The moisture content of papaya fruit during drying in a cabinet dryer was estimated using both mathematical and neural network models. The effect of air temperatures (40, 50 and 60 °C) and fruit slice thickness (3, 5, 7 mm) on moisture ratio were investigated. A three-layer perceptron neural network with different training algorithms was designed and used. To obtain better estimation of fruit moisture content, topologies were established based on different threshold functions. The results showed that the multi-layer perceptron network with 3-9-1 topology, the Levenberg-Marquardt training algorithm and the logarithmic sigmoid threshold function provided the least errors. Furthermore, eight well-known mathematical models were also used to simulate the drying process. Among the mathematical drying models, the Two-term model was found to be more suitable for predicting the drying of papaya fruit slices, with a coefficient of determination (R2 ) of 0.9974 and a root-mean-square error (RMSE) of 0.0123. However, these values were lower than those obtained for the designed neural network model (R2 = 0.9994; RMSE = 0.0070). Therefore, estimation of the moisture content of papaya fruit could be better modelled by a neural network than by the mathematical models.
