Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter October 16, 2013

Modeling of Basil Leaves Drying by GA–ANN

  • Amin Taheri-Garavand EMAIL logo , Shahin Rafiee , Alireza Keyhani and Payam Javadikia

Abstract

In this research, the experiment is done by a dryer. It could provide any desired drying air temperature between 20 and 120°C and air relative humidity between 5 and 95% and air velocity between 0.1 and 5.0 m/s with high accuracy, and the drying experiment was conducted at five air temperatures of 40, 50, 60, 70 and 80°C and at three relative humidity 20, 40 and 60% and air velocity of 1.5, 2 and 2.5 m/s to dry Basil leaves. Then with developed Program in MATLAB software and by Genetic Algorithm could find the best Feed-Forward Neural Network (FFNN) structure to model the moisture content of dried Basil in each condition; anyway the result of best network by GA had only one hidden layer with 11 neurons. This network could predict moisture content of dried basil leaves with correlation coefficient of 0.99.

References

1. OzcanM, ArslanD, UnverA. Effect of drying methods on the mineral content of basil (Ocimum basilicum L.). J Food Eng2005;69:3759.10.1016/j.jfoodeng.2004.08.030Search in Google Scholar

2. LoughrinJH, KasperbauerMJ. Aroma content of fresh basil (Ocimum basilicum l.) leaves is affected by light reflected from colored mulches. J Agric Food Chem2003;51:22726.10.1021/jf021076cSearch in Google Scholar PubMed

3. IzliN, IsikE. Determination of economic cost, vigour and rate of germination in batch drying of maize seeds. Int Agrophys2010;24:936.Search in Google Scholar

4. KarimiF. Applications of superheated steam for the drying of food products. Int Agrophys2010;24:195204.Search in Google Scholar

5. PietrzykW, SumorekA. Influence of electric field on wheat grain drying. Int Agrophys1999;13:24550.Search in Google Scholar

6. ZielinskaM, MarkowskiM. Air drying characteristics and effective moisture diffusivity of carrots. Chem Eng Process2010;49:21218.10.1016/j.cep.2009.12.005Search in Google Scholar

7. MisA, GrundasS. Influence of the moistening and drying of wheat grain on its hardness. Int Agrophys2004;18:4753.Search in Google Scholar

8. RayaguruK, RoutrayW. Effect of drying conditions on drying kinetics and quality of aromatic Pandanus amaryllifolius leaves. J Food Sci Technol2010;47:66873.10.1007/s13197-010-0114-1Search in Google Scholar PubMed PubMed Central

9. SouzaJS, MedeirosMF, MagalhãesMM, RodriguesS, FernandesFA. Optimization of osmotic dehydration of tomatoes in a ternary system followed by air-drying. J Food Eng2007;83:50109.10.1016/j.jfoodeng.2007.03.038Search in Google Scholar

10. MousaviM, JavanS. Modeling and simulation of apple drying, using artificial neural network and neuro – Taguchi’s method. J Agric Sci Technol2009;11:55971.Search in Google Scholar

11. NiH, GunasekaranS. Food quality prediction with neural networks. Food Technol1998;52:605.Search in Google Scholar

12. ChenCR, RamaswamyHS, AlliI. Prediction of quality changes during osmo-convective drying of blueberries using neural network models for process optimization. Dry Technol2001;19:50723.10.1081/DRT-100103931Search in Google Scholar

13. MouraCP, MassonML, YamamotoCI. Prediction of osmotic retreatment parameters using neural networks models for process optimization. Proceedings of the 14th International Drying Symposium, 22–25 August, São Paulo, Brazil, 2004:58996.Search in Google Scholar

14. OmidM, BaharlooeiA, AhmadiH. Modeling drying kinetics of pistachio nuts with multilayer feed–forward neural network. Dry Technol2009;10:106977.10.1080/07373930903218602Search in Google Scholar

15. AssidjoE, YaoB, KisselminaK, AmanéD. Modeling of an industrial drying process by artificial neural networks. Braz J Chem Eng2008;25:51522.10.1590/S0104-66322008000300009Search in Google Scholar

16. MovagharnejadK, NikzadM. Modeling of tomato drying using artificial neural network. Comput Electron Agric2007;59:7885.10.1016/j.compag.2007.05.003Search in Google Scholar

17. GorjianS, Tavakoli HashjinT, khoshtaghazaMH, SharafatAR. Designing and optimizing a BP neural network to model a thin-layer drying process. Proceedings of the 11th WSEAS International Conference on Neural Networks, Evolutionary Computing and Fuzzy Systems, Romania, 2010:509.Search in Google Scholar

18. MadadlouA, Emam-DjomehZ, Ebrahimzadeh MousaviM, EhsaniMR, Javan-mardM, SheehanD. Response surface optimization of an artificial neural network for predicting the size of re-assembled casein micelles. Comput Electron Agric2009;68:21621.10.1016/j.compag.2009.06.005Search in Google Scholar

19. NazghelichiT, AghbashloM, KianmehrMH. Optimization of an artificial neural network topology using coupled response surface methodology and genetic algorithm for fluidized bed drying. Comput Electron Agric2011;75:8491.10.1016/j.compag.2010.09.014Search in Google Scholar

20. LiuX, ChenX, WuW, PengG. A neural network for predicting moisture content of grain drying process using genetic algorithm. Food Control2007;18:92833.10.1016/j.foodcont.2006.05.010Search in Google Scholar

21. DemuthH, BealeM. Neural network toolbox for use with MATLAB. Natick, MA: The MathWorks, 2002.Search in Google Scholar

22. HauptRL, HauptSE. Practical genetic algorithms. Hoboken, NJ: John Wiley & Sons, 2004.Search in Google Scholar

23. FathiM, MohebbiM, RazaviSM. Application of image analysis and artificial neural network to predict mass transfer kinetics and color changes of osmotically dehydrated kiwifruit. Food Bioprocess Technol2009. DOI:10.1007/s11947-009-0222-y.Search in Google Scholar

24. HeckerlingPS, GerberBS, TapeTG, WigtonRS. Use of genetic algorithms for neural networks to predict community-acquired pneumonia. Artif Intell Med2004;30:7184.10.1016/S0933-3657(03)00065-4Search in Google Scholar

25. IzadifarM, Zolghadri JahromiM. Application of genetic algorithm for optimization of vegetable oil hydrogenation process. J Food Eng2007;78:18.10.1016/j.jfoodeng.2005.08.044Search in Google Scholar

26. MohebbiA, TaheriM, SoltaniA. A neural network forpredicting saturated liquid density using genetic algorithmfor pure and mixed refrigerants. Int J Refrig2008;31:131727.10.1016/j.ijrefrig.2008.04.008Search in Google Scholar

27. MohebbiM, ShahidiF, FathiM, EhtiatiA, NoshadM.Prediction of moisture content in pre-osmosed and ultrasounded dried banana using genetic algorithm and neural network. Food Prod Process2010. DOI:10.1016/j.fbp.2010.08.001.Search in Google Scholar

Published Online: 2013-10-16

©2013 by Walter de Gruyter Berlin / Boston

Downloaded on 6.5.2024 from https://www.degruyter.com/document/doi/10.1515/ijfe-2012-0224/html
Scroll to top button