Study on infrared drying kinetics of quince slices and modelling of drying process using genetic algorithm-artificial neural networks (GA-ANNs)

Document Type : Research Article


1 Assistant professor, Faculty of Chemical Engineering, Bonab University

2 MSc student, Maragheh Branch, Islamic Azad University

3 PhD student, Ferdowsi University of Mashhad

4 Assistant professor, Bonab University


In this research infrared radiation was used for drying of quince slices. For this reason, the influence of drying temperature of 50, 60, 70 and 80 °C acquired by 51, 73, 98 and 12 W infrared lamp was investigated. Drying results showed that the drying rate increased with increasing temperature. The drying decreased up to 60% when temperature was increased from 50 to 80 °C. Affected by the lamp power from 51 to 125 W, the moisture content diminished from 453% (d.b.) to 16% (d.b.). Modeling of drying process using genetic algorithm-artificial neural networks (GA-ANNs) with 3 inputs (drying time, drying temperature and the slice enter temperature) and one output (the amount of moisture ratio (MR)) was done. The modeling results demonstrated a network with 7 neurons in hidden layer and tangent hyperbolic transport function could precisely predict the moisture content of slices during drying (R2 = 0.9997 and RMSE = 0.0044). This precision for optimized GA-ANNs was even higher than that of Midilli model -the best empirical model- (R2 = 0.9987-0.9994 and RMSE = 0.0068-0.0098) at all the temperatures tested. The results obtained from the sensitivity analysis by the optimized neural networks revealed that the center temperature of slices was the most pronounced factor (0.0044) to control the MR. Increase in temperature resulted in an increase in the effective diffusivity coefficient, so that this coefficient reached to 26.1×10-9 m2/s at 80 °C from the initial value of 10.8×10-9 m2/s at 50 °C. The activation energy (Ea) calculated for the quince slices were 28.68 kJ/mol.

Graphical Abstract

Study on infrared drying kinetics of quince slices and modelling of drying process using genetic algorithm-artificial neural networks (GA-ANNs)


  • Infrared radiation was used for drying of quince slices
  • GA-ANNs technique was applied for modeling of the drying process
  • GA-ANNs technique was more precise than the Midilli empirical model for prediction of moisture ratio
  • The center temperature of the quince slices was the most pronounced factor to control moisture ratio
  • Increase in temperature resulted in an increase in the effective diffusivity coefficient


