Abstract

The drying of curry leaves (Murraya koenigii) plays a significant role for their quality attributes, aromatic retention properties, and prolonged shelf stability for culinary and medicinal applications. The objective of this present research study was to investigate the impact of different drying temperatures of 40, 50, and 60 °C on drying kinetics of curry leaves through the application of a convective hot air oven drying technique. The experimental data (varying moisture ratio (MR) with respect to time) obtained by varying drying conditions were fitted into various mathematical models, whereby the Henderson and Pabis model provided the best description of the drying behavior, based on statistical parameters. Moreover, the artificial neural networks (ANNs) model was introduced and trained with obtained experimental data to accurately predict the MR values during the drying process. Predicted results demonstrated that the ANNs model obtained greater accuracy of fit with the experimental results compared to mathematical models, as confirmed by its superior performance matrices: coefficient of determination (R²) of 0.99782 and root mean square error (RMSE) of 0.0156. Experimental and predicted data on MR, which exhibited a good fit of 99.56%, reflect the potential applicability of the ANN model for real-time monitoring and control of the curry leaf drying process in industries. This study has provided valuable information on the drying condition and technique to be adopted for better retention of bioactive compounds present in curry leaves. In addition, the developed ANN model can be integrated into the automated drying systems to predict the MR in a precise manner and for process optimization in order to realize better quality and consistency in the drying of curry leaves and other biological products.

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