Skip to main content

Advertisement

SpringerLink
Log in

Experimental and ANN modeling study on microwave dried onion slices

  • Original
  • Published:
Heat and Mass Transfer Aims and scope Submit manuscript

Abstract

The present work deals mainly with dehydration characteristics of onion slices. Microwave power levels of 100, 350, 550 and 750 W was practiced to dry onion slices with thicknesses of 2.5, 5, 7.5 and 10 mm. The results showed that moisture diffusivity and specific energy consumption of the process increased with both increasing microwave power and the samples thickness, and ranged from 0.82 × 10−8 to 6.13 × 10−8 m2 s−1 and from 0.82 to 5.43 MJ kg−1 water, respectively. The average activation energy varied in the range of 1.28–1.77. Furthermore, for simulation of drying process and to predict the moisture removal behavior of the samples, multi-layer feed-forward (MLF) artificial neural network (ANN) was employed. Practicing different networks and based on statistical parameters, the best topology, transfer functions and training algorithms were determined. The results revealed that, as a powerful tool, ANN modeling could be effectively used to predict drying kinetics and determine the moisture content of the samples.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Joardder MUH, Brown R, Kumar C, Karim MA (2015) Effect of cell wall properties on porosity and shrinkage of dried apple. Int J Food Prop 18:2327–2337

    Article  Google Scholar 

  2. Torki-Harchegani M, Ghanbarian D, Ghasemi Pirbalouti A, Sadeghi M (2016) Dehydration behaviour, mathematical modelling, energy efficiency and essential oil yield of pepper mint leaves undergoing microwave and hot air treatments. Renew Sust Energ Rev 58:407–418

    Article  Google Scholar 

  3. Vallespir F, Rodríguez Ó, Eim VS, Rosselló C, Simal S (2018) Freezing pre-treatments on the intensification of the drying process of vegetables with different structures. J Food Eng 239:83–91

    Article  Google Scholar 

  4. Barati E, Esfahani JA (2011) A new solution spprosch for simulation heat and mass transfer during convective drying of mango. J Food Eng 102:302–309

    Article  Google Scholar 

  5. Beigi M, Torki-Harchegani M, Tohidi M (2017) Experimental and ANN modeling investigation of energy traits for rough rice drying. Energy 141:2196–2205

    Article  Google Scholar 

  6. Tohidi M, Sadeghi M, Mousavi SR, Mireei SA (2012) Artificial neural network modeling of process and product indices in deep bed drying of rough rice. Turk J Agric For 36:738–748

    Google Scholar 

  7. Aghbashlo M, Hosseinpour S, Mujumdar AS (2015) Application of artificial neural networks (ANNs) in drying technology–a comprehensive review. Dry Technol 33:1397–1462

    Article  Google Scholar 

  8. Azadbakht M, Aghili H, Ziaratban A, Vahedi Torshizi M (2017) Application of artificial neural network method to exergy and energy analyses of fluidized bed dryer for potato cubes. Energy 120:947–958

    Article  Google Scholar 

  9. Ergün A, Ceylan İ, Acar B, Erkaymaz O (2017) Energy–exergy–ANN analyses of solar-assisted fluidized bed dryer. Dry Technol 35:1711–1720

    Article  Google Scholar 

  10. Liu Z-L, Bai J-W, Wang S-X, Meng J-S, Wang H, Yu X-L, Gao Z-J, Xiao H-W (2019) Prediction of energy and exergy of mushroom slices drying in hot air impingement dryer by artificial neural network. Dry Technol. https://doi.org/10.1080/07373937.2019.1607873

  11. Zare D, Naderi H, Ranjbaran M (2015) Energy and quality attributes of combined hot air/infrared drying of paddy. Dry Technol 33:570–582

    Article  Google Scholar 

  12. Aghbashlo M, Mobli H, Rafiee S, Madadlou A (2013) An artificial neural network for predicting the physicochemical properties of fish oil microcapsules obtained by spray drying. Food Sci Biotechnol 22:677–685

    Article  Google Scholar 

  13. Çakmak G, Yildiz C (2011) The prediction of seedy grape drying rate using a neural network method. Comput Electron Agric 75:132–138

    Article  Google Scholar 

  14. Omari A, Behroozi-Khazaei N, Sharifian F (2018) Drying kinetic and artificial neural network modeling of mushroom drying process in microwave-hot air dryer. J Food Process Eng 41. https://doi.org/10.1111/jfpe.12849

  15. Jahedi Rad S, Kaveh M, Rasooli Sharabiani V, Taghinezhad E (2018) Fuzzy logic, artificial neural network and mathematical model for prediction of white mulberry drying kinetics. Heat Mass Transf 54:3361–3374

    Article  Google Scholar 

  16. Darvishi H, Mohammadi P, Azadbakht M, Farhudi Z (2018) Effect of different drying conditions on the mass transfer characteristics of kiwi slices. J Agric Sci Technol 20:249–264

    Google Scholar 

  17. Darvishi H, Zarein M, Farhudi Z (2016) Energetic and exergetic performance analysis and modeling of drying kinetics of kiwi slices. J Food Sci Technol 53:2317–2333

