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

• Published in: Journal of food engineering Vol. 51; no. 3; pp. 239 - 248
• Main Authors: Hussain, M.A., Shafiur Rahman, M., Ng, C.W.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.02.2002; Elsevier
• Subjects: Air drying; Biological and medical sciences; Density; Food engineering; Food industries; Fundamental and applied biological sciences. Psychology; General aspects; Generic model; Hybrid neural network; Porosity; Thermal conductivity; Generic model; Thermal conductivity; Porosity; Air drying; Density; Hybrid neural network; Temperature; Heat treatment; Water content; Foodstuff; Fruit vegetable; Neural network; Forecast model; Polynomial regression
• ISSN: 0260-8774; 1873-5770
• DOI: 10.1016/S0260-8774(01)00063-2

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.