Texture Quality Analysis of Rainbow Trout Using Hyperspectral Imaging Method

REFERENCES

• Behnke, R.J. Native Trout of Western North America; American Fisheries Society (Afs Monograph: No 6): Bethesda, MD, 1992.
   Google Scholar
• Clemento, A.J.; Anderson, E.C.; Boughton, D.; Girman, D.; Garza, J.C. Population Genetic Structure and Ancestry of Oncorhynchus Mykiss Populations above and Below Dams in South-Central California. Conservation Genetics 2009, 10, 1321–1336.
   Web of Science ®Google Scholar
• Froese, R.; Pauly, D. Fish Base. http://www.fishbase.org ( accessed on July 1, 2013).
   Google Scholar
• FAOSTAT. Food and Agriculture Organization of the United Nations, FAOSTAT database. http://www.fao.org/fishery/statistics/en ( accessed on October 13, 2013).
   Google Scholar
• Morkore, T.; Einen, O. Relating Sensory and Instrumental Texture Analyses of Atlantic Salmon. Journal of Food Science 2003, 68(4), 1492–1497.
   Web of Science ®Google Scholar
• Brewer, S.; Novakofski, J. Consumer Sensory Evaluations of Aging Effects on Beef Quality. Journal of Food Science 2008, 73, S78–S82.
   PubMed Web of Science ®Google Scholar
• Moskowitz, H.R. Food Texture: Instrumental and Sensory Measurement; Marcel Dekker Incorporated, New York, 1987; 335 p.
   Google Scholar
• Ashton, T.J.; Michie, I.; Johnston, I.A. A Novel Tensile Test Method to Assess Texture and Gaping in Salmon Fillets. Journal of Food Science 2010, 75(4), S182–S190.
   PubMed Web of Science ®Google Scholar
• Wu, D.; He, H.J.; Sun, D.W. Non-Destructive Texture Analysis of Farmed Salmon Using Hyperspectral Imaging Technique. In Proceedings of the 3rd CIGR International Conference of Agricultural Engineering (CIGR-AgEng 2012). Valencia, Spain, 8–12 July, 2012.
   Google Scholar
• Sigurgisladottir, S.; Hafsteinsson, H.; Jonsson, A.; Lie, O.; Tho-massen, M.; Torrissen, O. Textural Properties of Raw Salmon Fillets As Related to Sampling Method. Journal of Food Science 1999, 64, 99–104.
   Web of Science ®Google Scholar
• Casas, C.; Martinez, O.; Guillen, M.D.; Pin, C.; Salmeron, J. Textural Properties of Raw Atlantic Salmon (Salmosalar) at Three Points Along the Fillet, Determined by Different Methods. Food Control 2006, 17(7), 511–515.
   Web of Science ®Google Scholar
• Nollet Leo, M.L. Handbook of Meat, Poultry, and Seafood Quality, 2nd Ed; John Wiley & Sons Inc, Iowa, 2012; 576 p.
   Google Scholar
• Wu, D.; Sun, D.W. Advanced Applications of Hyperspectral Imaging Technology for Food Quality and Safety Analysis and Assessment: A Review—Part II: Applications. Innovative Food Science and Emerging Technologies 2013, 19, 15–28.
   Web of Science ®Google Scholar
• Heia, K.; Sivertsen, A.H.; Stormo, S.K.; Elvevoll, E.; Wold, J.P.; Nilsen, H. Detection of Nematodes in Cod (Gadusmorhua) Fillets by Imaging Spectroscopy. Journal of Food Science 2007, 72, E11–E15.
   PubMed Web of Science ®Google Scholar
• Dale, L.M.; Thewis, A.; Boudry, C.; Rotar, I.; Dardenne, P.; Baeten, V.; Pierna, J.A.F. Hyperspectral Imaging Applications in Agriculture and Agro-Food Product Quality and Safety Control: A Review. Applied Spectroscopy Reviews 2013, 48(2), 142–159.
   Web of Science ®Google Scholar
• Liu, Y.; Lyon, B.G.; Windham, W.R.; Realini, C.E.; Pringle T.D.; Duckett, S. Prediction of Color, Texture, and Sensory Characteristics of Beef Steaks by Visible and Near Infrared Reflectance Spectroscopy. A Feasibility Study. Meat Science 2003, 65(3), 1107–1115.
