 135–151. Anderson, G.H., Blundell, J. and Chiva, M., Eds., Danone Institution, Paris.
   Google Scholar
• Folkestad, A., Wold, J.P., Rørvik, K.-A., Tschudi, J., Haugholt, K.H., Kolstad, K. and Mørkøre, T. (2008). Rapid and non-invasive measurements of fat and pigment concentrations in live and slaughtered Atlantic salmon (Salmo salar L.). Aquaculture 280:129–135.
   Web of Science ®Google Scholar
• Frid, C.L. J. and Paramor, O.A. L. (2012). Feeding the world: What role for fisheries? ICES J. Marine Sci. 69(2):145–150.
   Web of Science ®Google Scholar
• Georgakopoulou, E., Katharios, P., Divanach, P. and Koumoundouros, G. (2010). Effect of temperature on the development of skeletal deformities in Gilthead seabream (Sparus aurata Linnaeus, 1758). Aquaculture 308:13–19.
   Web of Science ®Google Scholar
• Gram, L. and Dalgaard, P. (2002). Fish spoilage bacteria–Problems and solutions. Curr. Opin. Biotech. 13:262–266.
   PubMed Web of Science ®Google Scholar
• Gümüş, B. and Balaban, M. Ö. (2010). Prediction of the weight of aquacultured rainbow trout (Oncorhynchus mykiss) by image analysis. J. Aquat. Food Prod. Technol. 19:227–237.
   Web of Science ®Google Scholar
• Gümüş, B., Balaban, M.Ö. and Ünlüsayın, M. (2011). Machine vision applications to aquatic foods: A review. Turk. J. Fish. Aquat. Sci. 11:171–181.
   Web of Science ®Google Scholar
• Hammers, M.M., Cavinato, A.G., Mayes, D., Lin, M. and Rasco, B.A. (2007). Non-destructive detection of deep muscle bruising in salmon by near infrared spectroscopy. Eastern Oregon Sci. J. 3:25–29.
   Google Scholar
• Heia, K., Sivertsen, A.H., Stormo, S.K., Elvevoll, E., Wold, J.P. and Nilsen, H. (2007). Detection of nematodes in cod (Gadus morhua) fillets by imaging spectroscopy. J. Food Sci. 72(1):E011E015.
   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. In: International Commission of Agricultural and Biosystems Engineering (CIGR), Valencia, Spain, OR, Paper No. P1214.
   Google Scholar
• He, H.-J., Wu, D. and 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. Innovat. Food Sci. Emer. Technol. 18:237–245.
   Web of Science ®Google Scholar
• He, H.-J., Wu, D. and Sun, D.-W. (2014a). Rapid and non-destructive determination of drip loss and pH distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared (Vis–NIR) hyperspectral imaging. Food Chem. 156:394–401.
   PubMed Web of Science ®Google Scholar
• He, H.-J., Wu, D. and Sun, D.-W. (2014b). 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
• Horváth, K., Seregély, Zs., Andrássy, E., Dalmadi, I. and Farkas, F. (2008). A preliminary study using near infrared spectroscopy to evaluate freshness and detect spoilage in sliced pork meat. Acta Alimentaria. 37(1):93–102.
   Web of Science ®Google Scholar
• Hsieh, C.L., Chang, H.Y., Chen, F.H., Liou, J.H., Chang, S.K. and Lin, T.T. (2011). A simple and effective digital imaging approach for tuna fish length measurement compatible with fishing operations. Comput. Electron. Agric. 75:44–51.
   Web of Science ®Google Scholar
• Huang, Y., Cavinato, A.G., Mayes, D.M., Bledsoe, G.E. and Rasco, B.A. (2002). Nondestructive prediction of moisture and sodium chloride in cold smoked Atlantic salmon (Salmo salar). J. Food Sci. 67:2543–2547.
   Web of Science ®Google Scholar
• Huang, Y., Cavinato, A.G., Mayes, D.M., Kangas, L.J., Bledsoe, G.E. and Rasco, B.A. (2003). Nondestructive determination of moisture and sodium chloride in cured Atlantic salmon (Salmo salar) (Teijin) using short-wavelength near-infrared spectroscopy (SW-NIR). J. Food Sci. 68:482–486.
   Web of Science ®Google Scholar
• Huang, Y., Rogers, T.M., Wenz, M.A., Cavinato, A.G., Mayes, D.M., Bledsoe, G.E. and Rasco, B.A. (2001). Detection of sodium chloride in cured salmon roe by SW-NIR spectroscopy. J. Agric. Food Chem. 49:4161–4167.
   PubMed Web of Science ®Google Scholar
• Hutchings, J. (1999). Food Color and Appearance. Aspen Publishers, Gaithersburg, Maryland.
