Application of Visible and Near Infrared Hyperspectral Imaging to Differentiate Between Fresh and Frozen-Thawed Fish Fillets

Food and Bioprocess Technology

Volumn 6, Issue 10, 2013, Pages 2931-2937

  Zhu, Fengle ^a   Zhang, Derong ^b   He, Yong ^a   Liu, Fei ^a   Sun, Da Wen ^c  

^a ZHEJIANG UNIVERSITY   (China)

^b ZHEJIANG UNIVERSITY   (China)

^c NATIONAL UNIVERSITY OF IRELAND   (Ireland)

Author keywords

Differentiation; Fresh fish; Frozen thawed fish; Hyperspectral imaging; Least squares support vector machine

Indexed keywords

FRESH FISH; FROZEN-THAWED FISH; GRAY LEVEL CO-OCCURRENCE MATRIX; HYPERSPECTRAL IMAGING; HYPERSPECTRAL IMAGING SYSTEMS; HYPERSPECTRAL IMAGING TECHNIQUES; LEAST SQUARES SUPPORT VECTOR MACHINES; NON-DESTRUCTIVE TECHNIQUE;

DIFFERENTIATION (CALCULUS); FREEZING; IMAGING TECHNIQUES; MICROCOMPUTERS; PRINCIPAL COMPONENT ANALYSIS; SPECTROSCOPY; SUPPORT VECTOR MACHINES; THAWING;

FISH;

SCOPHTHALMUS MAXIMUS;

EID: 84883655179     PISSN: 19355130     EISSN: 19355149     Source Type: Journal    
DOI: 10.1007/s11947-012-0825-6     Document Type: Article

References (25)

1. Baixas-Nogueras, S., Bover-Cid, S., Veciana-Nogués, M. T., & Vidal-Carou, M. C. (2007). Effects of previous frozen storage on chemical, microbiological and sensory changes during chilled storage of Mediterranean hake (Merluccius merluccius) after thawing. European Food Research and Technology, 226, 287-293.
2. Benjakul, S., Visessanguan, W., Thongkaew, C., & Tanaka, M. (2003). Comparative study on physicochemical changes of muscle proteins from some tropical fish during frozen storage. Food Research International, 36, 787-795.
3. Chau, A., Whitworth, M., Leadley, C., & Millar, S. (2009). Innovative sensors to rapidly and non-destructively determine fish freshness. Campden BRI. Report No. CMS/REP/110284/1.
4. Costa, C., D'Andrea, S., Russo, R., Antonucci, F., Pallottino, F., & Menesatti, P. (2011). Application of non-invasive techniques to differentiate sea bass (Dicentrarchus labrax, L. 1758) quality cultured under different conditions. Aquaculture International, 19, 765-778.
5. Duflos, G., Le Fur, B., Mulak, V., Becel, P., & Malle, P. (2002). Comparison of methods of differentiating between fresh and frozen-thawed fish or fillets. Journal of the Science of Food and Agriculture, 82, 1341-1345.
6. ElMasry, G., & Wold, J. P. (2008). High-speed assessment of fat and water content distribution in fish fillets using online imaging spectroscopy. Journal of Agricultural and Food Chemistry, 56, 7672-7677.
7. Folkestad, A., Wold, J. P., Rørvik, K. A., Tschudi, J., Haugholt, K. H., Kolstad, K., & 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.
8. Gao, X., & Tan, J. (1996). Analysis of expended-food texture by image processing part I: geometric properties. Journal of Food Processing Engineering, 19, 425-444.
9. Green, A. A., Berman, M., Switzer, P., & Craig, M. D. (1988). A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing, 26, 65-74.
10. Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3, 610-621.
11. Johnson, R. A. (1998). Applied multivariate methods for data analysis. New York: Duxbury.
12. Karoui, R., Thomas, E., & Dufour, E. (2006). Utilisation of a rapid technique based on front-face fluorescence spectroscopy for differentiating between fresh and frozen-thawed fish fillets. Food Research International, 39, 349-355.
13. Karoui, R., Lefur, B., Grondin, C., Thomas, E., Demeulemester, C., Baerdemaeker, J. D., & Guillard, A.-S. (2007). Mid-infrared spectroscopy as a new tool for the evaluation of fish freshness. International Journal of Food Science and Technology, 42, 57-64.
14. Li, B., & Sun, D. W. (2002). Novel methods for rapid freezing and thawing of foods-a review. Journal of Food Engineering, 54, 175-182.
15. Li, X. L., He, Y., & Wu, C. Q. (2008). Non-destructive discrimination of paddy seeds of different storage age based on Vis/NIR spectroscopy. Journal of Stored Products Research, 44, 264-268.
16. Menesatti, P., Costa, C., & Aguzzi, J. (2010). Quality evaluation of fish by hyperspectral imaging. In D.-W. Sun (Ed.), Hyperspectral imaging for food quality analysis and control (pp. 273-294). London: Academic.
17. Nilsen, H., Esaiassen, M., Heia, K., & Sigernes, F. (2002). Visible/near-infrared spectroscopy: a new tool for the evaluation of fish freshness? Journal of Food Science, 67, 1821-1826.
18. Osborne, B. G., & Fearn, T. (1986). Near infrared spectroscopy in food analysis. New York: Longman Scientific & Technical.
19. Sivertsen, A. H., Chu, C. K., Wang, L. C., Godtliebsen, F., Heia, K., & Nilsen, H. (2009). Ridge detection with application to automatic fish fillet inspection. Journal of Food Engineering, 90, 317-324.
20. Sivertsen, A. H., Kimiya, T., & Heia, K. (2011). Automatic freshness assessment of cod (Gadus morhua) fillets by Vis/Nir spectroscopy. Journal of Food Engineering, 103, 317-323.
21. Suykens, J. A. K., van Gestel, T., De Brabanter, J., De Moor, B., & Vandewalle, J. (2002). Least squares support vector machines. Singapore: World Scientific.
22. Uddin, M., & Okazaki, E. (2004). Classification of fresh and frozen-thawed fish by near-infrared spectroscopy. Journal of Food Science, 69, C665-C668.
23. Uddin, M., Okazaki, E., Turza, S., Yumiko, Y., Tanaka, M., & Fukuda, Y. (2005). Non-destructive visible/NIR spectroscopy for differentiation of fresh and frozen-thawed fish. Journal of Food Science, 70, C506-C510.
24. Xiccato, G., Trocino, A., Tulli, F., & Tibaldi, E. (2004). Prediction of chemical composition and origin identification of European sea bass (Dicentrarchus labrax L.) by near infrared reflectance spectroscopy (NIRS). Food Chemistry, 86, 275-281.
25. Zheng, C. X., Sun, D. W., & Zheng, L. Y. (2006). Recent applications of image texture for evaluation of food qualities-a review. Trends in Food Science and Technology, 17, 113-128.