Modeling and optimization of the process parameters in vacuum drying of culinary banana (Musa ABB) slices by application of artificial neural network and genetic algorithm

The influence of drying temperature, sample slice thickness, and pretreatment on quality attributes like rehydration ratio, scavenging activity, color (in terms of nonenzymatic browning), and texture (in terms of hardness) of culinary banana (Musa ABB) has been evaluated in the present study. A comparative approach was made between artificial neural network (ANN) and response surface methodology (RSM) to predict various parameters for vacuum drying of culinary banana. The effect of process variables on responses during dehydration were investigated using general factorial experimental design. This design was used to train feed-forward back-propagation ANN. The predictive capabilities of these two methodologies for optimization of process parameters were compared in terms of relative deviation (R_d). Results revealed that a properly trained ANN model is found to be more accurate in prediction as compared to RSM. The optimum condition selected from ANN/GA responses on the basis of highest fitness value revealed that culinary banana slices of 6 mm thickness pretreated with 1% citric acid and dried at 76°C resulted in a maximum rehydration ratio of 6.20, scavenging activity of 48.63% with minimum nonenzymatic browning of 25%, and hardness of 43.63 N. Results further revealed that, in the case of rehydration ratio, temperature and pretreatment showed a positive effect while thickness had a negative effect. On the contrary, for scavenging activity, temperature showed the highest negative effect followed by slice thickness and positive effect with pretreatment. For nonenzymatic browning, thickness showed the highest negative effect but temperature and pretreatment showed a positive effect. Similarly, for hardness, all three parameters showed a negative effect.