Prediction of pores formation (porosity) in foods during drying: Generic models by the use of hybrid neural network

M. A. Hussain, M. Shafiur Rahman, C. W. Ng

Research output: Contribution to journalArticle

90 Citations (Scopus)


General porosity prediction models of food during air-drying have been developed using regression analysis and hybrid neural network techniques. Porosity data of apple, carrot, pear, potato, starch, onion, lentil, garlic, calamari, squid, and celery were used to develop the model using 286 data points obtained from the literature. The best generic model was developed based on four inputs as temperature of drying, moisture content, initial porosity, and product type. The error for predicting porosity using the best generic model developed is 0.58%, thus identified as an accurate prediction model.

Original languageEnglish
Pages (from-to)239-248
Number of pages10
JournalJournal of Food Engineering
Issue number3
Publication statusPublished - Feb 2002



  • Air drying
  • Density
  • Generic model
  • Hybrid neural network
  • Porosity
  • Thermal conductivity

ASJC Scopus subject areas

  • Food Science

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