Machine vision applications to aquatic foods: a review

Bilgisayarlı resim analizi (BRA); hızlı, ekonomik, tutarlı ve objektif olarak kontrol etme ve değerlendirme metodudur. Ürüne zarar vermeyen bu metodun, su ürünleri endüstrisine uygulamaları bulunmaktadır. BRA‟nın otomatik porsiyonlama gibi, çoğu fonksiyonu veya ürünün türe, ağırlığa ve görsel kalite özelliklerine göre sınıflandırması su ürünleri işlemesinde, hızlı bir şekilde uygulanabilir. Bu derlemede, BRA sisteminin çalışma biçimi ve parçaları, kısaca gıdalara uygulanması, avantajları ve dezavantajları açıklanmaktadır. Su ürünlerine BRA uygulamalarının kaynakçaları; su ürünleri kompozisyonun belirlenmesi, ağırlık ve hacimin değerlendirmesi, şekil özelliklerinin ölçülmesi, su ürünlerinin et ya da yüzey renginin tanımlanması ve kalite değerlendirmesi sırasında istenmeyen kusurların belirlenmesi şeklindeki başlıklar altında gruplandırılmıştır. Sonuç olarak; gelecek için umut verici bu teknolojinin, endüstriyel uygulamalardaki bazı örnekleri verilmektedir. Bu konular derlemede kapsamlı kaynakça ile belirtilmektedir.

Bilgisayarlı resim analizinin su ürünlerine uygulanması: bir derleme

Machine vision (MV) is a rapid, economic, consistent and objective inspection and evaluation technique. This non- destructive method has applications in the aquatic food industry. MV can perform many functions at once in an aquatic food processing line: sorting by species, by size, and by visual quality attributes, as well as automated portioning. In this review, the mode of operation and the components of a MV system are introduced, its applications to foods are briefly discussed, and the advantages and disadvantages listed. The literature in the MV applications to aquatic foods is grouped under the following topics: determination of composition, measurement and evaluation of size and volume, measurement of shape parameters, quantification of the outside or meat color of aquatic foods, and detection of defects during quality evaluation. Finally, brief examples from the industrial applications of this promising technology are given. Extensive bibliography is cited in this field.