   PubMed Web of Science ®Google Scholar
• Vermeulen, P.; Pierna, J.F.; Egmond, H.V.; Dardenne, P.; Baeten, V. Online Detection and Quantification of Ergot Bodies in Cereals Using Near Infrared Hyperspectral Imaging. Food Additives & Contaminants: Part 2012, 29(2), 232–240.
   Web of Science ®Google Scholar
• Liu, G.; He, J.; Wang, S.; Luo, Y.; Wang, W.; Wu, L.; Si, Z.; He, X. Application of Near-Infrared Hyperspectral Imaging for Detection of External Insect Infestations on Jujube Fruit. International Journal of Food Properties 2016, 19, 41–52.
   Web of Science ®Google Scholar
• Mollazade, K.; Omid, M.; Akhlaghian Tab, F.; Rezaei Kalaj, Y.; Mohtasebi, S.S. Data Mining-Based Wavelength Selection for Monitoring Quality of Tomato Fruit by Backscattering and Multispectral Imaging. International Journal of Food Properties 2014, 18, 880–896.
   Web of Science ®Google Scholar
• Rasmussen, R.S.; Heinrich, M.H.; Hyldig, G.; Jacobsen C.; Jokumsen, A. Moderate Exercise of Rainbow Trout Induces Only Minor Differences in Fatty Acid Profile, Texture, White Muscle Fibers, and Proximate Chemical Composition of Fillets. Aquaculture 2011, 314, 159–164.
   Web of Science ®Google Scholar
• Kamruzzaman, M.; ElMasry, G.; Sun, D.W.; Allen, P. Non-Destructive Prediction and Visualization of Chemical Composition in Lamb Meat Using NIR Hyperspectral Imaging and Multivariate Regression. Innovative Food Science and Emerging Technologies 2012, 16, 218–226.
   Web of Science ®Google Scholar
• Huang, X.; Xin, J.; Zhao, J. A Novel Technique for Rapid Evaluation of Fish Freshness Using Colorimetric Sensor Array. Journal of Food Engineering 2011, 105(4), 632–637.
   Web of Science ®Google Scholar
• Huang, Y.; Cavinato, A.G.; Mayes, D.M.; Bledsoe, G.E.; Rasco, B.A. Non-Destructive Prediction of Moisture and Sodium Chloride in Cold Smoked Atlantic Salmon (Salmosalar). Journal of Food Science 2002, 67, 2543–2547.
   Web of Science ®Google Scholar
• Savitzky, A.; Golay, M.J.E. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry 1964, 36, 16–27.
   Web of Science ®Google Scholar
• Bakeev, K.A. Process Analytical Technology: Spectroscopic Tools and Implementation, 2nd Ed; Wiley: New York, NY, 2010; 557 p.
   Google Scholar
• Martens, H.; Næs, T. Multivariate Calibration; John Wiley & Sons: New York, NY, 1989; 504 p.
   Google Scholar
• Parikh, R.; Mathai, A.; Parikh, S.; Sekhar, G.C.; Thomas, R. Understanding and Using Sensitivity, Specificity, and Predictive Values. Indian Journal of Ophthalmology 2008, 56(1), 45–50.
   PubMed Web of Science ®Google Scholar
• Pizarro, C.; Esteban-Díez, I.; Nistal, A.J.; González-Sáiz, J.M. Influence of Data Pre-Processing on the Quantitative Determination of the Ash Content and Lipids in Roasted Coffee by Near Infrared Spectroscopy. Analytica Chimica Acta 2004, 509, 217–227.
   Web of Science ®Google Scholar
• Zhu, F.; Cheng, S.; Wu, D.; He, Y. Rapid Discrimination of Fish Feeds Brands Based on Visible and Short-Wave Near-Infrared Spectroscopy. Food and Bioprocess Technology 2011, 4, 597–602.
   Web of Science ®Google Scholar
• Lin, M.; Al-Holy, N.; Mousavy-Hesary, M.; Al-Qadiri, H.; Cavinato, A.G.; Rasco, B.A. Rapid and Quantitative Detection of the Microbial Spoilage of Chicken Breasts by Diffused Reflectance Spectroscopy (600–1100 nm). Letters in Applied Microbiology 2004, 39, 148–155.
   PubMed Web of Science ®Google Scholar
• Grau, R.; Sanchez, A.J.; Giron, J.; Iborra, E.; Fuentes, A.; Barat, J.M. Nondestructive Assessment of Freshness in Packaged Sliced Chicken Breasts Using SW-NIR Spectroscopy. Food Research International 2011, 44(1), 331–337.
   Web of Science ®Google Scholar