TY - JOUR
T1 - Prediction of pores formation (porosity) in foods during drying
T2 - Generic models by the use of hybrid neural network
AU - Hussain, M. A.
AU - Shafiur Rahman, M.
AU - Ng, C. W.
PY - 2002/2
Y1 - 2002/2
N2 - 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.
AB - 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.
KW - Air drying
KW - Density
KW - Generic model
KW - Hybrid neural network
KW - Porosity
KW - Thermal conductivity
UR - http://www.scopus.com/inward/record.url?scp=0036466560&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0036466560&partnerID=8YFLogxK
U2 - 10.1016/S0260-8774(01)00063-2
DO - 10.1016/S0260-8774(01)00063-2
M3 - Article
AN - SCOPUS:0036466560
SN - 0260-8774
VL - 51
SP - 239
EP - 248
JO - Journal of Food Engineering
JF - Journal of Food Engineering
IS - 3
ER -