___

  • Abdullah, Z.M., Aziz, A.S. and Dos-Mohamed, A.M. 2000. Quality inspection of bakery products using a colour- based machine vision system. Journal of Food Quality, 23(1): 39-50.
  • Andreadis, I. 1999. Modelling and evaluating colour information for robot vision. Mechatronics, 9: 429- 446.
  • Arnarson, H., Bengoetxea, K. and Pau, L.F. 1988. Vision applications in the fishing and fish product industries. International Journal of Pattern Recognition and Artificial Intelligence, 2: 657-671.
  • Arnarson, H. 1991. Fish and fish product sorting. In: L.F. Pau., R. Olafsson (Eds.), In Fish quality control by machine vision, Marcel Dekker, New York: 245-261.
  • Arnarson, H. and Pau, L.F. 1994. PDL-HM: morphological and syntactic shape classification algorithm-real-time application to fish species classification. Machine Vision and Applications, 7(2): 59-68.
  • Bachelor, B.G. 1985. Lighting and viewing techniques in automated visual inspection. IFS Publication Ltd., Bedford, UK.
  • Balaban, M.O., Yeralan, S. and Bergmann, Y. 1994. Determination of count and uniformity ratio of shrimp by machine vision. Journal of Aquatic Food Product Technology, 3: 43-58.
  • 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 1: Fresh tuna. Journal of Aquatic Food Product Technology, 14: 5-24.
  • Balaban, M.O. and Odabaşı, A.Z. 2006. Measuring color with machine vision. Food Technology, 60: 32-36.
  • Balaban, M.O. 2008. Quantifying nonhomogeneous colors in agricultural materials. Part I: Method development. Journal of Food Science, 73: 431-437.
  • Balaban, M.O., Odabaşı, A.Z., Damar, S. and Oliveira, A.C.M. 2008a. Quality evaluation of seafood. In: D.W. Sun (Ed.), Computer Vision Technology for Food Quality Evaluation, Academic Press, Burlington: 189-209.
  • Balaban, M.O., Aparicio, J., Zotarelli, M. and Sims, C. 2008b. Quantifying nonhomogeneous colors in agricultural materials. Part II: Comparison of machine vision and sensory panel evaluations. Journal of Food Science, 73: 438-442.
  • Balaban, M.O., Ünal-Şengör, G.F., Gil-Soriano, M. and Guillén-Ruiz, E. 2010a. Using image analysis to predict the weight of Alaskan salmon of different species. Journal of Food Science, 75(3): 157-162.
  • Balaban, M.O., Chombeau, M., Cırban, D. and Gümüş, B. 2010b. Prediction of the weight of Alaskan pollock using image analysis. Journal of Food Science, 75 (8): 552-556.
  • Balaban, M.O., Chombeau, M., Gümüş, B. and Cırban, D. 2011. Determination of volume of Alaska pollock (Theragra chalcogramma) by image analysis. Journal of Aquatic Food Product Technology, 20: 45-52.
  • Barni, M., Cappellini, V. and Mecocci, A. 1997. Colour based detection of defects on chicken meat. Image and Vision Computing, 15: 549-556.
  • Borderías, A.J., Gomez-Guillen, M.C. and Hurtado, O. 1999. Use of image analysis to determine fat and connective tissue in salmon muscle. European Food Research Technology, 209: 104-107.
  • Brosnan, T. and Sun, D-W. 2002. Inspection and grading of agricultural and food products by computer vision systems-a review. Computers and Electronics in Agriculture, 36: 193-213.
  • Brosnan, T. and Sun, D-W. 2004. Improving quality inspection of food products by computer vision-a review. Journal of Food Engineering, 61: 3-16.
  • Buckingham, R. and Davey, P. 1995. This robot‟s gone fishing. Industrial Robot, 22(5): 12-14.
  • Chen, K., Sun, X., Qin, C. and Tang, X. 2010. Color grading of beef fat by using computer vision and support vector machine. Computers and Electronics in Agriculture, 70: 27-32.
  • Chombeau, M., Gümüş, B., Cırban, D. and Balaban, M.O. 2010b. Quality grading of Alaska Pollock (Theragra chalcogramma) roe by image analysis. Second International Congress on Seafood Technology, May 10-13, Book of abstracts, Anchorage, Alaska: 31.
  • Clausen, S., Greiner, K., Andersen, O., Lie, K-A., Schulerud, H. and Kavli, T. 2007. Automatic segmentation of overlapping fish using shape priors. In: B.K. Ersbøll and K.S. Pedersen (Eds.), SCIA, LNCS: 11-20.
  • Croft, E.A., de Silva, C.W. and Kurnianto, S. 1996. Sensor Technology integration in an intelligent machine for herring roe grading. IEEE/ASME Transactions on Mechatronics, 1(3): 204-215.