   Google Scholar
• Hyldig, G. and Nieisen, D. (2001). A review of sensory and instrumental methods used to evaluate the texture of fish muscle. J. Texture Stud. 32:219–242.
   Web of Science ®Google Scholar
• Isaksson, T., Swensen, L.P., Taylor, R.G., Fjæra, S.O. and Skjervold, P.O. (2001). Non-destructive texture analysis of farmed Atlantic salmon using visual/near-infrared reflectance spectroscopy. J. Sci. Food Agric. 82:53–60.
   Web of Science ®Google Scholar
• Isaksson, T., Tøgersen, G., Iversen, A. and Hildrum, K.I. (1995). Non-destructive determination of fat, moisture and protein in salmon fillets by use of near-infrared diffuse spectroscopy. J. Sci. Food Agric. 69:95–100.
   Web of Science ®Google Scholar
• Jackman, P., Sun, D.-W., Du, C.-J. and Allen, P. (2008). Prediction of beef eating quality from colour, marbling and wavelet texture features. Meat Science. 80(4):1273–1281.
   PubMed Web of Science ®Google Scholar
• Jawad, L.A., Ahyong, S.T. and Hosie, A. (2006/2007). Malformation of the lateral line and ambicolouration in the triplefin Grahamina capito (Jenyns, 1842) (Pisces: Tripterygiidae) from New Zealand. Annali del Museo Civico di Storia Naturale di Ferrara. 9/10:89–97.
   Google Scholar
• Jha, S.N. (2010). Near infrared spectroscopy. In: Non-Destructive Evaluation of Food Quality, Chapter 6. Shyam N. Jha, Ed., Springer-Verlag, Berlin.
   Google Scholar
• Katikou, P., Hughes, S.I. and Robb, D.H. F. (2001). Lipid distribution within Atlantic salmon (Salmo salar) fillets. Aquaculture 202(1–2):89–99.
   Web of Science ®Google Scholar
• Khodabux, K., L’Omelette, M.S. S., Jhaumeer-Laulloo, S., Ramasami, P. and Rondeau, P. (2007). Chemical and near-infrared determination of moisture, fat and protein in tuna fishes. Food Chem. 102:669–675.
   Web of Science ®Google Scholar
• Kohler, A., Skaga, A., Hjelme, G. and Skarpeid, H.J. (2002). Sorting salted cod fillets by computer vision: A pilot study. Comput. Electron. Agric. 36:3–16.
   Web of Science ®Google Scholar
• Konophka, I., Kozirok, W. and Rotkiewicz, D. (2004). Lipids and carotenoids of wheat grains and flour and attempt of correlating them with digital image analysis of kernel surface and crosssections. Food Res. Int. 37:429–438.
   Web of Science ®Google Scholar
• Korel, F., Luzuriaga, D. and Balaban, M.O. (2001a). Objective quality assessment of raw tilapia (Oreochromis Niloticus) fillets using electronic nose and machine vision. J. Food Sci. 66(7):1018–1024.
   Web of Science ®Google Scholar
• Korel, F., Luzuriaga, D. and Balaban, M.O. (2001b). Quality evaluation of raw and cooked catfish (Ictalurus punctatus) using electronic nose and machine vision. J. Aquat. Food Prod. Technol. 10(1):3–18.
   Google Scholar
• Koumoundouros, G., Divanach, P. and Kentouri, M. (2001c). The effect of rearing conditions on development of saddleback syndrome and caudal fin deformities in Dentex dentex (L.) Aquaculture 200:285–304.
   Web of Science ®Google Scholar
• Koumoundouros, G., Gagliardi, F., Divanach, P., Boglione, C., Cataudella, S. and Kentouri, M. (1997a). Normal and abnormal osteological development of caudal fin in Sparus aurata L. fry. Aquaculture 149:215–226.
   Web of Science ®Google Scholar
• Koumoundouros, G., Maingot, E., Divanach, P. and Kentouri, M. (2002). Kyphosis in reared sea bass (Dicentrarchus labrax L.): Ontogeny and effects on mortality. Aquaculture 209:49–58.
   Web of Science ®Google Scholar
• Koumoundouros, G., Oran, G., Divanach, P., Stefanakis, S. and Kentouri, M. (1997b). The opercular complex deformity in intensive gilthead sea bream (Sparus aurata L.) larviculture. Moment of apparition and description. Aquaculture 156:165–177.
   Web of Science ®Google Scholar
• Lee, M.H., Cavinato, A.G., Mayes, D.M. and Rasco, B.A. (1992). Noninvasive short-wavelength near-infrared spectroscopic method to estimate the crude lipid content in the muscle of intact rainbow trout. J. Agric. Food Chem. 40:2176–2161.
   Web of Science ®Google Scholar
• Leon, K., Mery, D., Pedreschi, F. and Leon, J. (2006). Color measurement in L*a*b* units from RGB digital images. Food Res. Int. 39:1084–1091.
