MOISTURE CONTENT PREDICTION OF BERGAMOT FRUIT FROM DRYING PROCESS WITH ARTIFICIAL NEURAL NETWORK (ANNs)

摘要

In this study thin-layer drying of bergamot was modelled using artificial neural network (ANN).An experimental dryer was used. Thin-layer of bergamot slices at five air temperatures (40, 50, 60, 70 & 80 oC), one thickness (4 mm) and three air velocities (0.5, 1 & 2 m/s) were artificially dried.Initial moisture content (M.C.) during all experiments was between 5.2 to 5.8 (g.g) (d.b.).Mass of samples were recorded and saved every 5 sec.using a digital balance connected to a PC.MLP with momentum and levenberg-marquardt (LM) were used to train the ANNS.In order to develop ANN's models, temperatures, air velocity and time are used as input vectors and moisture ration as the output.Results showed a 3-6-1 topology for thickness of 4 mm, with LM algorithm and TANSIG activation function was able to predict moisture ratio with 2 R of 0.99925. The corresponding MSE for this topology was 0.00011.