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Fuzzy logic, artificial neural network and mathematical model for prediction of white mulberry drying kinetics

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Abstract

The thin-layer convective- infrared drying behavior of white mulberry was experimentally studied at infrared power levels of 500, 1000 and 1500 W, drying air temperatures of 40, 55 and 70 °C and inlet drying air speeds of 0.4, 1 and 1.6 m/s. Drying rate raised with the rise of infrared power levels at a distinct air temperature and velocity and thus decreased the drying time. Five mathematical models describing thin-layer drying have been fitted to the drying data. Midlli et al. model could satisfactorily describe the convective-infrared drying of white mulberry fruit with the values of the correlation coefficient (R2=0.9986) and root mean square error of (RMSE= 0.04795). Artificial neural network (ANN) and fuzzy logic methods was desirably utilized for modeling output parameters (moisture ratio (MR)) regarding input parameters. Results showed that output parameters were more accurately predicted by fuzzy model than by the ANN and mathematical models. Correlation coefficient (R2) and RMSE generated by the fuzzy model (respectively 0.9996 and 0.01095) were higher than referred values for the ANN model (0.9990 and 0.01988 respectively).

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References

  1. Ercisli S, Orhan E (2007) Chemical composition of white (Morus alba), red (Morus rubra) and black (Morus nigra) mulberry fruits. Food Chem 103(4):1380–1384

    Article  Google Scholar 

  2. Wanyo P, Siriamornpun S, Meeso N (2011) Improvement of quality and antioxidant properties of dried mulberry leaves with combined far-infrared radiation and air convection in Thai tea process. Food Bioprod Process 89:22–30

    Article  Google Scholar 

  3. Li R, Fei J, Cai Y, Li Y, Feng J, Yao J (2009) Cellulose whiskers extracted from mulberry: a novel biomass production. Carbohydr Polym 76:94–99

    Article  Google Scholar 

  4. Kaya A, Aydin O, Akgun M (2011) Drying kinetics and moisture transfer parameters of hazelnut. J Food Process Preserv 35:714–721

    Article  Google Scholar 

  5. Doymaz I, Kipcak AS, Piskin S (2015) Characteristics of thin-layer infrared drying of green bean. Czech J Food Sci 33(1):83–90

    Article  Google Scholar 

  6. Nozad M, Khojastehpour M, Tabasizadeh M, Azizi M, Ashtiani SHM, Salarikia A (2016) Characterization of hot-air drying and infrared drying of spearmint (Mentha spicata L.) leaves. Food Measure 10(3):466–473

    Article  Google Scholar 

  7. Khir R, Pan Z, Salim A, Hartsough BR, Mohamed S (2011) Moisture diffusivity of rough rice under infrared radiation drying. LWT Food Sci Technol 44:1126–1132

    Article  Google Scholar 

  8. 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 

  9. Doymaz I (2014) Infrared drying of button mushroom slices. Food Sci Biotechnol 23(3):723–729

    Article  Google Scholar 

  10. Hirun S, Choi JH, Ayarungsaritkul J, Pawsaut C, Sutthiwanjampa C, Vuong QV, Chockchaisawasdee S, Heo YR, Scarlett CJ (2015) Optimization of far-infrared vacuum drying conditions for miang leaves (Camellia sinensis var. assamica) using response surface methodology. Food Sci Biotechnol 24(2):461–469

    Article  Google Scholar 

  11. Ghaboos SHH, Ardabili SMS, Kashaninejad M, Asadi G, Aalami M (2016) Combined infrared-vacuum drying of pumpkin slices. J Food Sci Technol 53(5):2380–2388

    Article  Google Scholar 

  12. Doymaz I, Karasu S, Baslar M (2016) Effects of infrared heating on drying kinetics, antioxidant activity, phenolic content, and color of jujube fruit. Food Measure 10(2):283–291

    Article  Google Scholar 

  13. Amiri Chayjan R, Kaveh M, Khayati S (2015) Modeling drying characteristics of hawthorn fruit under microwave- convective conditions. J Food Process Preserv 39:239–253

    Article  Google Scholar 

  14. Aghbashlo M (2016) Exergetic simulation of a combined infrared- convective drying process. Heat Mass Transf 52(4):829–844

    Article  Google Scholar 

  15. Jafari SM, Ganje M, Dehnad D, Ghanbari V (2016) Mathematical, fuzzy logic and artificial neural network modeling techniques to predict drying kinetics of onion. J Food Process Preserv 40(2):329–339

