Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network
Document Type
Article
Publication Date
1-1-2002
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.
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.
Divisions
fac_eng
Publication Title
Journal of Food Engineering
Volume
51
Issue
3
Publisher
Elsevier
Additional Information
511PF Times Cited:35 Cited References Count:40