Abstract:
Since sesame seeds (SS) are more sensitive to high drying temperature, seeds
are dried naturally indoor with either natural or forced convection air. In this study, SS with the
initial moisture content of around 50.8% (d.b.) were dried until the final moisture content of
about 3.0-3.7% (d.b.). The drying characteristics of SS were investigated under indoor
conditions with both forced convection (FC) and natural convection (NC) of air. Modelling the
correlation between moisture content with drying time and drying method was carried out by
using mathematical and artificial neural networks (ANN). During the FC and NC experiments,
the time to reach the final moisture content was found to be 400 and 900 min, respectively. The
FC drying times were around 55% shorter than the NC drying times. The effective water
diffusion coefficients of SS, under FC and NC conditions, were 3.1×10-11 and 1.1×10-11 m2/s,
respectively. The corresponding values for the overall resistance to diffusion were 70.8×105
and 19.6×106 m2 s/kg, respectively. The ANN model was capable of predicting the moisture
content with a R2 of 0.999, RMSE of less than 0.0116 and MRE of about 1.73%. It was found
that both the Khazaei and Peleg’s models were suitable for predicting the moisture content of
sesame seeds. However, the Khazaei model gave better fit to the drying data. It was concluded
that ANN represented the drying characteristics better than the mathematical models.
