Journal
JOURNAL OF FOOD ENGINEERING
Volume 51, Issue 3, Pages 239-248Publisher
ELSEVIER SCI LTD
DOI: 10.1016/S0260-8774(01)00063-2
Keywords
generic model; hybrid neural network; air drying; porosity; density; thermal conductivity
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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. (C) 2001 Elsevier Science Ltd. All rights reserved.
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