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

Hussain, M.A. and Rahman, M.S. and Ng, C.W. (2002) Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network. Journal of Food Engineering, 51 (3). pp. 239-248. ISSN 0260-8774

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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. © 2001 Elsevier Science Ltd. All rights reserved.

Item Type: Article
Additional Information: 511PF Times Cited:35 Cited References Count:40
Uncontrolled Keywords: Air drying; Density; Generic model; Hybrid neural network; Porosity; Thermal conductivity; Drying; Mathematical models; Moisture; Neural networks; Regression analysis; Generic models; Hybrid neural networks; Food processing; Allium cepa; Allium sativum; Apium graveolens var. dulce; Cephalopoda; Daucus carota; Lens culinaris; Malus x domestica; Pyrus communis; Solanum tuberosum.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TP Chemical technology
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 10 Jul 2013 07:03
Last Modified: 11 Dec 2013 03:30
URI: http://eprints.um.edu.my/id/eprint/7072

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