   Web of Science ®Google Scholar
• Liang, Y.T. and Chiou, Y.C. (2009). Machine vision-based automatic raw fish handling and weighing system of Taiwan Tilapia. Lect. Notes Comput. Sci. 5579:711–720.
   Google Scholar
• Liao, K., Paulsen, M.R. and Reid, J.F. (1994). Real-time detection of colour and surface defects of maize kernels using machine vision. J. Agric. Eng. Res. 59:263–271.
   Web of Science ®Google Scholar
• Lin, M., Al-Holy, M., Mousavi-Hesary, M., Al-Qadiri, H., Cavinato, A.G. and Rasco, B.A. (2004). Rapid and quantitative detection of the microbial spoilage of chicken breasts by diffused reflectance spectroscopy (600–1100 nm). Lett. Appl. Microbiol. 39:148–155.
   PubMed Web of Science ®Google Scholar
• Lin, J. and Brown, C.W. (1992). Near-IR spectroscopic determination of NaCI in aqueous solution. Appl. Spectros. 46(12):1809–1815.
   Web of Science ®Google Scholar
• Lin, M., Cavinato, A.G., Huang, Y. and Rasco, B.A. (2003a). Predicting sodium chloride content in commercial king (Oncorhynchus tshawytscha) and chum (O. keta) hot smoked salmon fillet portions by short-wavelength near-infrared (SW-NIR) spectroscopy. Food Res. Int. 36:761–766.
   Web of Science ®Google Scholar
• Lin, M., Cavinato, A.G., Mayes, D.M., Smiley, S., Huang, Y., Al-Holy, M. and Rasco, B.A. (2003b). Bruise detection in pacific pink salmon (Orncorhynchus gorbuscha) by short-wavelength near-infrared (SW-NIR) spectroscopy and image analysis. J. Agric. Food Chem. 51:6404–6408.
   PubMed Web of Science ®Google Scholar
• Lin, M., Mousavi, M., Al-Holy, M., Cavinato, A.G. and Rasco, B.A. (2006). Rapid near infrared spectroscopic method for the detection of spoilage in rainbow trout (Oncorhynchus mykiss) fillet. J. Food Sci. 71:S18–S23.
   Web of Science ®Google Scholar
• Liu, L. and Ngadi, M.O. (2012). Detecting fertility and early embryo development of chicken eggs using near-Infrared hyperspectral imaging. Food Bioprocess Technol. 6:2503–2513.
   Web of Science ®Google Scholar
• Loy, A., Boglione, C. and Cataudella, S. (1999). Geometric morphometrics and morpho-anatomy: A combined tool in the study of sea bream (Sparus aurata, Sparidae) shape. J. Appl. Ichthyol. 15:104–110.
   Web of Science ®Google Scholar
• Loy, A., Boglione, C., Gagliardi, F., Ferrucci, L. and Cataudella, S. (2000a). Geometric morphometrics and internal anatomy in sea bass shape analysis (Dicentrarchus labrax L., Moronidae). Aquaculture 186:33–44.
   Web of Science ®Google Scholar
• Loy, A., Busilacchi, S., Costa, C., Ferlin, L. and Cataudella, S. (2000b). Comparing geometric morphometrics and outline fitting methods to monitor fish shape variability of Diplodus puntazzo (Teleostea: Sparidae). Aquacul. Eng. 21:271–283.
   Web of Science ®Google Scholar
• Magwaza, L.S., Opara, U.L., Nieuwoudt, H., Cronje, P.J. R., Saeys, W. and Nicolaï, B. (2012). Food Bioprocess Technol. 5:425–444.
   Web of Science ®Google Scholar
• Majolini, D., Trocino, A., Xiccato, G. and Santulli, A. (2009). Near infrared reflectance spectroscopy (NIRS) characterization of European sea bass (Dicentrarchus labrax) from different rearing systems. Italian J. Animal Sci. 8:860–862.
   Web of Science ®Google Scholar
• Marty-Mahé, P., Loisel, P., Fauconneau, B., Haffray, P., Brossard, D. and Davenel, A. (2004). Quality traits of brown trouts (Salmo trutta) cutlets described by automated color image analysis. Aquaculture 232:225–240.
   Web of Science ®Google Scholar
• Mateo, A., Soto, F., Villarejo, J.A., Roca-Dorda, J., Gandara, F.D. and García, A. (2006). Quality analysis of tuna meat using an automated color inspection system. Aquacul. Eng. 35:1–13.
   Web of Science ®Google Scholar
• Mathiassen, J.R., Misimi, E., Toldnes, B., Bondø, M. and Østvik, S.O. (2011). High-speed weight estimation of whole herring (Clupea harengus) using 3D machine vision. J. Food Sci. 76(6):E458–E464.