    Article  Google Scholar 

  16. Bahmani A, Jafari SM, Shahidi S, Dehnad D (2016) Mass transfer kinetics of eggplant during osmotic dehydration by neural networks. J Food Process Preserv 40(5):815–827

    Article  Google Scholar 

  17. Aghbashlo M, Hosseinpour S, Mujumdar AS (2015) Application of artificial neural networks (ANNs) in drying technology- a comprehensive review. Dry Technol 33(12):1397–1462

    Article  Google Scholar 

  18. Aghajani N, Kashaninejad M, Dehghani MA, Garmakhany MD (2012) Comparison between artificial neural networks and mathematical models for moisture ratio estimation in two varieties of green malt. Qual Assur Saf Crop Foods 4:93–101

    Article  Google Scholar 

  19. Aghbashlo M, Mobli H, Rafiee S, Madadlou A (2013) An artificial neural network for predicting the physiochemical properties of fish oil microcapsules obtained by spray drying. Food Sci Biotechnol 22(3):677–685

    Article  Google Scholar 

  20. Yu H, Wilamowski BM (2011). Levenberg–Marquardt Training Industrial Electronics Handbook. chapter 12, Editors: CRC, pp. 12–1 to 12–15

    Google Scholar 

  21. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  Google Scholar 

  22. Mohamed MT (2011) Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. Int J Rock Mech Min Sci 48:845–851

    Article  Google Scholar 

  23. Hasanipanah M, Armaghani DJ, Khamesi H, Amnieh HB, Ghoraba S (2016) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput 32(3):441–455

    Article  Google Scholar 

  24. Inan O, Arslan D, Taşdemir S, Özcan MM (2011) Application of fuzzy expert system approach on prediction of some quality characteristics of grape juice concentrate (Pekmez) after different heat treatments. J Food Sci Technol 48(4):423–431

    Article  Google Scholar 

  25. Tavakolipour H, Mokhtarian M, Kalbasi-Ashtari A (2014) Intelligent monitoring of zucchini drying process based on fuzzy expert engine and ANN. J Food Process Eng 37(5):474–481

    Article  Google Scholar 

  26. Nadian MH, Abbaspour-Fard MH, Martynenko A, Golzarian MR (2017) An intelligent integrated control of hybrid hot air-infrared dryer based on fuzzy logic and computer vision system. Comput Electron Agric 137:138–149

    Article  Google Scholar 

  27. Kaveh M, Amiri Chayjan R, Nikbakht AM (2017) Mass transfer characteristics of eggplant slices during length of continuous band dryer. Heat Mass Transf 53:2045–205956

    Article  Google Scholar 

  28. Silva BGD, Fileti AMF, Taranto OP (2015) Drying of brazilian pepper-tree fruits (Schinus terebinthifolius Raddi): development of classical models and artificial neural network approach. Chem Eng Commun 202:1089–1097

    Article  Google Scholar 

  29. Motavali A, Najafi GH, Abbasi S, Minaei S, Ghaderi A (2015) Microwave–vacuum drying of sour cherry: comparison of mathematical models and artificial neural networks. J Food Sci Technol 50(4):714–722

    Article  Google Scholar 

  30. Beigi M (2017) Thin layer drying of wormwood (Artemisia absinthium L.) leaves: dehydration characteristics, rehydration capacity and energy consumption. Heat Mass Transf 53(8):2711–2718

    Article  Google Scholar 

  31. Nadhari WNAW, Hashim R, Sulaiman O, Jumhuri N (2014) Drying kinetics of oil palm trunk waste in control atmosphere and open air convection drying. Int J Heat Mass Transf 68:14–20

    Article  Google Scholar 

  32. Parlak N (2015) Fluidized bed drying characteristics and modeling of ginger (zingiber officinale) slices. Heat Mass Transf 51(8):1085–1095

    Article  Google Scholar 

  33. Kaveh M, Abbaspour-Gilandeh Y, Chayjan RA, Taghinezhad E, Mohammadigol R (2018) Mass transfer, physical, and mechanical characteristics of terebinth fruit (Pistacia atlantica L.) under convective infrared microwave drying. Heat Mass Transf. https://doi.org/10.1007/s00231-018-2287-5

