The thin layer drying kinetics of pumpkin slices (Cucurbita moschata) were experimentally
investigated in a convective hot air dryer. In order to select the appropriate model for predicting
the drying kinetics of pumpkin (Cucurbita moschata), twelve thin layer semi theoretical,
theoretical and empirical models, widely used in describing the drying behaviour of agricultural
products were fitted to the experimental data. The Page and Two term exponential models
showed the best fit under certain drying conditions. The Hii et al. (2009) model, which was
adopted from a combination of the Page and Two term models was compared to the other 11
selected thin layer models based on the coefficient of determination (R2
) and sum of squares
error (SSE). Comparison was made between the experimental and model predicted moisture
ratio by non-linear regression analysis. Furthermore, the effect of drying temperature and slice
thickness on the best model constants was evaluated. Consequently, the Hii et al. (2009) model
showed an excellent fit with the experimental data (R2 > 0.99 and SSE < 0.012) for the drying
temperatures of 50, 60, 70 and 80 °C and at different sample thicknesses of 3 mm, 5 mm and
7 mm respectively. Thus, the Hii et al. (2009) model can adequately predict the drying kinetics
of pumpkin.
This study investigated the drying kinetic of pumpkin under different drying temperatures (50,
60, 70 and 80°C), samples thickness (3, 4, 5 and 7mm), air velocity (1.2m/s) and relative
humidity (40 - 50%). Kinetic models were developed using semi-theoretical thin layer models
and multi-layer feed-forward artificial neural network (ANN) method. The Hii et al. (2009)
semi-theoretical model was found to be the most suitable thin layer model while two hidden
layers with 20 neurons was the best for the ANN method. The selections were based on the
statistical indicators of coefficient of determination (R2), root mean square error (RMSE) and
sum of squares error (SSE). Results indicated that the ANN demonstrated better prediction
than those of the theoretical models with R2, RMSE and SSE values of 0.992, 0.036 and 0.207
as compared to the Hii et al. (2009) model values of 0.902, 0.088 and 1.734 respectively. The
validation result also showed good agreement between the predicted values obtained from
the ANN model and the experimental moisture ratio data. This indicates that an ANN can
effectively describe the drying process of pumpkin.