  • Damar, S., Yağız, Y., Balaban, M.O., Ural, S., Oliveira, A.C.M. and Crapo, C.A. 2006. Prediction of oyster volume and weight using machine vision. Journal of Aquatic Food Product Technology, 15: 3-15.
  • Du, C-J. and Sun, D-W. 2004. Recent developments in the applications of image processing techniques for food quality evaluation. Trends in Food Science and Technology, 15: 230-249.
  • Du, C-J. and Sun, D-W. 2006. Learning techniques used in computer vision for food quality evaluation: a review. Journal of Food Engineering, 72: 39-55.
  • Erdem, O.A., Balaban, M.O., Crapo, C. and Oliveira, A. 2009. Quantification of gaping in fish fillets using image analysis. Institute of Food Technologists Annual Meeting, June 6-9, Anaheim, California: 91.
  • Erikson, U. and Misimi, E. 2008. Atlantic salmon skin and fillet color changes effected by peri-mortem handling stress, rigor mortis, and ice storage. Journal of Food Science, 73: 50-59.
  • 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.
  • Francis, F.J. 1991. Color measurement interpretation. In: D.Y.C. Fung, R.F. Mathews (Eds.), Instrumental methods for quality assurance in foods, ASQC Quality Press, Marcel Dekker Inc, New York: 189- 210.
  • Gerrard, D.E., Gao, X. and Tan, J. 1996. Beef marbling and colour score determination by image processing. Journal of Food Science, 61(1): 145-148.
  • Gormley, T.R. 1992. A note on consumer preference of smoked salmon color. Irish Journal of Agricultural and Food Research, 31: 199-202.
  • Gunasekaran, S. 1996. Computer vision technology for food quality assurance. Trends in Food Science and Technology, 7(8): 245-256.
  • Gunasekaran, S. 2001. Non-destructive Food Evaluation Techniques to Analyse Properties and Quality. Food Science and Technology Series. Marcel Dekker, New York, 105 pp.
  • Gümüş, B. and Balaban, M.O. 2010. Prediction of the weight of aquacultured rainbow trout (Oncorhynchus mykiss) by image analysis. Journal of Aquatic Food Product Technology, 19: 227-237.
  • 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. Journal of Food Science, 72(1): 11-15.
  • Heinemann, P.H., Hughes, R., Morrow, C.T., Sommer, H.J., Beelman, R.B. and Wuest, P.J. 1994. Grading of mushrooms using a machine vision system. Transactions of the ASAE, 37(5): 1671-1677.
  • Heinemann, P.H., Varghese, Z.A., Morrow, C.T., Sommer, H.J. and Crassweller, R.M. 1995. Machine vision inspection of Golden Delicious apples. Applied Engineering in Agriculture Transactions of the ASAE, 11(6): 901-906.
  • 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: 751-763.
  • Jamieson, V. 2002. Physics raises food standards. Physics World, 1: 21-22.
  • Kassler, M., Corke, P. and Wong, P. 1993. Automatic grading and packing of prawns. Computers and Electronics in Agriculture, 9: 319-333.
  • Kohler, A., Skaga, A., Hjelme, G. and Skarpeid, H.J. 2002. Sorting salted cod fillets by computer vision: a pilot study. Computers and Electronics in Agriculture, 36: 3-16.
  • Kong, F., Tang, J., Rasco, B., Crapo, C. and Smiley, S. 2007. Quality changes of salmon (Oncorhynchus gorbuscha) muscle during thermal processing. Journal of Food Science, 72: 103-111.
  • Kong, F., Tang, J., Lin, M. and Rasco, B. 2008. Thermal effects on chicken and salmon muscles: Tenderness, cook loss, area shrinkage, collagen solubility and microstructure. LWT-Food Science and Technology, 41: 1210-1222.
  • Korel, F., Luzuriaga, D. and Balaban, M.O. 2001a. Objective quality assessment of raw tilapia (Oreochromis Niloticus) fillets using electronic nose and machine vision. Journal of Food Science, 66(7): 1018-1024.
  • 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. of Aquatic Food Product Technology, 10(1): 3-18.
  • Korel, F. and Balaban, M.O. 2010. Quality assessment of aquatic foods by machine vision, electronic nose, and electronic tongue. In: C. Alasalvar, K. Miyashita, F. Shahidi, U. Wanasundara (Eds.), Seafood quality, safety and health effects, Blackwell Publishing, Oxford: 68-81
