Prediction of some physical and drying properties of terebinth frut (Pistacia atlantica L.) using artifical neural networks

• Autorzy: Kaveh M. , Chayjan R. A.
• Języki publikacji: EN

Abstrakty

EN

Background. Drying of terebinth fruit was conducted to provide microbiological stability, reduce product deterioration due to chemical reactions, facilitate storage and lower transportation costs. Because terebinth fruit is susceptible to heat, the selection of a suitable drying technology is a challenging task. Artificial neural networks (ANNs) are used as a nonlinear mapping structures for modelling and prediction of some physical and drying properties of terebinth fruit. Materiał and methods. Drying characteristics of terebinth fruit with an initial moisture content of 1.16 (d.b.) was studied in an infrared fluidized bed dryer. Different levels of air temperatures (40,55 and 70°C), air velocities (0.93, 1.76 and 2.6 m/s) and infrared (IR) radiation powers (500, 1000 and 1500 W) were applied. In the present study, the application of Artificial Neural NetWork (ANN) for predicting the drying moisture diffusivity, energy consumption, shrinkage, drying rate and moisture ratio (output parameter for ANN modelling) was investigated. Air temperature, air velocity, IR radiation and drying time were considered as input parameters. Results. The results revealed that to predict drying rate and moisture ratio a network with the TANSIG- -LOGSIG-TANSIG transfer function and Levenberg-Marquardt (LM) training algorithm made the most accurate predictions for the terebinth fruit drying. The best results for ANN at predications were R2 = 0.9678 for drying rate, R2 = 0.9945 for moisture ratio, R2 = 0.9857 for moisture diffusivity and R2 = 0.9893 for energy consumption. Conclusion. Results indicated that artificial neural network can be used as an altemative approach for modelling and predicting of terebinth fruit drying parameters with high correlation. Also ANN can be used in optimization of the process.

• Wydawca: -
• Czasopismo: Acta Scientiarum Polonorum. Technologia Alimentaria
• Rocznik: 2014
• Tom: 13
• Numer: 1
• Opis fizyczny: p.65-78,fig.,ref.

Twórcy

autor

Kaveh M.

• Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

autor

Chayjan R. A.

• Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

Bibliografia

• Aghbashlo M., Kianmehr M.H., Nazghelichi T., Rafiee S., 2011. Optimization of an artificial neural network topology for predicting drying kinetics of carrot cubes using combined response surface and genetic algorithm. Drying Technol. 29 (7), 770-779.
• Aghbashlo M., Mobli H., Rafiee S., Madadlou A., 2012. The use of artificial neural network to predict exergetic performance of spray drying process: A preliminary study. Comput. Electron. Agric. 88, 32-43.
• Ahrne L.M., Pereira N.R., Staack N., Floberg P., 2007. Microwave convective drying of plant foods at constant and variable microwave power. Drying Technol. 25 (7), 1149-1153.
• Amiri Chayjan R., Kaveh M., 2013. Physical parameters and kinetic modeling of fix and fluid bed drying of terebinth seeds. J. Food Process. Preserv. doi: 10.1111/ jfpp. 12092 [in press].
• Amiri Chayjan R., Amiri Parian J., Esns-Ashari M., 2011. Modeling of moisture diffusivity, activation energy and specific energy consumption of high moisture com in a fixed and fluidized bed convective dryer. Spanish J. Agric.Res. 9(1), 28-40.
• Amiri Chayjan R., Salari K., Barikloo H., 2012 a. Modelling moisture diffusivity of pomegranate seed cultivars under fixed, semi fluidized and fluidized bed using mathematical and neural network methods. Acta Sci. Pol., Technol. Aliment. 11 (2), 137-149.
• Amiri Chayjan R., Alizade H.H.A., Shadidi B., 2012 b. Modelling of some pistachio drying characteristics in fix, semi fluid and fluid bed dryer. Agric. Eng. Int. CIGR J. 14(2), 143-154.
• 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. J. Food Eng. 84 (4), 534-543.
• Chen D., Zheng Y., Zhu X., 2012. Determination of effective moisture diffusivity and drying kinetics for poplar sawdust by thermogravimetric analysis under isothermal condition. Biores. Technol. 107,451-455.
• Das I., Das S.K., Bal S., 2009. Drying kinetics of high moisture paddy undergoing vibration-assisted infrared (IR) drying. J. Food Eng. 95, 166-171.
• Demuth H., Beale M., Hagan M., 2007. Neural network toolbox 5. The MathWorks, Natick, MA, USA.
• Dissa A.O., Desmorieux H., Savadogo P.W., Segda B.G., Koulidiati J., 2010. Shrinkage, porosity and density behaviour during convective drying of spirulina. J. Food Eng. 97,410-418.
• Dondee S., Meeso N., Soponronnarit S., Siriamompun S., 2011. Reducing cracking and breakage of soybean grains under combined near-infrared radiation and fluid- ized-bed drying. J. Food Eng. 104, 6-13.
• Doymaz I., 2005. Influence of pretreatment solution on the drying of sour-cherry. J. Food Eng. 78, 591-596.
• Erenturk S., Erenturk K., 2007. Comparison of genetic algorithm and neural network approaches for the drying process of carrot. J. Food Eng. 78 (3), 905-912.
• Jha G.K., 2007. Artificial Neural Networks, [online], http:// www.iasri.res.in/ebook/EB_SMAR/e-book_pdf%20 files/Manual%20IV/3-ANN.pdf.
• Khir R., Pan Z., Salim A., Hartsough B.R., Mohamed S., 2011 Moisture diffusivity of rough rice under infrared radiation drying. LWT-Food Sci. Technol. 44, 1126-1132.
• Khoshhal A., Alizadeh Dakhel A., Etemad A., Zereshki S., 2010 Artificial neural network of apple drying process. J Food Process Eng. 33 (1), 298-313.
• Kurozawa L.E., Hubinger M.D., Park K.J., 2012. Glass transition phenomenon on shrinkage of papaya during convective drying. J. Food Eng. 108, 43-50.
• Madadlou A., Emam-Djomeh Z., Mousavi M.E., Ehsani M., Javanmard M., Sheehan D., 2009. Response surface optimization of an artificial neural network for predicting the size of re-assembled casein micelles. Comput. Electron. Agric. 68 (2), 216-221.
• Markowski M., Białobrzewski I., Modrzewska A., 2010. Kinetics of spouted-bed drying of barley: Diffusivities for sphere and ellipsoid. J. Food Eng. 96, 380-387.
• Meeso N. 2008. Far-infrared heating in paddy drying process. In New food engineering research trends. Ed. A.P. Urwaye. Nova Sci. Publ. New York, 225-256.
• Menlik T., Özdemir M.B., Kirmaci V., 2010. Determination of freeze-drying behaviors of apples by artificial neural network. Expert Syst. Applic. 37, 7669-7677.
• Mohebbi M., Shahidi F., Fathi M., Ehtiati A., Noshad M., 2010 Prediction of moisture content in pre-osmosed and ultrasounded dried banana using genetic algorithm and neural network. Food Bioprod. Process. 89, 362-366.
• Mundada M., Hathan B.S, Maske S., 2010. Convective de- hydration kinetics of osmotically pretreated pomegranate arils. Biosyst. Eng. 107, 307-316.
• Niamnuy C., Nachaisin M., Poomsa-ad N., Devahastin S., 2010 Kinetic modelling of drying and conversion/degradation of isoflavones during infrared drying of soybean. Food Chem. 133, 946-952.
• Nowacka M., Wiktor A., Sledz M., Jurek N., Witrowa-Rajchert M.D., 2012. Drying of ultrasound pretreated apple and its selected physical properties. J. Food Eng. 113, 427-433.
• 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.
• Omid M., Baharlooei A., Ahmadi H., 2009. Modeling drying kinetics of pistachio nuts with multilayer feed-forward neural network. Drying Technol. 27(10), 1069-1077.
• 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.
• Ruiz Celma A., López-Rodriguez F., Cuadros Blázquez F., 2009. Experimental modelling of infrared drying of industrial grape by-products. Food Bioprod. Process. 87, 247-253.
• Ruiz Celma A., Cuadros F., López-Rodriguez F., 2012. Convective drying characteristics of sludge from treatment plants in tomato processing industries. Food Bioprod. Process. 90, 224-234.
• Siriamornpun S., Kaisoon O., Meeso N., 2012. Changes in colour, antioxidant activities and carotenoids (lycopene, b-carotene, lutein) of marigold flower (Tagetes erecta L.) resulting from different drying processes. J. Funct. Foods 4, 757-766.
• Thuwapanichayanan R., Prachayawarakom S., Kunwisawa J., Soponronnarit S., 2011. Determination of effective moisture diffusivity and assessment of quality attributes of banana slices during drying. LWT-Food Sci. Technol. 44, 1502-1510.
• Xiao H.W., Pang C.L., Wang L.H., Bai J.W., Yang W.X, Gao Z.J., 2010. Drying kinetics and quality of Monukka seedless grapes dried in an air-impingement jet dryer. Biosyst. Eng. 105, 233-240.

Uwagi

Rekord w opracowaniu