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Neural Model of Osmotic Dehydration Kinetics of Fruits Cubes

  • Ieroham Baruch
  • Próspero Genina-Soto
  • Boyka Nenkova
  • Josefina Barrera-Cortés
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3192)

Abstract

The paper proposed to use a Recurrent Neural Network model (RNN) for process prediction of the osmotic dehydration kinetics of nature product cubes (apple, sweet potatoes and potatoes) at different operational conditions of temperature and concentration of the osmotic solution. The proposed RNN model has five inputs, three outputs and eight neurons in the hidden layer, with global and local feedbacks. The learning algorithm is a modified version of the dynamic backpropagation one. The learning and generalization mean squared errors are below 2%. The learning was performed in 701 epochs, 40 iterations each one. The statistical analysis confirms the good quality of the proposed RNN model.

Keywords

Hide Layer Sweet Potato Recurrent Neural Network Osmotic Dehydration Osmotic Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Ieroham Baruch
    • 1
  • Próspero Genina-Soto
    • 2
  • Boyka Nenkova
    • 3
  • Josefina Barrera-Cortés
    • 2
  1. 1.Department of Automatic ControlCINVESTAV-IPNMexico D.F.Mexico
  2. 2.Department of Biotechnology and BioengineeringCINVESTAV-IPNMexico D.F.Mexico
  3. 3.IIT-BASSofiaBulgaria

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