Linear regression, neural network and induction analysis to determine harvesting and processing effects on surimi quality

Journal of Food Science

Volumn 61, Issue 5, 1996, Pages 876-880

  Peters, G ^a   Morrissey, M T ^b   Sylvia, G ^a   Bolte, J ^a  

^a OREGON STATE UNIVERSITY   (United States)

^b OSU Seafood Laboratory   (United States)

Author keywords

induction; neural networks; quality attributes; surimi

Indexed keywords

MERLANGIUS MERLANGUS;

EID: 0029985019     PISSN: 00221147     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1365-2621.1996.tb10893.x     Document Type: Article

References (22)

1. California Scientific Software. 1988. Brainmaker - Neural Network Simulation Software. User's Guide and Reference Manual. California Scientific Software, Sierra Madre, CA.
2. Chung, Y.C., Richardson, L., and Morrissey, M.T. 1993. Effects of pH and NaCl on gel strength of Pacific whiting surimi. J. Aquatic Food Product Technol. 2(3): 19-36.
3. Eerikainen, T., Linko, P., Linko, S., Siimes, T., and Zhu, Y-H. 1993. Fuzzy logic and neural network applications in food science and technology. Trends in Food Sci. & Technol. 4: 237-242.
4. Horimoto, Y., Durance, T., Nakai, S., and Lukow, O.M. 1995. Neural networks versus principal component regression for prediction of wheat flour volume in baking tests. J. Food Sci. 60: 429-433.
5. Larkin, S. and Sylvia, G. 1995. Intrinsic product characteristics and fisheries management. Submitted to J. Mar. Resource Management.
6. Lanier, T.C., Hart, K., and Martin, R.E. 1991. A manual of standard methods for measuring and specifying the properties of surimi. Univ. of North Carolina Sea Grant Program, Raleigh, NC.
7. Lin, J. and Park, J.W. 1996. Effective washing conditions reduce water usage for surimi processing. Submitted to J. Aquatic Food Product Technol.
8. Morrissey, M.T., Wu, J.W., Lin, D., and An, H. 1993. Protease inhibitor effects on torsion measurements and autolysis of Pacific whiting surimi. J. Food Sci. 58: 1050-1054.
9. Nelson, R.W., Barnett, H.J., and Kudo, G. 1985. Preservation and processing characteristics of Pacific whiting, Merluccius productus. Mar. Fish. Rev. 47(2): 60-74.
10. Park, J.W. and Morrissey, M.T. 1994. The need for developing uniform surimi standards. In Quality Control & Quality Assurance for Seafood, G. Sylvia, A.L. Shriver and M.T. Morrissey (Ed.), p.64-71. Oregon Sea Grant, Corvallis, OR.
11. Patashnik, M., Groninger, H.S., Barnett, H., Kudo, G., and Koury, B. 1982. Pacific whiting (Merluccius productus): 1. Abnormal muscle texture caused by Myxosporidean-induced proteolysis. Mar. Fish. Rev. 44(5): 1-12.
12. Peters, G. 1995. Determination of Quality Parameters for the Pacific Whiting Fishery Using Neural Network and Induction Modeling. Ph.D. dissertation, Oregon State Univ., Corvallis, OR.
13. Peters, G. and Sylvia, G. 1991. Market opportunities for Pacific whiting. Report prepared for the Oregon Dept. of Agriculture. Oregon Coastal Zone Management Association, Newport, OR.
14. Peters, G., Morrissey, M.T., Bolte, J., and Sylvia, G. 1994. Determination of quality parameters for Pacific whiting using computer analysis techniques. In Proceedings Conference on New Developments in Seafood Science and Technology, in press. Canadian Institute of Food Technology, Vancouver, B.C.
15. Quinlan, J.R. 1992. Learning with continuous classes. In Proceedings AI '92, Australian National Conference on Artificial Intelligence, p. 343-348, World Scientific, Singapore.
16. Quinlan, J.R. 1993. Combining instance-based and model-based learning. In Proceedings Tenth International Conference on Machine Learning, p. 236-43. Morgan Kaufmann, San Mateo, CA.
17. Radtke, H. 1995. Windows on Pacific whiting: An economic success story for Oregon's fishing industry. Oregon Coastal Zone Management Association, Newport, OR.
18. Rumelhart, D.E. and McClelland, J.L. 1987. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 1. Foundations. MIT Press, Cambridge MA.
19. Schank, R.C. and Kass, A. 1990. Explanations, machine learning, and creativity. In Machine Learning: An Artificial Intelligence Approach, Volume III, Y. Kodratoff and R.S. Michalski (Ed.), p. 31-38, San Mateo, CA.
20. Stanley, J. 1988. Introduction to Neural Networks. California Scientific Software, Sierra Madre, CA.
21. Sylvia, G., Larkin, S., and Morrissey, M.T. 1994. Product quality and resource management: Bioeconomic analysis of the Pacific whiting industry. In Recent Advances in Marine Science and Technology '94, O. Bellwood, H. Choat, and N. Saxena (Ed.), p. 723-731. James Cook Univ., Townsville, AU.
22. Vallejo-Cordoba, B., Arteaga, G.E., and Nakai, S. 1995. Predicting milk shelf-life based on artificial neural networks and headspace gas chromatographic data. J. Food Sci. 60: 885-888.