Genetic Algorithm–Artificial Neural Network Modeling of Moisture and Oil Content of Pretreated Fried Mushroom

In this research, the effect of different pretreatments (osmotic dehydration and gum coating) on moisture and oil content of fried mushroom was investigated, and artificial neural network and genetic algorithm were applied for modeling of these parameters during frying. Osmotic dehydration was performed in solution of NaCl with concentrations of 5% and 10%, and methyl cellulose was used for gum coating. Either pretreated or control samples were fried at 150, 170, and 190 °C for 0.5, 1, 2, 3, and 4 min. The results showed that osmotic dehydration and gum coating significantly decreased (0–84%, depending upon the processing conditions) oil content of fried mushrooms. However, moisture content of fried samples diminished as result of osmotic pretreatment and increased by gum coating. An artificial neural network was developed to estimate moisture and oil content of fried mushroom, and genetic algorithm was used to optimize network configuration and learning parameters. The developed genetic algorithm–artificial neural network (GA–ANN) which included 17 hidden neurons could predict moisture and oil content with correlation coefficient of 0.93 and 96%, respectively. These results indicating that GA–ANN model provide an accurate prediction method for moisture and oil content of fried mushroom.

[1]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[2]  E. Vicente,et al.  Reducing fat uptake in cassava product during deep-fat frying. , 2009 .

[3]  Amir Ahmad Dehghani,et al.  Intelligent Estimation of the Canola Oil Stability Using Artificial Neural Networks , 2012, Food and Bioprocess Technology.

[4]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[5]  G. Mittal,et al.  Low-Fat Fried Foods with Edible Coatings: Modeling and Simulation , 1999 .

[6]  Mohammad-R. Akbarzadeh-T,et al.  Computer vision systems (CVS) for moisture content estimation in dehydrated shrimp , 2009 .

[7]  Victor Kuri,et al.  Osmotic pre-treatment effect on fat intake reduction and eating quality of deep-fried plantain , 2007 .

[8]  N. Rastogi,et al.  Effect of pre-drying on kinetics of moisture loss and oil uptake during deep fat frying of chickpea flour-based snack food , 2003 .

[9]  M. Izadifar,et al.  Application of genetic algorithm for optimization of vegetable oil hydrogenation process , 2007 .

[10]  S. Goñi,et al.  Prediction of foods freezing and thawing times: Artificial neural networks and genetic algorithm approach , 2008 .

[11]  Zacharias B. Maroulis,et al.  Effect of osmotic dedydration pretreatment on quality of french fries , 2001 .

[12]  Suresh Prasad,et al.  Drying kinetics and rehydration characteristics of microwave-vacuum and convective hot-air dried mushrooms , 2007 .

[13]  G. Mittal,et al.  Comparative evaluation of edible coatings to reduce fat uptake in a deep-fried cereal product , 2002 .

[14]  F. Pedreschi,et al.  Kinetics of oil uptake during frying of potato slices : Effect of pre-treatments , 2006 .

[15]  Ben S. Gerber,et al.  Use of genetic algorithms for neural networks to predict community-acquired pneumonia , 2004, Artif. Intell. Medicine.

[16]  Ali Mohebbi,et al.  A neural network for predicting saturated liquid density using genetic algorithm for pure and mixed refrigerants , 2008 .

[17]  Seyed Mohammad Ali Razavi,et al.  Application of Image Analysis and Artificial Neural Network to Predict Mass Transfer Kinetics and Color Changes of Osmotically Dehydrated Kiwifruit , 2011 .

[18]  T. J. Shankar,et al.  A Case Study on Optimization of Biomass Flow During Single-Screw Extrusion Cooking Using Genetic Algorithm (GA) and Response Surface Method (RSM) , 2010 .

[19]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[20]  F. Pedreschi,et al.  Modeling water loss and oil uptake during vacuum frying of pre-treated potato slices , 2009 .