    Article  Google Scholar 

  34. Ghnimi T, Hassini L, Bagane M (2017) Experimental study of water desorption isotherms and thin-layer convective drying kinetics of bay laurel leaves. Heat Mass Transf 52(12):2649–2659

    Article  Google Scholar 

  35. Amiri Chayjan R, Kaveh M, Khayati S (2017) Modeling some thermal and physical characteristics of terebinth fruit under semi industrial continuous drying. Food Measure 11(1):12–23

    Article  Google Scholar 

  36. Delgado T, Pereira JA, Baptista P, Casal S, Ramalhosa E (2014) Shell's influence on drying kinetics, color and volumetric shrinkage of castanea sativa mill. fruits. Food Res Int 55:426–435

    Article  Google Scholar 

  37. Kucerova I, Hubackova A, Rohlik BA, Banout J (2015) Mathematical modeling of thin-layer solar drying of eland (Taurotragus oryx) jerky. Int J Food Eng 11(2):229–242

    Google Scholar 

  38. Mota CL, Luciano C, Dias A, Barroca MJ, Guiné RPF (2010) Convective drying of onion: kinetics and nutritional evaluation. Food Bioprod Process 88:115–123

    Article  Google Scholar 

  39. Siles JA, Gonzalez-Tello P, Martin MA, Martin A (2015) Kinetics of alfalfa drying: simultaneous modeling of moisture content and temperature. Biosyst Eng 129:185–196

    Article  Google Scholar 

  40. Duc LA, Han JW, Keum DH (2011) Thin layer drying characteristic s of rape seed (Brassica napus L.). J Stored Prod Res 47:32–38

    Article  Google Scholar 

  41. Guine RPF, Pinho S, Barroca MJ (2011) Study of the convective drying of pumpkin (Cucurbita maxima). Food Bioprod Process 89:422–428

    Article  Google Scholar 

  42. Motevali A, Jafari H, Hashemi SJ (2017) Effect of IR intensity and air temperature on exergy and energy at hybrid infrared-hot air dryer. Chem Ind Chem Eng Q. https://doi.org/10.2298/CICEQ170123015M (In Press)

    Article  Google Scholar 

  43. Onwude DI, Hashim N, Abdan K, Janius R, Chen G (2018) Investigating the influence of novel drying methods on sweet potato (Ipomoea batatas L.): Kinetics, energy consumption, color, and microstructure. J Food Process Eng. doi:https://doi.org/10.1111/jfpe.12686 (In Press)

  44. Nazghelichi T, Aghbashlo M, Kianmehr MH (2011) Optimization of an artificial neural network topology using coupled response surface methodology and genetic algorithm for fluidized bed drying. Comput Electron Agric 75:84–91

    Article  Google Scholar 

  45. Motevali A, Minaei S, Banakar A, Ghobadian B, Khoshtaghaza MH (2014) Comparison of energy parameters in various dryers. Energy Convers Manag 87:711–725

    Article  Google Scholar 

  46. Li X, Xie X, Zhang C, Zhen S, Jia W (2018) Role of mid- and far- infrared for improving dehydration efficiency in beef jerky drying. Dry Technol 36(3):283–293

    Article  Google Scholar 

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

    Article  Google Scholar 

  48. Kaveh M, Chayjan RA, Khezri B (2018) Modeling drying properties of pistachio nuts, squash and cantaloupe seeds under fixed and fluidized bed using data-driven models and artificial neural networks. Int J Food Eng 14(1). https://doi.org/10.1515/ijfe-2017-0248

  49. Hasanipanah M, Jahed Armaghani D, Khamesi H, Bakhshandeh Amnieh H, Ghoraba S (2016) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput 32:441–455

    Article  Google Scholar 

  50. Bahadir Ozdemir M, Aktas M, Sevik S, Khanlari A (2017) Modeling of a convective-infrared kiwifruit drying process. Int J Hydrog Energy 42(8):18005–18013

    Article  Google Scholar 

  51. Armaghani DJ, Mohamad ET, Hajihassani M, Yagiz S, Motaghedi H (2016) Application of several nonlinear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances. Eng Comput 32(2):189–206

    Article  Google Scholar 

  52. Deepika S, Sutar PP (2018) Combining osmotic–steam blanching with infrared–microwave–hot air drying: Production of dried lemon (Citrus limon L.) slices and enzyme inactivation. Dry Technol. https://doi.org/10.1080/07373937.2017.1422744