   PubMed Web of Science ®Google Scholar
• Mathias, J.A., Williams, P.C. and Sobering, D.C. (1987). The determination of lipid and protein in freshwater fish using near-infrared reflectance spectroscopy. Aquaculture 61:303–311.
   Web of Science ®Google Scholar
• Mendoza, F., Valous, N.A., Sun, D.-W, and Allen, P. (2009). Characterization of fat-connective tissue size distribution in pre-sliced pork hams using multifractal analysis. Meat Sci. 83(4):713–722.
   PubMed Web of Science ®Google Scholar
• Menesatti, P., Angelini, C., Pallottino, F., Antonucci, F., Aguzzi, J. and Costa, C. (2012). RGB color calibration for quantitative image analysis: The “3D Thin-Plate Spline” warping approach. Sensors 12:7063–7079.
   PubMed Web of Science ®Google Scholar
• Menesatti, P and Costa, C. (2010). Quality evaluation of fish by hyperspectral imaging. In: Hyperspectral Imaging for Food Quality: Analysis and Control, pp. 273–294. D.-W. Sun, Ed., Academic Press, Elsevier, London, Burlington, San Diego. ISBN: 9780123747532.
   Google Scholar
• Menesatti, P., Zanella, A., D’Andrea, S., Costa, C., Paglia, G. and Pallottino, F. (2009). Supervised multivariate analysis of hyper-spectral NIR images to evaluate the starch index of apples. Food Bioprocess Technol. 2:308–314.
   Web of Science ®Google Scholar
• Michie, I. (2001). Causes of downgrading in the salmon farming industry. In: Farmed Fish Quality, pp. 129–136. Kestin, S.C. and Warris, P.D., Eds., Fishing News Books, Blackwell, Oxford.
   Google Scholar
• Misimi, E., Erikon, U., Diger, H., Skavhaug, A. and Mathiassen, J.R. (2008). Computer vision-based evaluation of preand postrigor changes in size and shape of Atlantic cod (Gadus morhua) and Atlantic salmon (Salmo salar) fillets during rigor mortis and ice storage: Effects of perimortem handling stress. J. Food Sci. 73(2):E57–E68.
   PubMed Web of Science ®Google Scholar
• Misimi, E., Erikson, U. and Skavhaug, A. (2008). Quality grading of Atlantic salmon (Salmo salar) by computer vision. J. Food Sci. 73(5):E211–E217.
   PubMed Web of Science ®Google Scholar
• Nakagawa, H., Sato, M. and Gatlin, D.M. (2007). Dietary Supplements for the Health and Quality of Cultured Fish. Cromwell Press, Trowbridge, UK.
   Google Scholar
• Nilsen, H. and Esaiassen, M. (2005). Predictingsensory score of cod (Gadus morhua) from visible spectroscopy. Lebensm.-Wiss Technol. 38:95–99.
   Web of Science ®Google Scholar
• Nilsen, H., Esaiassen, M., Heia, K. and Sigernes, F. (2002). Visible/near-infrared spectroscopy: A new tool for the evaluation of fish freshness. J. Food Sci. 67(5):1821–1826.
   Web of Science ®Google Scholar
• Nortvedt, R., Torrissen, O.J. and Tuene, S. (1998). Application of near-infrared transmittance spectroscopy in the determination of fat, protein and dry matter in Atlantic halibut fillet. Chemometr. Intell. Lab. Syst. 42:199–207.
   Web of Science ®Google Scholar
• Odone, F., Trucco, E. and Verri, A. (1998). Visual learning of weight from shape using support vector machines. In: Proceeding of British Machine Vision Conference, pp. 469–477, Southampton.
   Google Scholar
• OECD/FAO. (2011). OECD-FAO Agricultural Outlook 2011–2020, OECD Publishing and FAO. Available from http://dx.doi.org/10.1787/agr_outlook-2011-en.
   Google Scholar
• Olafsdottir, G., Nesvadba, P., Natale, C.D., Oehlenschläger, J., Tryggvadóttir, S.V., Schubring, R., Kroeger, M., Heia, K., Esaiassen, M., Macagnano, A. and Jøgensen, B.M. (2004). Multisensor for fish quality determination. Trends Food Sci. Technol. 15:86–93.
   Web of Science ®Google Scholar
• Oliveira, A.C. M. and Balaban, M.O. (2006). Comparison of a colorimeter with a machine vision system in measuring color of Gulf of Mexico sturgeon fillets. Appl. Eng. Agric. 22(4):583–587.
   Web of Science ®Google Scholar
• Osborne, B.G. (2000). Near-infrared spectroscopy in food analysis. In: Encyclopedia of Analytical Chemistry, pp. 4069–4082. Meyers, R.A., Eds., John Wiley and Sons Ltd, Chichester, UK.
