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: | , , |
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| Format: | Article |
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Elsevier
2002
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| Online Access: | http://eprints.um.edu.my/7072/ https://doi.org/10.1016/S0260-8774(01)00063-2 |
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| Summary: | 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. |
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