    Article  Google Scholar 

  53. Aktas M, Khanlari A, Amini A (2017) Sevik S (2017) performance analysis of heat pump and infrared–heat pump drying of grated carrot using energy- exergy methodology. Energy Convers Manag 132:327–338

    Article  Google Scholar 

  54. Nuthong P, Achariyaviriya A, Namsanguan K, Achariyaviriya S (2011) Kinetics and modeling of whole longan with combined infrared and hot air. J Food Eng 102:233–239

    Article  Google Scholar 

  55. Ponkham K, Meeso N, Soponronnarit S, Siriamornpun S (2012) Modeling of combined far-infrared radiation and air drying of a ring shaped-pineapple with/without shrinkage. Food Bioprod Process 90:155–164

    Article  Google Scholar 

  56. Kaveh M, Amiri Chayjan R (2017) Modeling thin-layer drying of turnip slices under semi-industrial continuous band dryer. J Food Process Preserv 41(2):e12778

    Article  Google Scholar 

  57. Darvishi H, Zarein M, Minaei S, Khafajeh H (2014) Exergy and energy analysis, drying kinetics and mathematical modeling of white mulberry drying process. Int J Food Eng 10(2):269–280

    Article  Google Scholar 

  58. Aktas M, Sevik S, Aktekeli B (2016) Development of heat pump and infrared-convective dryer and performance analysis for stale bread drying. Energy Convers Manag 113:82–94

    Article  Google Scholar 

  59. Onwude DI, Hashim N, Abdan K, Janius R, Chen G (2018) Modelling the mid-infrared drying of sweet potato: kinetics, mass and heat transfer parameters, and energy consumption. Heat Mass Transf. https://doi.org/10.1007/s00231-018-2338-y

    Article  Google Scholar 

  60. Puente-Díaz L, Ah-Hen K, Vega-Gálvez A, Lemus-Mondaca R, Scala KD (2013) Combined infrared-convective drying of murta (Ugni molinae Turcz) berries: kinetic modeling and quality assessment. Dry Technol 31:329–338

    Article  Google Scholar 

  61. Bezerra CV, Silva LHM, Corrêa DF, Rodrigues AMC (2015) A modeling study for moisture diffusivities and moisture transfer coefficients in drying of passion fruit peel. Int J Heat Mass Transf 85:750–755

    Article  Google Scholar 

  62. Zhang QA, Song Y, Wang X, Zhao WQ, Fan XH (2016) Mathematical modeling of debittered apricot (Prunus armeniaca L.) kernels during thin-layer drying. Cyta – J Food:14(4): 509–14(4): 517

  63. Motevali A, Tabatabaei SR (2017) A comparison between pollutants and greenhouse gas emissions from operation of different dryers based on energy consumption of power plants. J Clean Prod 154:445–461

    Article  Google Scholar 

  64. El-Mesery HS, Mwithiga G (2015) Performance of a convective, infrared and combined infrared- convective heated conveyor-belt dryer. J Food Sci Technol 52(5):2721–2730

    Article  Google Scholar 

  65. Salarikia A, Ashtiani SHM, Golzarian MR (2016) Comparison of drying characteristics and quality of peppermint leaves using different drying methods. J Food Process Preserv 41(3):e12930

    Article  Google Scholar 

  66. Banakar A, Zareiforoush H, Baigvand M, Montazeri M, Khodaei J, Behroozi-Khazaei N (2017) Combined application of decision tree and fuzzy logic techniques for intelligent grading of dried figs. J Food Process Eng 40(3):e12456

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Agriculture Science, Payame Noor University, Tehran, Iran

    Shahpour Jahedi Rad

  2. Young researchers club and elite, Sardasht Branch, Islamic Azad University, Sardasht, Iran

    Mohammad Kaveh

  3. Young researchers club and elite, Urmia Branch, Islamic Azad University, Urmia, Iran

    Mohammad Kaveh

  4. Department of Biosystem Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

    Vali Rasooli Sharabiani

  5. Moghan College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

    Ebrahim Taghinezhad

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  1. Shahpour Jahedi Rad
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  2. Mohammad Kaveh
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Jahedi Rad, S., Kaveh, M., Sharabiani, V.R. et al. Fuzzy logic, artificial neural network and mathematical model for prediction of white mulberry drying kinetics. Heat Mass Transfer 54, 3361–3374 (2018). https://doi.org/10.1007/s00231-018-2377-4

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