   Google Scholar
• Ottavian, M., Facco, P., Fasolato, L., Novelli, E., Mirisola, M., Perini, M. and Barolo, M. (2012). Use of near-infrared spectroscopy for fast fraud detection in seafood: Application to the authentication of wild European Sea Bass (Dicentrarchus labrax). J. Agric. Food Chem. 60:639–648.
   PubMed Web of Science ®Google Scholar
• Pallottino, F., Menesatti, P., Costa, C., Paglia, G., De Salvador, F.R. and Lolletti, D. (2010). Image analysis techniques for automated hazelnut peeling determination. Food Bioprocess Technol. 3:155–159.
   Web of Science ®Google Scholar
• Patel, K.K., Kar, A., Jha, S.N. and Khan, M.A. (2012). Machine vision system: A tool for quality inspection of food and agricultural products. J. Food Sci. Technol. 49(2): 123–141.
   PubMed Web of Science ®Google Scholar
• Peturson, J. (1991). Optical spectra of fish flesh and quality defects. In: Fish Quality Control by Computer Vision, pp. 45–76., Pau, L.F. and Olafsson, R., Eds., Marcel Dekker, New York.
   Google Scholar
• Quevedo, R.A., Aguilera, J.M. and Pedreschi, F. (2010). Color of salmon fillets by computer vision and sensory panel. Food Bioprocess Technol. 3(5):637–643.
   Web of Science ®Google Scholar
• Rajkumar, P., Wang, N., ElMasry, G., Raghavan, G.S. V. and Gariepy, Y. (2012). Studies on banana fruit quality and maturity stages using hyperspectral imaging. J. Food Eng. 108:194–200.
   Web of Science ®Google Scholar
• Rasco, B.A., Miller, C.E. and King, T.L. (1991). Utilization of NIR spectroscopy to estimate the proximate composition of trout muscle with minimal sample pretreatment. J. Agric. Food Chem. 39:67–72.
   Web of Science ®Google Scholar
• Rønsholdt, B., Nielsen, H., Færgemand, J. and McLean, E. (2000). Evaluation of image analysis as a method for examining carcass composition of rainbow trout. Ribarstvo. 58(1): 3–11.
   Google Scholar
• Sándor, Z., Papp, Z.G., Csengeri, I. and Jeney, Z. (2011). Fish meat quality and safety. Tehnologija Mesa. 52:97–105.
   Google Scholar
• Segtnan, V.H., Høy, M., Lundby, F., Narum, B. and Wold, J.P. (2009a). Fat distribution analysis in salmon using non-contact near infrared interactance imaging: A sampling and calibration strategy. J. Near Infrared Spec. 17:247–253.
   Web of Science ®Google Scholar
• Segtnan, V.H., Høy, M., Sorheim, O., Kohler, A., Lundby, F., Wold, J.P. and Ofstad, R. (2009b). 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
• Sfakianakis, D.G., Georgakopoulou, E., Kentouri, M. and Koumoundouros, G. (2006b). Geometric quantification of lordosis effects on body shape in European sea bass, Dicentrarchus labrax (Linnaeus, 1758). Aquaculture 256:27–33.
   Web of Science ®Google Scholar
• Sfakianakis, D.G., Georgakopoulou, E., Papadakis, I.E., Divanach, P., Kentouri, M. and Koumoundouros, G. (2006a). Environmental determinants of haemal lordosis in European sea bass, Dicentrarchus labrax (Linnaeus, 1758). Aquaculture 254:54–64.
   Web of Science ®Google Scholar
• Sfakianakis, D.G., Koumoundouros, G., Anezaki, L.G., Divanach, P. and Kentouri, M. (2003). Development of a saddleback-like syndrome in reared white seabream Diplodus sargus (Linnaeus, 1758). Aquaculture 217:673–676.
   Web of Science ®Google Scholar
• Sfakianakis, D.G., Koumoundouros, G., Divanach, P. and Kentouri, M. (2004). Osteological development of the vertebral column and of the fins in Pagellus erythrinus (L. 1758). Temperature effect on the developmental plasticity and morpho-anatomical abnormalities. Aquaculture 232:407–424.
   Web of Science ®Google Scholar
• Shahidi, F. (2009). Maximising the value of marine by-products: An overview. In: Maximising the Value of Marine By-Products, pp. xxi–xxv. Shahidi, F., Eds., Woodhead Publishing, Cambridge, UK.
   Google Scholar
• Shimamoto, J., Hiratsuka, S., Hasegawa, K., Sato, M. and Kawano, S. (2003). Rapid non-destructive determination of fat content in frozen skipjack using a portable near infrared spectrophotometer. Fish. Sci. 69:856–860.
