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 ºC), 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 ANN’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 R 2 of 0.99936. The corresponding MSE for this topology was 0.00006.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Anonymous, Annual agricultural statistics. Ministry of Jihad-e-Agriculture of Iran (2010), www.maj.ir
Shry, C., Reiley, E.: Introductory Horticulture. Delmar Cengage Learning Press, India (2010)
Mojtahedi, M.: Horticulture. Behnashr publication, Tehran (2006)
Hernández, J.A.: Optimum operating conditions for heat and mass transfer in foodstuffs drying by means of neural network inverse. Food Control 20(4), 435–438 (2009)
Menhaj, M.B.: Artificial Neural Networks Principles. Amirkabir University of Technology Press, Tehran (2001)
Kishan, M., Chilukuri, K., Ranka, M.: Elements of Artificial Neural Networks (1996)
Khanna, T.: Foundations of Neural Networks. Addison-Wesley Publishing Company, USA (1990)
Omid, M., Baharlooei, A., Ahmadi, H.: Modeling Drying Kinetics of Pistachio Nuts with Multilayer Feed-Forward Neural Network. Drying Technology 27, 1069–1077 (2009)
Erenturk, S., Erenturk, K.: Comparison of genetic algorithm and neural network approaches for the drying process of carrot. Journal of Food Engineering 78, 905–912 (2006)
Erenturk, K., Erenturk, S., Lope, G.: Comparative study for the estimation of dynamical drying behavior of Echinacea angustifolia: regression analysis and neural network. Computers and Electronics in Agriculture 45(3), 71–90 (2004)
Islam, M.R., Sablani, S.S., Mujumdar, A.S.: An artificial neural network model for prediction of drying rates. Drying Technology 21(9), 1867–1884 (2003)
Chen, C.R., Ramaswamy, H.S., Alli, I.: Predicting quality changes during osmo-convective drying of blueberries for process optimization. Drying Technology 19, 507–523 (2001)
Nazghelichi, T., Aghbashlo, M., Kianmehr, M.H.: Optimization of an artificial neural network topology using coupled response surface methodology and genetic algorithm for fluidized bed drying. Computers and Electronics in Agriculture 75(1), 84–91 (2011)
Movagharnejad, K., Nikzad, M.: Modeling of tomato drying using artificial neural network. Computers and Electronics in Agriculture 59, 78–85 (2007)
Lertworasirikul, S.: Drying kinetics of semi-finished cassava crackers: A comparative study. Lebensmittel-Wissenschaft und-Technologie 41, 1360–1371 (2008)
Yadollahinia, A.: A Thin Layer Drying Model for Paddy Dryer. Master’s thesis. University of Tehran, Iran (2006)
Asabe. Moisture measurement: grain and seeds. ASABE Standard S352.2. FEB03. American Society of Agricultural and Biological Engineers, St Joseph, MI 49085, USA (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sharifi, M., Rafiee, S., Ahmadi, H., Rezaee, M. (2011). Prediction of Moisture Content of Bergamot Fruit during Thin-Layer Drying Using Artificial Neural Networks. In: Pichappan, P., Ahmadi, H., Ariwa, E. (eds) Innovative Computing Technology. INCT 2011. Communications in Computer and Information Science, vol 241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27337-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-27337-7_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27336-0
Online ISBN: 978-3-642-27337-7
eBook Packages: Computer ScienceComputer Science (R0)