  • Köse, S., Balaban, M.O., Boran, M. and Boran, G. 2009. The effect of mincing method on the quality of refrigerated whiting burgers. International Journal of Food Science and Technology, 44: 1649-1660.
  • Krutz, G.W., Gibson, H.G., Cassens, D.L. and Zhang, M. 2000. Colour vision in forest and wood engineering. Landwards, 55: 2-9.
  • Lauth, R.R., Lanelli, J. and Wakefield, W.W. 2004. Estimating the size selectivity and catching efficiency of a survey bottom trawl for thornyheads, Sebastolobus spp. using a towed video camera sled. Fisheries Research, 70: 27-37.
  • Lee, D.L., Lane, R.M. and Chang, G.H. 2001. Three- dimensional reconstruction for high speed volume measurement. Proceedings of SPIE, 4189: 258-267.
  • Lee, D.J., Eifert, J.D., Zhan, P.C. and Westover, B.P. 2003. Fast surface approximation for volume and surface area measurements using distance transform. Optical Engineering, 42(10): 2947-2955.
  • Lee, D.J., Xua, X., Laneb, R.M. and Zhan, P. 2004. Shape analysis for an automatic oyster grading system. SPIE Optics East, Two and Three-Dimensional Vision Systems for Inspection, Control, and Metrology II, vol. 5606-05, October 25-28, Philadelphia, PA, USA.
  • Li, J. and Wheaton, F.W. 1992. Image processing and pattern recognition for oyster hinge line detection. Aquaculture Engineering, 11: 231-250.
  • Ling, P.P. and Searcy, S.W. 1989. Feature extraction for a vision based shrimp deheader. International Winter Meeting of the ASAE, December 12-15, New Orleans, LA.
  • Little, N.E., Smith, O.H., Wheaton, F.W. and Little, M.A. 2007a. Automated oyster shucking Part I. An orientation system for American oysters, Crassostrea virginica. Aquacultural Engineering, 37: 24-34.
  • Little, N.E., Smith, O.H., Wheaton, F.W. and Little, M.A. 2007b. Automated oyster shucking Part II. Computer vision and control system for an automated oyster orienting device. Aquacult. Engineering, 37: 35-43.
  • Locht, P., Thomsen, K. and Mikkelsen, P. 1997. Full color image analysis as a tool for quality control and process development in the food industry. Paper No. 973006, ASAE, 2950 Niles Road, St. Joseph, MI 49085-9659, USA.
  • Louka, N., Juhel, F., Fazilleau, V. and Loonis, P. 2004. A novel colorimetry analysis used to compare different fish drying processes. Food Control, 15: 327-334.
  • Loy, A., Busilacchi, S., Costa, C., Ferlin, L. and Cataudella, S. 2000. Comparing geometric morphometrics and outline fitting methods to monitor fish shape variability of Diplodus puntazzo (Teleostea: Sparidae). Aquacultural Engineering, 21: 271-283.
  • Luzuriaga, D., Balaban, M.O. and Yeralan, S. 1997. Analysis of visual quality attributes of white shrimp by machine vision. J. of Food Science, 62(1): 1-7.
  • Luzuriaga, D.A. and Balaban, M.O. 1999. Color machine vision system: an alternative for color measurement. Institute of Food Technologists Annual Meeting, July 25-28, Atlanta, GA, 22E-6.
  • Majumdar, S. and Jayas, D.S. 2000. Classification of cereal grains using machine vision: I. Morphology models. Transactions of the ASAE, 43(6): 1669-1675.
  • 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 colour image analysis. Aquaculture, 232: 225-240.
  • Misimi, E., Mathiassen, J.R. and Erikson, U. 2007. Computer vision-based sorting of Atlantic salmon (Salmo salar) fillets according to their color level. Journal of Food Science, 72: 30–35.
  • Misimi, E., Erikson, U., Digre, H., Skavhaug, A. and Mathiassen, J.R. 2008a. Computer vision-based evaluation of pre-and post-rigor changes in size and shape of Atlantic cod (Gadus morhua) and Atlantic salmon (Salmo salar) fillets during rigor mortis and ice storage: Effects of peri-mortem handling stress. Journal of Food Science, 73: 57-68.
  • Misimi, E., Erikson, U. and Skavhaug, A. 2008b. Quality grading of Atlantic salmon (Salmo salar) by computer vision. Journal of Food Science, 73: 211–217.
  • Mohebbi, M., Akbarzadeh-T, M-R., Shahidi, F., Moussavi, M. and Ghoddusi, H-B. 2009. Computer vision systems (CVS) for moisture content estimation in dehydrated shrimp. Computers and Electronics in Agriculture, 69: 128-134.