   Web of Science ®Google Scholar
• Shimasaki, T., Miake, K., Tsukamasa, Y., Sugiyama, M., Minegishi, Y. and Shinano, H. (1994). Effect of water activity and storage temparature on the quality and microflora of smoked salmon. Nippon Suisan Gakk. 60(5):569–576.
   Web of Science ®Google Scholar
• Shiroma, C. and Rodriguez-Saona, L. (2009). Application of NIR and MIR spectroscopy in quality control of potato chips. J.Food Compos. Anal. 22:596–605.
   Web of Science ®Google Scholar
• Sigurdsson, G.J. (1947). Comparison of chemical tests of the quality of fish. Anal. Chem. 19:892–902.
   Web of Science ®Google Scholar
• Sigurgisladottira, S., ØTorrissenb, O., Liec, Ø., Thomassend, M. and Hafsteinssone, H. (1997). Salmon quality: Methods to determine the quality parameters. Rev. Fish. Sci. 5(3):223–252.
   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
• Solberg, C. (1995). Use of near infrared transmittance in quality analysis of fish. In: Near Infrared Spectroscopy: The Future Waves, pp. 591–596. Williams, P.C. and Davies, A.M. C., Eds., NIR Publications, Chichester, UK.
   Google Scholar
• Solberg, C., Saugen, E., Swenson, L.-P., Bruun, L. and Isaksson, T. (2003). Determination of fat in live farmed Atlantic salmon using non-invasive NIR techniques. J. Sci. Food Agric. 83:692–696.
   Web of Science ®Google Scholar
• Sollid, H. and Solberg, C. (1992). Salmon fat content estimation by near infrared transmission spectroscopy. J. Food Sci. 57(3):792–793.
   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. 109:482–489.
   Web of Science ®Google Scholar
• Stien, L.H., Kiessling, A. and Manne, F. (2007). Rapid estimation of fat content in salmon fillets by colour image analysis. J. Food Compos. Anal. 20:73–79.
   Web of Science ®Google Scholar
• Stien, L.H., Manne, F., Ruohonene, K., Kause, A., Rungruangsak-Torrissen, K. and Kiessling, A. (2006a). Automated image analysis as a tool to quantify the colour and composition of rainbow trout (Oncorhynchus mykiss W.) cutlets. Aquaculture 261:695–705.
   Web of Science ®Google Scholar
• Stien, L.H., Suontama, J. and Kiessling, A. (2006b). Image analysis as a tool to quantify rigor contraction in pre-rigor-filleted fillets. Comput. Electron. Agric. 50:109–120.
   Web of Science ®Google Scholar
• Storbeck, F. and Daan, B. (2001). Fish species recognition using computer vision and a neural network. Fish. Res. 51(1
   Web of Science ®Google Scholar
• Stormo, S.K, Ernstsen, A., Nilsen, H., Heia, K., Sivertsen, A.H. and Elvevoll, E. (2004). Compounds of parasitic roundworm absorbing in the visible region: Target molecules for detection of roundworm in atlantic cod. J. Food Protect. 67:1522–1525.
   PubMed Web of Science ®Google Scholar
• Sullivan, J.D., Ellis, P.C., Lee, R.G., Combs, W.S. and Watson, S.W. (1983). Comparison of the Limulus amoebocyte lysate test with plate counts and chemical analyses for assessment of the quality of lean fish. Appl. Environ. Microbiol. 45(2):720–722.
   PubMed Web of Science ®Google Scholar
• Sun, D.-W. (1999). Comparison and selection of EMC ERH isotherm equations for rice. Journal of Stored Products Research 35(3):249–264.
   Web of Science ®Google Scholar
• Sun, D.-W. (2000). Inspecting pizza topping percentage and distribution by a computer vision method. J. Food Eng. 44(4):245–249.
   Web of Science ®Google Scholar
• Sun, D.-W. (2008). Computer Vision Technology for Food Quality Evaluation. Academic Press/Elsevier, San Diego, California.
   Google Scholar
• Sun, D.-W. (2010). Hyperspectral Imaging for Food Quality Analysis and Control. Academic Press/Elsevier, San Diego, California.
   Google Scholar
• Sun, D.-W. and Brosnan, T. (1999). Extension of the vase life of cut daffodil flowers by rapid vacuum cooling. International Journal of Refrigeration-Revue Internationale du Froid. 22(6):472–478.
   Web of Science ®Google Scholar
• Sun, D.-W. and Byrne, C. (1998). Selection of EMC/ERH isotherm equations for rapeseed. Journal of Agricultural Engineering Research. 69(4):307–315.
   Web of Science ®Google Scholar
• Sun, D.-W. and Hu, Z.H. (2003). CFD simulation of coupled heat and mass transfer through porous foods during vacuum cooling process. International Journal of Refrigeration-Revue Internationale du Froid. 26(1):19–27.
