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

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 poin...

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Main Authors: Hussain, Mohd Azlan, Rahman, M.S., Ng, C.W.
Format: Article
Published: Elsevier 2002
Subjects:
TA Engineering (General). Civil engineering (General)
TP Chemical technology
Online Access:http://eprints.um.edu.my/7072/
https://doi.org/10.1016/S0260-8774(01)00063-2
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spelling my.um.eprints.70722021-02-10T03:38:46Z http://eprints.um.edu.my/7072/ Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network Hussain, Mohd Azlan Rahman, M.S. Ng, C.W. TA Engineering (General). Civil engineering (General) TP Chemical technology 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. Elsevier 2002 Article PeerReviewed Hussain, Mohd Azlan 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 https://doi.org/10.1016/S0260-8774(01)00063-2 doi:10.1016/S0260-8774(01)00063-2
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TA Engineering (General). Civil engineering (General)
TP Chemical technology
spellingShingle TA Engineering (General). Civil engineering (General)
TP Chemical technology
Hussain, Mohd Azlan
Rahman, M.S.
Ng, C.W.
Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network
description 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.
format Article
author Hussain, Mohd Azlan
Rahman, M.S.
Ng, C.W.
author_facet Hussain, Mohd Azlan
Rahman, M.S.
Ng, C.W.
author_sort Hussain, Mohd Azlan
title Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network
title_short Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network
title_full Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network
title_fullStr Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network
title_full_unstemmed Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network
title_sort prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network
publisher Elsevier
publishDate 2002
url http://eprints.um.edu.my/7072/
https://doi.org/10.1016/S0260-8774(01)00063-2
_version_ 1691733422576238592
score 13.160551

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