  • Munkevik, P., Hall, G. and Duckett, T. 2007. A computer vision system for appearance-based descriptive sensory evaluation of meals. Journal of Food Engineering, 78(1): 246-256.
  • Novini, A. 1995. The latest in vision technology in today's food and beverage container manufacturing industry. In Food Processing Automation IV Proceedings of the Conference. St. Joseph, Michigan, USA.
  • Odabaşı, A.Z., Miles, R.D., Balaban, M.O. and Portier, K.M. 2007. Physiology, endocrinology, and reproduction changes in brown eggshell color as the hen ages. Poultry Science, 86: 356-363.
  • Odone, F., Trucco, E. and Verri, A. 1998. Visual learning of weight from shape using support vector machines. In: J.N. Carter, M.S. Nixon (Eds.), Proceedings of the British Machine Vision Conference, Southampton: 469-477.
  • Panigrahi, S. and Gunasekaran, S. 2001. Computer vision. In: S. Gunasekaran (Ed.), Nondestructive food evaluation techniques to analyze properties and quality, Marcel Dekker, New York: 39-92.
  • Park, B. and Chen, Y.R. 1994. Intensified multispectral imaging system for poultry carcass inspection. Transactions of the ASAE, 37(6): 1983-1988.
  • Park, B. and Chen, Y.R. 2001. Co-occurrence matrix texture features of multi-spectral images on poultry carcasses. J. of Agricultural Engin. Research, 78(2): 127-139.
  • Parr, M.B., Byler, R.K., Diehl, K.C. and Hackney, C.R. 1994. Machine vision based oyster meat grading and sorting machine. Journal of Aquatic Food Product Technology, 3: 5-24.
  • Purnell, G. 1998. Robotic equipment in the meat industry. Meat Science, 49: 297-307.
  • 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: 3-11.
  • Roth, B., Schelvis-Smit, R., Stien, L.H., Foss, A., Nortvedt, R. and Imsland, A. 2007. Exsanguination of turbot and the effect on fillet quality measured mechanically, by sensory evaluation, and with computer vision. Journal of Food Science, 72(9): 525-531.
  • Sarkar, N.R. 1991. Machine vision for quality control in the food industry. In: D.Y.C. Fung., R.F. Mathews (Eds.), Instrumental methods for quality assurance in foods, ASQC Quality Press, Marcel Dekker Inc, New York: 166-188.
  • Shahin, M.A. and Symons, S.J. 2001. A machine vision system for grading lentils. Canadian Biosystems Engineering, 43: 7.7-7.14.
  • Sivertsen, A.H., Chu, C-K., Wang, L-C., Godtliebsen, F., Heia, K. and Nilsen, H. 2009. Ridge detection with application to automatic fish fillet inspection. Journal of Food Engineering, 90: 317-324.
  • Sistler, F.E. 1991. Machine vision techniques for grading and sorting agricultural products. Postharvest News and Information, 2(2): 81-84.
  • So, J.D. and Wheaton, F.W. 2002. Detection of Crassostrea virginica hinge lines with machine vision: software development. Aquaculture Engineering, 26: 171-190.
  • Sonka, M., Hlavac, V. and Boyle, R. 1999. Image Processing, Analysis, and Machine Vision., 2nd edition, PWS Publishing, California, 770 pp.
  • Stien, L.H., Suontama, J. and Kiessling, A. 2006a. Image analysis as a tool to quantify rigor contraction in pre- rigor-filleted fillets. Computer Electronic Agriculture, 50: 109-120.
  • Stien, L.H., Manne, F., Ruohonene, K., Kause, A., Rungruangsak-Torrissen, K. and Kiessling, A. 2006b. Automated image analysis as a tool to quantify the colour and composition of rainbow trout (Oncorhynchus mykiss W.) cutlets. Aquaculture, 261: 695-705.
  • Stien, L.H., Kiessling, A. and Manne, F. 2007. Rapid estimation of fat content in salmon fillets by colour image analysis. Journal of Food Composition and Analysis, 20: 73-79.
  • Storbeck, F. and Daan, B. 2001. Fish species recognition using computer vision and a neural network. Fisheries Research, 51: 11-15.
  • Strachan, N.J.C., Nesvadba, P. and Allen, A.R. 1990. Fish species recognition by shape analysis of images. Pattern Recognition, 23(5): 539-544.
  • Strachan, N.J.C. 1993a. Recognition of fish species by colour and shape. Image Vision Comp., 11(1): 2-10.
  • Strachan, N.J.C. 1993b. Length measurements of fish by computer vision. Computers and Electronics in Agriculture, 8: 93-104.
  • Strachan, N.J.C. 1994. Sea trials of a computer vision based fish species sorting and size grading machine. Mechantronics, 4(8): 773-783.