   Web of Science ®Google Scholar
• Sun, D.-W. and Woods, J.L. (1993). The moisture-content relative-humidity equilibrium relationship of wheat: A review. Drying Technology. 11(7):1523–1551.
   Web of Science ®Google Scholar
• Sun, D.-W. and Woods, J.L. (1994a). Low-temperature moisture transfer characteristics of wheat in thin-layers. Transactions of the ASAE. 37(6):1919–1926.
   Google Scholar
• Sun, D.-W. and Woods, J.L. (1994b). Low-temperature moisture transfer characteristics of barley: Thin-layer models and equilibrium isotherms. J. Agricultural Engineering Research. 59(4):273–283.
   Web of Science ®Google Scholar
• Sun, D.-W. and Woods, J.L. (1997). Simulation of the heat and moisture transfer process during drying in deep grain beds. Drying Technology 15(10):2479–2508.
   Web of Science ®Google Scholar
• Sun, D.-W. and Zheng, L.Y. (2006). Vacuum cooling technology for the agri-food industry: Past, present and future. J. Food Engin. 77(2):203–214.
   Web of Science ®Google Scholar
• Svellingen, C., Totland, B., White, D. and Øvredal, J.T. (2006). Automatic species recognition, length measurement and weight determination, using the CatchMeter computer vision system. In: International Council for Exploration of the Sea (ICES)–Theme Session on Technologies for Monitoring Fishing Activities and Observing Catch, Maastricht, Netherlands, OR, Paper Code N:03.
   Google Scholar
• Uddin, M. and Okazaki, E. (2004). Classification of fresh and frozen-thawed fish by nearinfrared spectroscopy. J. Food Sci. 69:C665–C668.
   Web of Science ®Google Scholar
• Uddin, M., Okazaki, E., Turza, S., Yumiko, Y., Tanaka, M. and Fukuda, Y. (2005). Non-destructive Visible/NIR spectroscopy for differentiation of fresh and frozen-thawed fish. J. Food Sci. 70:C506–C510.
   Web of Science ®Google Scholar
• Valdes, E.V., Atkinson, J.L., Hilton, J.W. and Leeson, S. (1989). Near infrared reflectance analysis of fat, protein and gross energy of chicken and rainbow trout carcass. Canadian J. Animal Sci. 69:1087–1090.
   Web of Science ®Google Scholar
• Valous, N.A., Mendoza, F., Sun, D.-W. and Allen, P. (2009). Colour calibration of a laboratory computer vision system for quality evaluation of pre-sliced hams. Meat Science 81(1):132–141.
   PubMed Web of Science ®Google Scholar
• Veliyulin, E., Misimi, E., Bondø, M., Vebenstad, P.A. and Østvik, S.O. (2011). A simple method for weight estimation of whole herring (Clupea harengus) using planar x-ray imaging. J. Food Sci. 76:E291–E297.
   PubMed Web of Science ®Google Scholar
• Wang, L.J. and Sun, D.-W. (2001). Rapid cooling of porous and moisture foods by using vacuum cooling technology. Trends in Food Sci. & Tech. 12(5-6):174–184.
   Web of Science ®Google Scholar
• White, D.J., Svellingen, C. and Strachan, N.J. C. (2006). Automated measurement of species and length of fish by computer vision. Fish. Res. 80(1):203–210
   Web of Science ®Google Scholar
• Wold, J.P. and Isaksson, T. (1997). Non-destructive determination of fat and moisture in whole atlantic salmon by near-infrared diffuse spectroscopy. J. Food Sci. 62(4):734–736.
   Web of Science ®Google Scholar
• Wold, J.P., Jakobsen, T. and Krane, L. (1996). Atlantic salmon average fat content estimated by near-infrared transmittance spectroscopy. J. Food Sci. 61(1):74–77.
   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 Infrared Spec. 14:59–66.
   Web of Science ®Google Scholar
• Wold, J.P., Westad, F. and Heia, K. (2001). Detection of parasites in cod fillets by using SIMCA classification in multispectral images in the visible and NIR region. Appl. Spectros. 55:1025–34.
   Web of Science ®Google Scholar
• Woodcock, T., Fagan, C.C., O’Donnell, C.P. and Downey, G. (2008). Application of near and mid-Iinfrared spectroscopy to determine cheese quality and authenticity. Food Bioprocess Technol. 1:117–129.
   Web of Science ®Google Scholar
• Wu, D., Chen, X.J., Shi, P.Y., Wang, S.H., Feng, F.Q. and He, Y. (2009). Determination of alpha-linolenic acid and linoleic acid in edible oils using near-infrared spectroscopy improved by wavelet transform and uninformative variable elimination. Anal. Chim. Acta 634(2):166–171.
