Modeling Microwave Drying Kinetics of Thyme (Thymus Vulgaris L.) Leaves Using ANN Methodology and Dried Product Quality

Abstract

Effects of microwave power output and sample mass on drying behavior, color parameters, rehydration characteristics and some sensory scores of thyme leaves were investigated. Within the range of the microwave power outputs, 180–900 W, and sample amounts, 25–100 g, moisture content of the leaves were reduced to 0.1 ± (0.01) from 4.05 kg water/kg dry base value. Drying times of the leaves were found to be varying between 3.5 and 15.5 min for constant sample amount, and 6.5 and 20.5 min for constant power output. Experimental drying data obtained were successfully modeled using artificial neural networks methodology. Statistical values of the test data were found to be 0.9999, 4.0937 and 0.025 for R-square, MAPE (%) and RMSE, respectively. Some changes were recorded in the quality parameters, and acceptable sensory scores for the dried leaves were observed in all of the experimental conditions (P < 0.05). Practical Applications Drying is a very important preservation method used in the food industry. Microwave drying supplies uniform energy, higher drying rates and gives higher quality of the finished products compared with conventional drying methods. In order to model experimental drying data, many correlations that are available in literature may be used. However, artificial neural network methodology has become increasingly popular recently because of its capability of giving more general and precise results as also presented in this study.