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

Author(s) : PETERS G., MORRISSEY M. T., SYLVIA G., BOLTE J.

Type of article: Article

Summary

Multiple linear regression, neural networks, and M5-induction were used to determine significant variables in the industry. Significant factors included variable intrinsic to the fish (moisture content, salinity, pH, length, weight) and processing variables (processing time, storage temperature, harvest date, wash time, wash ratios). Most variables were highly interactive and nonlinear. Information derived from these models have implications for production and management decisions.

Details

  • Original title: Linear regression, neural network and induction analysis to determine harvesting and processing effects on surimi quality.
  • Record ID : 1997-2268
  • Languages: English
  • Source: Ital. J. Food Sci. - vol. 61 - n. 5
  • Publication date: 1996/09
  • Document available for consultation in the library of the IIR headquarters only.

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