   PubMed Web of Science ®Google Scholar
• Wu, D., He, Y., Nie, P.C., Cao, F. and Bao, Y.D. (2010). Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice. Anal. Chim. Acta 659(1–2):229–237.
   PubMed Web of Science ®Google Scholar
• Wu, D., He, H.-J. and Sun, D.-W. (2012e). Non-destructive texture analysis of farmed salmon using hyperspectral imaging technique. In: International Commission of Agricultural and Biosystems Engineering (CIGR), Valencia, Spain, OR, Paper No. C1123.
   Google Scholar
• Wu, D., Nie, P.C., He, Y. and Bao, Y.D. (2012a). Determination of calcium content in powdered milk using near and mid-infrared spectroscopy with variable selection and chemometrics. Food Bioprocess Technol. 5(4):1402–1410.
   Web of Science ®Google Scholar
• Wu, D., Shi, H., Wang, S., He, Y., Bao, Y. and Liu, K. (2012c). 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. and Sun, D.-W. (2013). Colour measurements by computer vision for food quality control–A review. Trends Food Sci. Technol. 29:5–20.
   Web of Science ®Google Scholar
• Wu, D., Sun, D.-W. and He, Y. (2012d). Application of long-wave near infrared hyperspectral imaging for measurement of colour distribution in salmon fillet. Innovat. Food Sci. Emer. Technol. 16:361–372.
   Web of Science ®Google Scholar
• Wu, D., Wang, S., Wang, N., Nie, P., He, Y., Sun, D.-W. and Yao, J. (2012b). Application of time series-hyperspectral imaging (TS-HSI) for determining water distribution within beef and spectral kinetic analysis during dehydration. Food Bioprocess Technol. 6:2943–2958.
   Web of Science ®Google Scholar
• Xiccato, G., Trocino, A., Tulli, F. and Tibaldi, E. (2004). Prediction of chemical composition and origin identification of European sea bass (Dicentrarchus labrax L.) by near infrared reflectance spectroscopy (NIRS). Food Chem. 86:275–281.
   Web of Science ®Google Scholar
• Xu, S.Y., Chen, X.F. and Sun, D.W.- (2001). Preservation of kiwifruit coated with an edible film at ambient temperature. Journal of Food Engineering 50(4):211–216.
   Web of Science ®Google Scholar
• Yagiz, Y., Balaban, M.O., Kristinsson, H.G., Welt, B.A. and Marshall, M.R. (2009). Comparison of Minolta colorimeter and machine vision system in measuring colour of irradiated Atlantic salmon. J. Sci. Food Agric. 89:728–730.
   Web of Science ®Google Scholar
• Zheng, C. and Sun, D.-W. (2006). Recent developments and applications of image features for food quality evaluation and inspection–A review. Trends Food Sci. Technol. 17(12):642–655.
   Web of Science ®Google Scholar
• Zheng, C., Sun, D.-W. and Du, C.-J. (2006). Estimating shrinkage of large cooked beef joints during air-blast cooling by computer vision. J. Food Eng. 72:56–62.
   Web of Science ®Google Scholar
• Zheng, L.Y. and Sun, D.-W. (2006). Innovative applications of power ultrasound during food freezing processes - A review. Trends in Food Science & Technology, 17(1):16–23.
   Web of Science ®Google Scholar
• Zhou, T., Harrison, A.D., McKellar, R., Young, J.C., Odumeru, J., Piyasena, P., Lu, X., Mercer, D.G. and Karr, S. (2004). Determination of acceptability and shelf life of ready-to-use lettuce by digital image analysis. Food Res. Int. 37:875–881.
   Web of Science ®Google Scholar
• Zhu, F., Zhang, D., He, Y., Liu, F. and Sun, D.-W. (2012). Application of visible and near infrared hyperspectral imaging to differentiate between fresh and frozen–thawed fish fillets. Food Bioprocess Technol. 6:2931–2937.
   Web of Science ®Google Scholar
• Zion, B., Alchanatis, V., Ostrovsky, V., Barki, A. and Karplus, I. (2007). Real-time underwater edible fish species sorting. Comput. Electron. Agric. 56:34–45.
   Web of Science ®Google Scholar
• Zion, B., Alchanatis, V., Ostrovsky, V., Barki, A. and Karplus, I. (2008). Classification of guppies’ (Poecilia reticulata) gender by computer vision. Aquacul. Eng. 38:97–104.
   Web of Science ®Google Scholar
• Zion, B., Shklyar, A. and Karplus, I. (1999). Sorting fish by computer vision. Comput. Electron. Agric. 23:175–187.
   Web of Science ®Google Scholar
• Zion, B., Shklyar, A. and Karplus, I. (2000). In-vivo fish sorting by computer vision. Aquacul. Eng. 22:165–179.
   Web of Science ®Google Scholar