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

83 Citations (Scopus)

Abstract

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
Volume51
Issue number3
DOIs
Publication statusPublished - Feb 2002

Fingerprint

Porosity
neural networks
porosity
drying
Food
prediction
Apium graveolens
calamari
Lens Plant
Pyrus
Decapodiformes
Daucus carota
Garlic
Onions
Malus
Solanum tuberosum
celery
Starch
potato starch
drying temperature

Keywords

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

ASJC Scopus subject areas

  • Food Science

Cite this

Prediction of pores formation (porosity) in foods during drying : Generic models by the use of hybrid neural network. / Hussain, M. A.; Shafiur Rahman, M.; Ng, C. W.

In: Journal of Food Engineering, Vol. 51, No. 3, 02.2002, p. 239-248.

Research output: Contribution to journalArticle

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