  • Strachan, N.J.C. and Kell, L. 1995. A potential method for differentiation between haddock fish stocks by computer vision using canonical discriminant analysis. ICES J. of Marine Science, 52: 145-149.
  • Sun, D-W. 2004. Computer vision- an objective, rapid and non-contact quality evaluation tool for the food industry. Journal of Food Engineering, 61: 1-2.
  • Tao, Y., Heinemann, P.H., Varghese, Z., Morrow, C.T. and Sommer, H.J. 1995a. Machine vision for colour inspection of potatoes and apples. Transactions of the ASAE, 38(5): 1555-1561.
  • Tao, Y., Morrow, C.T., Heinemann, P.H. and Sommer, H.J. 1995b. Fourier based separation techniques for shape grading of potatoes using machine vision. Transactions of the ASAE, 38(3): 949-957.
  • Tayama, M., Shimadate, N., Kubota, N. and Nomure, Y. 1982. Application for optical sensor to fish sorting. Reito (Tokyo). Refrigeration, 57: 1146-1150.
  • Tillett, R.D. 1990. Image analysis for agricultural processes. Division Note DN 1585, Silsoe Research Institute. Timmermans, A.J.M. 1998. Computer vision system for online sorting of pot plants based on learning techniques. Acta Horticulturae, 421: 91-98.
  • Tojeiro, P. and Wheaton, F. 1991. Oyster orientation using computer vision. Transactions of the ASAE, 34(2): 689-693.
  • Varela, P., Aguilera, J.M. and Fiszman, S. 2008. Quantification of fracture properties and microstructural features of roasted Marcona almonds by image analysis. LWT-Food Science and Technology, 41: 10-17.
  • Wagner, H., Schmidt, U. and Rudek, J.H. 1987. Distinction between species of sea fish. Lebesmittelindustrie, 34: 20-23.
  • White, D.J., Svellingen, C. and Strachan, N.J.C. 2006. Automated measurement of species and length of fish by computer vision. Fisheries Research, 80: 203-210.
  • Wold, J.P. and Isakkson, T. 1997. Non-destructive determination of fat and moisture in whole Atlantic salmon by near-infrared diffuse spectroscopy. Journal of Food Science, 62(4): 734-736.
  • Xiong, G., Lee, D-J., Moon, K.R. and Lane, R.M. 2010. Shape similarity measure using turn angle cross- correlation for oyster quality evaluation. Journal of Food Engineering, 100: 178-186.
  • Yağız, Y., Kristinsson, H.G., Balaban, M.O. and Marshall, M.R. 2007. Effect of high pressure treatment on the quality of rainbow trout (Oncorhynchus mykiss) and mahi mahi (Coryphaena hippurus). Journal of Food Science, 72: 509-515.
  • Yağız, Y., Balaban, M.O., Kristinsson, H.G., Welt, B.A. and Marshall, M.R. 2009a. Comparison of Minolta colorimeter and machine vision system in measuring colour of irradiated Atlantic salmon. Journal of the Science of Food and Agriculture, 89: 728-730.
  • Yağız, Y., Kristinsson, H.G., Balaban, M.O., Welt, B.A., Ralat, M. and Marshall, M.R. 2009b. Effect of high pressure processing and cooking treatment on the quality of Atlantic salmon. Food Chemistry, 116: 828- 835.
  • Yağız, Y., Kristinsson, H.G., Balaban, M.O., Welt, B.A., Raghavan, S. and Marshall, M.R. 2010. Correlation between astaxanthin amount and a* value in fresh Atlantic salmon (Salmo salar) muscle during different irradiation doses. Food Chemistry, 120: 121-127.
  • Yang, C-C., Chao, K. and Chen, Y-R. 2005. Development of multispectral image processing algorithms for identification of wholesome, septicemic, and inflammatory process chickens. Journal of Food Engineering, 69: 225-234.
  • Yoruk, R., Yoruk, S., Balaban, M.O. and Marshall, M.R. 2004. Machine vision analysis of antibrowning potency for oxalic acid: a comparative investigation on banana and apple. Journal of Food Science, 69(6): 281-289.
  • Zheng, C., Sun, D-W. and Zheng, L. 2006. Recent developments and applications of image features for food quality evaluation and inspection-a review. Trends in Food Science and Technology, 17: 642-655.
  • Zion, B., Shklyar, A. and Karplus, I. 1999. Sorting fish by computer vision. Computers and Electronics in Agriculture, 23(3): 175-187.
Turkish Journal of Fisheries and Aquatic Sciences-Cover
  • ISSN: 1303-2712
  • Başlangıç: 2015
  • Yayıncı: Su Ürünleri Merkez Araştırma Enstitüsü - Trabzon
Sayıdaki Diğer Makaleler

