Prediction of Moisture Content of Bergamot Fruit During Thin-Layer Drying Using Artificial Neural Networks
Mohammad Sharifi, Shahin Rafiee, Hojjat Ahmadi, Masoud Rezaee
Journal of E-Technology, Vol. 3, No. 1, 2012.
DOI: 
Link: http://www.dline.info/jet/fulltext/v3n1/1.pdf
Abstract:
In this study thin-layer drying of bergamot was modelled using artificial neural network. An experimental dryer was used. Thin-layer of bergamot slices at five air temperatures (40, 50, 60, 70 & 80 ÂºC), one thickness (6 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 ANNâ€™s models, temperatures, air velocity and time are used as input vectors and moisture ration as the output. Results showed a 3-8-1 topology for thickness of 6 mm, with LM algorithm and TANSIG activation function was able to predict moisture ratio with R2 of 0.99936. The corresponding MSE for this topology was 0.00006.