MODELING ISOSTERIC HEAT OF BANANA FOAM MAT USING NEURAL NETWORK APPROACH
Preeda Prakotmak, Hataitep Wongsuwan, Somchart Soponronnarit and Somkiat Prachayawarakorn
DOI : 10.3844/ajassp.2014.1279.1291
American Journal of Applied Sciences
Volume 11, Issue 8
Information on the adsorption isotherm and the thermodynamic properties can assist in optimizing food processing operations such as drying, packaging and storage in the assessment of the quality of food. In this study, an Artificial Neural Network (ANN) was used for modelling the water activity/Equilibrium Relative Humidity (ERH) of banana foam mat under a range of values of the Experimental Equilibrium Moisture Content (EMC) to calculate the isosteric heat of sorption (qst) by applying the Clausius-Clapeyron equation. The EMC of three dry banana foam samples at different densities of 0.21, 0.26 and 0.30 g/cm3 was determined by a standard gravimetric method over a temperature range of 35-45°C and a relative humidity range of 32-83%. The modified-GAB model best fitted the EMC data. However, the modified-GAB model was not acceptable for predicting the heat sorption behaviour. A negative value of qst estimated using the modified-GAB equation was found at a moisture content above 0.24 kg/kg d.b., showing the poor fit of the model. A multilayer feed-forward ANN trained by back-propagation algorithms was developed to correlate the output ERH to three exogenous inputs (foam density, EMC and temperature). The developed ANN models could predict the ERH more accurately than the modified-GAB model. The predictions from the ANN models produced R2 values higher than 0.97. No negative qst values were found using the ANN method."
© 2014 Preeda Prakotmak, Hataitep Wongsuwan, Somchart Soponronnarit and Somkiat Prachayawarakorn. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.