Main Subjects

[1] Potter, D., et al. (2007). Phylogeny and classification of Rosaceae. Plant Syst. Evol., 266, 5-43.
[2] Yousefi, A.R., Niakousari, M., Moradi, M. (2013). Microwave assisted hot air drying of papaya (Carica papaya L.) pretreated in osmotic solution. African J. Agric. Res., 8, 3229-3235.
[3] Nowak, D., Lewicki P.P.,(2004). Infrared drying of apple slices. Innov. Sci.Eng. Technol., 5, 353-360.
[4] Doymaz, I. (2012). Drying of pomegranate seeds using infrared radiation. Food Sci. Biotechnol., 21, 1269-1275.
[5] Yousefi, A.R., Ghasemian, N., Salari, A. (2017). Infrared drying kinetics study of lime slices using hybrid GMDH-neural networks. Innov. Food Technol., 5, 91-105.
[6] Hebbar, H.U., Viahwanathan, K.H., Ramesh, M.N. (2004). Development ofcombined infrared and hot air dryer forvegetables. J. Food Eng., 65,557-563.
[7] Pokham, K., Meeso, N., Soponronnarit, S., Siriamornpun, S. (2012). Modeling of combined far-infrared radiation and drying of a ring shap-pineapple with/without shrinkage. Food Bioprod.Process, 90, 155-164.
[8] Niamnuy, M., Poomsa-ad N, Devahastin S. (2012). Kinietic modeling infrared drying conversion/degradation of isoflavones during infrared drying of soybean. Food Chem., 133, 946-952.
[9] Bi, J., Chen, Q., Zhou, Y., Liu, X., Wu, X., Chen, R. (2014). Optimization of short-and medium-wave infrared drying and quality evalution of jujube powder. Food Bioprocess Tech., 7, 2375-2387.
[10] Ziaforoughi, A., Yousefi, A.R., Razavi, S.M.A. (2016). A Comparative Modeling Study of Quince Infrared Drying and Evaluation of Quality Parameters. Int. J. Food Eng., 12, 901-910.
[11] Yousefi, A.R., Asadi, V., Nassiri, S.M., Niakousari, M., Khodabakhsh Aghdam, Sh. (2012). Comparison of mathematical and neural network models in the estimation of papaya fruit moisture content. Philipp. J. Agric. Sci., 95, 192-198.
[12] Yousefi, A. R., Razavi, S.M.A. (2016). Modeling of glucose release from native and modified wheat starch gels during in vitro gastrointestinal digestion using artificial intelligence methods. Int. J.Biol. Macromol., 97, 752-760.
[13] Yousefi, A.R. (2017). Estimation of papaw (Carica papaw L.) moisture content using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm-artificial neural network (GA-ANN). Iran. Food Sci. Technol. Res. J., 12, 767-779.
[14] Salehi, F., Gohari Ardabili, A., Nemati, A., Ltifi Drab, A. (2017). Modeling of strawberry drying process using infrared dryer by genetic algorithm–artificial neural network method. Iran. J. Food Sci. Technol., 69, 105-114.
[15] Yousefi, A.R., Ghasemian, N. (2017). Prediction of papaw moisture ratio during hot air-drying: GMDH vs. mathematical modeling. Int. Food Res. J., 24, 2347-2352.
‌[16] Crank, J.(1975).The mathematics of diffusion (2nd ed.). Oxford, UK: Clarendon Press.
[17] Simal, S., Mulet, A., Tarrazo, J., Rosello, C. (1996). Drying models for green peas. Food Chem., 55, 121-128.
[18] Vergara, F., Amezaga, E., Barcenas, M.E., Welti, J.(1997). Analysis of the drying processes of osmotically dehydrated apple using the characteristic curve model. Drying Technol., 15, 949-963.
[19] Haghi, A.K., Amanifard, N. (2008). Analysis of heat and mass transfer during microwave drying of food products. Brazil. J. Chem. Eng., 25, 491-501.
[20] Kaymak-Ertekin, F. (2002). Drying and rehydrating kinetics of green and red peppers. J. Food Sci., 67, 168–175.
[21] Sogi, D.S., Shivhare, U.S., Garg, S.K., Bawa, A.S. (2003). Water sorption isotherms and drying characteristics of tomato seeds. Biosys. Eng., 84: 297–301.
[22] Doymaz, I. (2007). The kinetics of forced convective air-drying of pumpkin slices. J. Food Eng., 79, 243-248.
[23] Zomorodian, A., Moradi, M. (2010). Mathematical modeling of forced convection thin layer solar drying for cuminum cyminum. J. Agric. Sci. Technol., 12, 401-408.
[24] Thorat, I.D., Mohapatra, D., Sutar, R., Kapdi, S., Jagtap, D.D. (2012). Mathematical modeling and experimental study on thin-layer vacuum drying of ginger (Zingiber Officinale R.) slices. Food Bioprocess Technol., 5, 1379-1383.
[25] Akpinar, E.K., Bicer, Y. (2006). Mathematical modeling and experimental study on thin layer drying of strawberry. Int. J. Food Eng., 2.
[26] Doymaz, I. (2012). Drying of pomegranate seeds using infrared radiation. Food Sci. Biotechnol., 21, 1269-1275.
[27] Bala, B.K., Ashraf, M.A., Udidin, M.A., Janjai, S. (2005). Experimental and neural network prediction of the performance of a solar tunnel drier for drying jackfruit bulbs and leather. J. Food Process Eng., 28, 552-566.
[28] Kerdpiboon, S., Kerr, W.L., Devahastin, S. (2006). Neural network prediction of physical property changes of dried carrot as a function of fractal dimension and moisture content. Food Res. Int., 39, 1110-1118.
[29] Madamba, P.S., Driscoll, R.H., Buckle, K.A. (1996). The thin-layer drying characteristics of garlic slices. J.Food Eng., 29, 75-97.
[30] Kaleemullah, S., Kailappanm, R. (2005). Drying kinetics of red chillies in a rotary dryer. Biosyst Eng., 92, 15-23.
[31]Sacilik, K., Keskin,R.andElicin, A.K. (2006). Mathematical modelling of solar tunnel drying of thin layer organic tomato. J. Food Eng., 73, 231-238.
[32] Park, K.J., Vohnikova, Z., Brod, F.P.R. (2002). Evaluation of drying parameters and desorption isotherms of garden mint leaves (Mentha crispa L.). J.Food Eng., 51, 193-199.