    Article  Google Scholar 

  18. Arsalan D, Özcan MM (2010) Study the effect of sun, oven and microwave drying on quality of onion slice. LWT Food Sci Technol 43:1121–1127

    Article  Google Scholar 

  19. Cuccurullo G, Giordano L, Metallo A, Cinquanta L (2017) Influence of mode stirrer and air renewal om controlled microwave drying of slices zucchini. Biosyst Eng 158:95–101

    Article  Google Scholar 

  20. Wang J, Xi YS (2005) Drying characteristics and drying quality of carrot using a two-stage microwave process. J Food Eng 68:505–511

    Article  Google Scholar 

  21. Yan WQ, Zhang M, Huang LL, Tang J, Mujumdar AS, Sun JC (2010) Studies on different combined microwave drying of carrot pieces. Int J Food Sci Technol 45:2141–2148

    Article  Google Scholar 

  22. Dadali G, Apar DK, Özbek B (2007) Microwave drying kinetics of okra. Dry Technol 25:917–924

    Article  Google Scholar 

  23. Yan WQ, Zhang M, Huang LL, Mujumdar AS, Tang J (2013) Influence of microwave drying method on the characteristics of the sweet potato dices. J Food Process Preserv 37:662–669

    Article  Google Scholar 

  24. Beigi M (2017) Thin layer drying of wormwood (Artemisia absinthium L.) leaves: dehydration characteristics, rehydration capacity and energy consumption. Heat Mass Transf 53:2711–2718

    Article  Google Scholar 

  25. Crank J (1975) The mathematics of diffusion, 2nd edn. Oxford University Press, London

    Google Scholar 

  26. Azimi-Nejadian H, Hoseini SS (2019) Study the effect of microwave power and slices thickness on drying characteristics of potato. Heat Mass Transf 55:2921–2930

    Article  Google Scholar 

  27. Nazghelichi T, Aghbashlo M, Kianmehr MH, Omid M (2011) Prediction of energy and exergy of carrot cubes in a fluidized bed dryer by artificial neural networks. Dry Technol 29:295–307

    Article  Google Scholar 

  28. Süfer Ö, Sezar S, Demir H (2017) Thin layer mathematical modeling of convective, vacuum and microwave drying of intact and brined onion slices. J food process Preserv 41. https://doi.org/10.1111/jfpp.13239

  29. Sadin R, Chegini GR, Sadin H (2014) The effect of temperature and slice thickness on drying kinetics tomato in the infrared dryer. Heat Mass Transf 50:501–507

    Article  Google Scholar 

  30. Sadeghi M, Mirzabeigi Kesbi O, Mireei SA (2013) Mass transfer characteristics during convective, microwave and combined microwave-convective drying of lemon slices. J Sci Food Agric 93:471–478

    Article  Google Scholar 

  31. Azadbakht M, Tajari N (2015) The effect of thickness and power on Hayward variety in drying process f kiwifruit using microwave. Agric Eng Int CIGR J 17:300–305

    Google Scholar 

  32. Ghanbarian D, Baraani Dastjerdi M, Torki-Harchegani M (2016) Mass transfer characteristics of bisporus mushroom (Agaricus bisporus) slices during convective hot air drying. Heat Mass Transf 52:1081–1088

    Article  Google Scholar 

  33. Nguyen MH, Price WE (2007) Air-drying of banana: influence of experimental parameters, slab thickness, banana maturity and harvesting season. J Food Eng 79:200–207

    Article  Google Scholar 

  34. Darvishi H (2012) Energy consumption and mathematical modeling of microwave drying of potato slices. Agric Eng Int CIGR J 14:94–102

    Google Scholar 

  35. Beigi M (2018) Effect of infrared drying power on dehydration characteristics, energy consumption, and quality attributes of common wormwood (Artemisia absinthium L.) leaves. J Agric Sci Technol 20:709–718

    Google Scholar 

  36. Barzegar M, Zare D, Stroshine RL (2015) An integrated energy and quality approach to optimization of green peas drying in a hot air infrared-assisted vibratory bed dryer. J Food Eng 166:302–315

    Article  Google Scholar 

  37. Khoshtaghaza MH, Darvishi H, Minaei S (2015) Effects of microwave-fluidized bed drying on quality, energy consumption and drying kinetics of soybean kernels. J Food Sci Technol 52:4749–4760

    Article  Google Scholar 

  38. Martynenko A, Zheng W (2016) Electrohydrodynamic drying of apple slices: energy and quality aspects. J Food Eng 168:215–222

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Tiran Branch, Islamic Azad University, Tiran, Iran

    Mohsen Beigi

  2. Department of Electrical and Computer Engineering, Chaharmahal va Bakhtyari Branch, Technical and Vocational University (TVU), Shahrekord, Iran

    Mehdi Torki

Authors
  1. Mohsen Beigi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mehdi Torki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohsen Beigi.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Beigi, M., Torki, M. Experimental and ANN modeling study on microwave dried onion slices. Heat Mass Transfer 57, 787–796 (2021). https://doi.org/10.1007/s00231-020-02997-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00231-020-02997-5

Keywords

Search

Navigation