Modeling of Eggplant Drying Process by Infrared System using Genetic Algorithm–Artificial Neural Network Method

Document Type : Complete scientific research article

Author

Abstract

In this study, the thin-layer drying behavior of eggplant slices in an infrared dryer (IR) was investigated. The effect of infrared lamp power (150, 250 and 375 watt), the distance of sample from lamp (5, 10 and 15 cm), samples thickness (0.5 and 1 cm) and drying time on drying of eggplant slices were examined. The results of infrared drying of eggplant showed that with increasing in lamp power and decreases in sample distance from the heat source, the drying rate increases. With increase in infrared power from 150 to 375 watts, weight loss increased from 31.08 to 92.44%. With increase in lamp distance from 5 to 15 cm, weight loss decreased from 92.44 to 31.15%. In this study, process modeling was done with the genetic algorithm–artificial neural network (GA-ANN) method with 4 inputs (power and lamp distance, sample thickness and drying time) and 1 output for prediction of weight reduction. The GA-ANN modeling results showed a network with 14 neurons in one hidden layer with using sigmoid function can be well predict the weight loss in eggplant drying by infrared system (R=0.99). Sensitivity analysis results by optimum ANN showed the infrared power was the most sensitive factor for controlling the weight loss of samples.

Keywords

Main Subjects

References

1. Afzal, M.T.T., and Hilida, Y. 1999. Energy and quality aspect during combined FIR
convection drying of barely. Journal of Food Engineering. 42: 177-188.
2. Akpinar, E.K., and Bicer, Y. 2005. Modeling of the drying of eggplants in thin-layers.
International Journal of Food Science and Technology. 40: 273–281
3. Amiri Chayjan, R., Tabatabaei Bahrabad, S.M., and Rahimi, S.F. 2013. Modeling infraredcovective drying of pistachio nuts under fixed and fluidized bed conditions. Journal of Food
Processing and Preservation. 38: 1224-1233.
4. BahramParvar, M., Salehi, F., and Razavi, S.M.A. 2014. Predicting total acceptance of ice
cream using artificial neural network. Journal of Food Processing and Preservation. 38:
3.1080-1088.
5. Das, I., Das, S., and Satish, K. 2004. Specific energy and quality aspects of infrared (IR)
dried. Journal of Food Engineering. 62: 9–14
6. Doymaz, I. 2011. Drying of eggplant slices in thin layers at different air
temperatures. Journal of Food Processing and Preservation. 35 (2): 280-289.
7. Erenturk, S., and Erenturk, K. 2007. Comparison of genetic algorithm and neural network
approaches for the drying process of carrot. Journal of Food Engineering. 78: 905-912.
8. Ertekin, C., and Yaldiz, O. 2004. Drying of eggplant and selection of a suitable thin layer
drying model. Journal of Food Engineering. 63: 349-359.
9. Hebbar, H.U., Vishwanathan, K.H., and Ramesh, M.N. 2004. Development of combined
infrared and hot air dryer for vegetables. Journal of Food Engineering. 65: 557–563.
10.Lertworasirikul, S., and Saetan, S. 2010. Artificial neural network modeling of mass transfer
during osmotic dehydration of kaffir lime peel. Journal of Food Engineering. 98: 214–223.
11.Nimmol, C. 2010. Vacuum far-infrared drying of foods and agricultural materials, The
Journal of the King Mongkut’s University of Technology North Bangkok. 20: 37-44.
12.Ramzi, M., Kashaninejad, M., Salehi, F., Sadeghi Mahoonak, A.R., and Razavi, S.M. 2015.
Modeling of rheological behavior of honey using genetic algorithm–artificial neural network
and adaptive neuro-fuzzy inference system. Food Bioscience. 9: 60-67.
13.Rastogi, N.K. 2012. Recent trends and developments in infrared heating in food processing.
Critical Review in Food Science and Nutrition. 52: 737-760.
14.Salehi, F., Abbasi Shahkoh, Z., and Godarzi, M. 2015. Apricot osmotic drying modeling
using genetic algorithm- artificial neural network. Journal of Innovation in Food Science and
Technology. 7: 65-76.
15.Salehi, F., Kashaninejad, M., Asadi, F., and Najafi, A. 2016a. Improvement of quality
attributes of sponge cake using infrared dried button mushroom. Journal of Food Science and
Technology. 53: 1418-1423.
16.Salehi, F., Kashaninejad, M., and Jafarianlari, A. 2017. Drying kinetics and characteristics of
combined infrared-vacuum drying of button mushroom slices. Heat Mass Transfer. 53: 1751-
1759.
17.Salehi, F., and Razavi, S.M.A. 2016b. Modeling of waste brine nanofiltration process using
artificial neural network and adaptive neuro-fuzzy inference system. Desalination and Water
Treatment. 57: 14369-14378.
Food Processing and Preservation Journal
Volume 9, Issue 1
June 2017
Pages 85-96

Files

Share

How to cite

Statistics