Convective drying of garlic (Allium sativum L.): Artificial neural networks approach for modeling the drying process

Document Type : Research Article-en

Author

• Majid Rasouli

Department of Biosystem Engineering, Faculty of Agricultural Engineering, Bu-Ali Sina University, Hamedan, Iran.

https://doi.org/10.22067/ifstrj.v14i3.66235

Abstract

In this study, artificial neural networks (ANNs) was utilized for modeling and the prediction of moisture content (MC) of garlic during drying. The application of a multi-layer perceptron (MLP) neural network entitled feed forward back propagation (FFBP) was used. The important parameters such as air drying temperature (50, 60 and 70°C), slice thickness (2, 3 and 4 mm) and time (min) were considered as the input parameters, and moisture content as the output for the artificial neural network. Experimental data obtained from a thin-layer drying process were used testing the network. The optimal topology was 3-25-5-1 with LM algorithm and TANSIG threshold function for layers. With this optimized network, R2 and mean relative error were 0.9923 and 9.67 %, respectively. The MC (or MR) of garlic could be predicted by ANN method, with less mean relative error (MRE) and more determination coefficient compared to the mathematical model (Weibull model).

Keywords

• Artificial neural networks
• Back propagation
• Convective drying
• Garlic
• Moisture content

References

Abbasi Souraki, B., & Mowla, D. (2008). Experimental and theoretical investigation of drying behaviour of garlic in an inert medium fluidized bed assisted by microwave. Journal of Food Engineering, 88(4), 438-449. doi:10.1016/j.jfoodeng.2007.12.034

 

AOAC. (1990). Official method of analysis of the Association of Official Analytical Chemists. NO. 934. 06, Arlington: Virginia, USA.

 

Brewester, J. (1997). Onions and Garlic. In: H.C. Wine(eds). The physiology of vegetable crops: CAB International, Cambridge. UK.

 

Chegini, G. R., Khazaei, J., Ghobadian, B., & Goudarzi, A. M. (2008). Prediction of process and product parameters in an orange juice spray dryer using artificial neural networks. Journal of Food Engineering, 84(4), 534-543. doi:10.1016/j.jfoodeng.2007.06.007

 

Demuth, H., & Beale, M. (2003). Neural Network Toolbox for Matlab-Users Guide Version 4.1. Natrick. New York, UAS: The Mathworks Press.

 

Erenturk, S., & Erenturk, K. (2007). Comparison of genetic algorithm and neural network approaches for the drying process of carrot. Journal of Food Engineering, 78(3), 905-912. doi:10.1016/j.jfoodeng.2005.11.031

 

Ertekin, C., & Yaldiz, O. (2004). Drying of eggplant and selection of a suitable thin layer drying model. Journal of Food Engineering, 63, 349-359.

 

Kerdpiboon, S., Kerr, W. L., & Devahastin, S. (2006). Neural network prediction of physical property changes of dried carrot as a function of fractal dimension and moisture content. Food Research International, 39(10), 1110-1118. doi:10.1016/j.foodres.2006.07.019

 

Khazaei, J., & Daneshmandi, S. (2007). Modeling of thin-layer drying kinetics of sesame seeds: mathematical and neural networks modeling. Int. Agrophysics, 21, 335-348.

 

Madamba, P. S., Driscoll, R. H., & Buckle, K. A. (1994). Shrinkage, density and porosity of garlic during drying. J Food Eng, 23(3), 309-319.

 

Midilli, A., Kucuk, H., & Yapar, Z. (2002). A new model for single–layer drying. Dry Technol, 20(7), 1503-1513.

 

Mittal, G., & Zhang, J. (2000). Prediction of temperature and moisture content of frankfurters during thermal processing using neural network. Meat Sci, 55(1), 13-24.

 

Movagharnejad, K., & Nikzad, M. (2007). Modeling of tomato drying using artificial neural network. Computers and Electronics in Agriculture, 59(1-2), 78-85. doi:10.1016/j.compag.2007.05.003

 

Nazghelichi, T., Kianmehr, M. H., & Aghbashlo, M. (2010). Prediction of carrot cubes drying kinetics during fluidized bed drying by artificial neural network. Journal of Food Science and Technology, 48(5), 542-550. doi:10.1007/s13197-010-0166-2

 

Poonnoy, P., Tansakul, A., & Chinnan, M. (2007). Artificial neural network modeling for temperature and moisture content prediction in tomato slices undergoing microwave-vacuum drying. Journal of food science, 72(1), E042-047. doi:10.1111/j.1750-3841.2006.00220.x

 

Rasouli, M., Seiiedlou, S., Ghasemzadeh, H. R., & Nalbandi, H. (2011). Convective drying of garlic (Allium sativum L.): Part I: Drying kinetics, mathematical modeling and change in color. Australian Journal of Crop Science, 5(13), 1707-1714.

 

Satish, S., & Pydi Setty, Y. (2005). Modeling of a continuous fluidized bed dryer using artificial neural networks. International Communications in Heat and Mass Transfer, 32(3-4), 539-547. doi:10.1016/j.icheatmasstransfer.2004.06.005

 

Sharma, G. P., & Prasad, S. (2006). Optimization of process parameters for microwave drying of garlic cloves. Journal of Food Engineering, 75(4), 441-446. doi:10.1016/j.jfoodeng.2005.04.029

 

Sharma, G. P., Prasad, S., & Chahar, V. K. (2009). Moisture transport in garlic cloves undergoing microwave-convective drying. Food and Bioproducts Processing, 87(1), 11-16. doi:10.1016/j.fbp.2008.05.001

 

Topuz, A. (2010). Predicting moisture content of agricultural products using artificial neural networks. Advances in Engineering Software, 41(3), 464-470. doi:10.1016/j.advengsoft.2009.10.003

 

Trelea, I. C., Courtois, F., & Trystram, G. (1997). Dynamic models for drying and wet-milling quality degradation of corn using neural networks. Drying Technol, 15, 1095-1102.

 

Zhang, Q., Yang, S., Mittal, G., & Yi, S. (2002). Ae-automation and emerging technologies:prediction of performance indices and optimal parameters of rough rice drying using neural networks. Biosystems Engineering, 83(3), 281.