Comparative Modeling of Yoghurt Drying Kinetics Using Artificial Neural Networks and Multiple Linear Regression in Microwave‐Assisted Foam‐Mat Drying

ABSTRACT

This study proposed a novel approach for modeling and predicting the moisture ratio of yoghurt powder during microwave‐assisted foam‐mat drying using artificial neural networks (ANN) and multiple linear regression (MLR). Various ANN configurations, including different training algorithms, transfer functions, and hidden‐layer neuron numbers, were systematically evaluated using the coefficient of determination ( R ² ) and the root mean squared error (RMSE) to find the best structure. In addition, three stepwise MLR models with different predictor combinations were developed to assess the contribution of the independent variables to moisture ratio prediction. The optimal ANN model, consisting of 15 hidden neurons with the logsig activation function in the hidden layer and the purelin activation function in the output layer, trained using the Levenberg–Marquardt backpropagation algorithm ( trainlm ), achieved the highest predictive performance with R ² of 0.9979 and RMSE of 0.0296. Garson's algorithm was employed to assess the relative importance of the input variables (drying time, egg albumin concentration, and microwave power) based on the optimal ANN model. The sensitivity analysis indicated that drying time (40.86%) and egg albumin concentration (40.41%) were the most influential factors governing moisture ratio prediction. Among the multiple linear regression models, MLR1 exhibited the best predictive performance. Nevertheless, the ANN model outperformed all MLR models by achieving higher prediction accuracy and better convergence. These findings demonstrate that the developed ANN model is a reliable tool for predicting moisture ratio and has considerable potential for process optimization, operational control, and reducing the need for extensive experimental trials.