The application of diatom indices in the Upper Porsuk Creek Kütahya - Turkey

Cüneyt Nadir SOLAK

Growth of mixed-sex and monosex Nile tilapia in different culture systems

Suman Bhusan CHAKRABOTY, Debasis MAZUMDAR, Urmi CHATTERJİ, Samir BANERJEE

Off-season maturation and spawning of the Pacific white shrimp litopenaeus vannamei in sub-tropical conditions

Metin KUMLU, Serhat TÜRKMEN, Mehmet KUMLU, O. Tufan EROLDOĞAN

Morphological variations of the trouts (Salmo trutta and Salmo platycephalus) in the rivers of Ceyhan, Seyhan and Euphrates, Turkey

Mustafa Cemil KARA, Ahmet Alp AKER, Mustafa Emre GÜRLEK

Stock assessment of silver pomfret Pampus argenteus (Euphrasen, 1788) in the Northern Persian Gulf

Amrollahi NARGES, Kochanian PREETA, Maremmazi JASEM, Eskandary GHOLAM-REZA, Yavary VAHID

First report of Nerocila orbigyni (Crustacea, Isopoda, Cymothoidae) on Solea solea (Teleostei, Soleidae) from Turkish Sea

Şevki KAYIŞ, Yusuf CEYLAN

About the record of anthias anthias (linnaeus, 1758), (pisces: serranidae) in the Canakkale Strait, Turkey

Sezgin TUNCER, Sabri BİLGİN, Soyal Lütfiye ERYILMAZ

Microcosmus polymorphus heller, 1877 (tunicata: ascidiacea: pyuridae)- a new addition to the fauna of the Turkish coasts

Herdem Aslan CİHANGİR, Andres Izquierdo MUNOZ, Maria A. Pancucci PAPADOPOULOU, A. Alfonso Ramos ESPLA, Elif YILMAZ CAN

Alterations of the Ionic composition in different organs of spotted murrel (Channa punctatus) exposed to sublethal concentration of endosulfan

Kamal SARMA, A. K. PAL, Kartik BARUAH

Protective Action of an Anti-oxidant (vitamin-C) against bisphenol-toxicity in cirrhinus mrigala (ham.)

Sarita MURMU, Vinoy K. SHRIVASTAVA