Artificial Neural Network Topology Optimization using K-Fold Cross Validation for Spray Drying of Coconut Milk

Abstract

In this study, the development of an optimized topology neural network model for spray drying coconut milk is investigated using K-fold cross validation technique. Performance between standalone ANN and ANN with K-fold cross validation is compared, as K-fold cross validation method is integrated into neural network to overcome the limitations of restricted dataset. With inlet temperature (140 °C-180 °C), concentration of maltodextrin and sodium caseinate (0 w/w %- 10w/w %) are established as the input parameters, while moisture content (3.64%-5.1%), outlet temperature (76.5 °C-104.5 °C) and surface free fat percentage (0.35%-34.51%) are the output parameters for the neural network. Experimental data from the spray drying process is used to develop the neural network. Selection from the best training algorithm (gradient descent backpropagation, gradient descent with momentum, resilience backpropagation, conjugate gradient backpropagation with Polak-Riebre restarts, conjugate gradient backpropagation with Fletcher-Reeves, scaled conjugate gradient, Broyden-Flectcher-Goldfard-Shanno backpropagation algorithm and Levenberg-Marquardt backpropagation), transfer function (tansig, logsig, purelin and satlin), number of training runs (1000-5000), number of hidden layers (1-3) and nodes (5-15) have significant effect on the performance of the ANN models based on the lowest MSE values and R2 values. Overall, the optimum topology ANN model with k-fold cross validation outperformed the recorded lowest MSE value of 0.064 and highest R2 value of 0.855 compared to the optimum standalone ANN model with MSE value of 0.082 and R2 value of 0.832. The optimum ANN with K-fold cross validation implements the Levenberg-Marquart training algorithm with hyperbolic tangent sigmoid transfer function using 4500 times training runs with optimal topology configuration of 3-8-2-3. Result concludes that the developed neural network using K-fold cross validation represents the spray drying process as a highly reliable model with high degree of accuracy.