This study presented both the empirical and artificial neural network (ANN) approaches to estimate the moisture content of Mentha spicata. Two different types of drying methods (in shade and in oven (35 and 50°C)) were used to investigate the drying kinetics of the Mentha spicata samples. The effects of drying methods on effective diffusion coefficient, moisture ratio (MR), drying rate, and activation energy were investigated. Moreover, six different thin layer drying models (Page, Diffusion approach, Newton, Modified Henderson, Henderson and Pabis and Pabis and Midilli) and an ANN with feed forward structure were used to define the drying kinetics of these samples. In order to estimate the kinetic model parameters, sequential quadratic programming (SQP) was used. Model performances were evaluated based on the coefficient of determination (R2), root mean square error (RMSE) and mean absolute percentage error (MAPE%) values. In the kinetic part of the modeling study, the Midilli model provided better results than the others. However, the ANN had the best results when a total assessment was made. The effective diffusion coefficient values were found in the range between 1.31 × 10–12 and 4.43 × 10–12 m2/s. The activation energy was obtained as 44.31 kJ/kmol. The R2, MAPE%, and RMSE values for the ANN test data were 1.00, 0.2257, and 5.9447 × 10−4, respectively. In the future, different modeling approaches will be applied to describe this drying process. Practical applications Drying is a process where heat transfer and mass transfer take place together. Modeling is an innovative approach used in evaluation of experimental data and has increasing popularity in recent years. ANNs are a powerful data‐driven method, and they have a very broad area of usage from medicine to engineering issues. Empirical models are another approach for describing experimental data. In this study, these two modeling approaches were used to obtain the MR. Humidity is a condition that needs to be checked in food safety and protection. Therefore, it is very important to ensure control with robust modeling techniques. In this study, the developed ANN model had a high R2 value (R2 = 1.00). This indicated that it may be used successfully in real applications.

This study was conducted to optimize the hydrodistillation process of Mentha spicata L. essential oil using the response surface methodology. The optimal values of operating parameters (independent variables) such as extraction time (100–240 min) and water volume to plant mass ratio (0.055–0.120) were investigated using a central composite design, which led to only 13 experiments. The response variables were selected the highest essential oil yield, and also the carvone ratio, which is the main component of the essential oil. The experimental data was fitted to a linear model for essential oil yield and a modified quadratic model for the carvone ratio. The hydrodistillation time and water volume to plant mass ratio have significant effects (p<0.05) on the both the essential oil yield and the carvone ratio. The optimal conditions were identified as 145.7 min of extraction time and a 0.105 ml/g water volume to plant mass ratio by the 3D response surface and the contour plots derived from the models. At these predicted conditions, the essential oil yield and carvone ratio were calculated to be 1.383% and 28.541%, respectively. The findings indicate that the response surface approach can be used successfully in the hydrodistillation of Mentha spicata L.