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Comparison of Kinetics, Neural Network and Fuzzy Logic in Modelling Texture Changes of Dry Peas in Long Time Cooking

Authors
Journal
LWT - Food Science and Technology
0023-6438
Publisher
Elsevier
Publication Date
Volume
31
Identifiers
DOI: 10.1006/fstl.1998.0416
Keywords
  • Texture
  • Kinetics
  • Neural Network
  • Fuzzy Logic
  • Peas
  • Cooking
Disciplines
  • Computer Science

Abstract

Abstract Kinetic, neural network and fuzzy logic models were proposed to model the textural changes of dry peas cooked at 70, 80, 90 and 100 °C for up to 240 min. The results were compared to the first order kinetic and Rizvi and Tong (R-T) models. It was observed that the textural changes in cooked peas vs. time did not follow the first order reaction kinetic model. The neural network model consistently produced the best fit to the experimental data, and the first-order-reaction kinetic model the worst. The performance of the other three models, that is, the proposed kinetic, fuzzy and R-T, varied. The models were also validated and a similar pattern was observed. Compared to the traditional kinetic models, the neural network and fuzzy logic models are more flexible. They use the weight matrix (NN model) or membership function and fuzzy rules, instead of activation energy and constant.

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