Estimation of papaw (Carica papaw L.) moisture content using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm-artificial neural network (GA-ANN)

مجله پژوهشهای علوم و صنایع غذایی ایران

Article 3, Volume 12, Issue 6 - Serial Number 42, January and February 2017, Pages 767-779   

Document Type: Research Article-en

DOI: 10.22067/ifstrj.v12i6.62521

Author

Alireza Yousefi^*

University of Bonab

Abstract

Adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm-artificial neural network (GA-ANN) were used for modeling of the hot-air drying kinetics of papaw slices. The ANFIS and GA-ANN were fed with 3 inputs of drying time (0-320 min), drying temperature (40, 50 and 60 °C) and slice thickness (3, 5 and 7 mm) for prediction of moisture ratio (MR). The triangular membership functions (MFs) were applied and 27 rules were provided for the ANFIS designing. The developed ANFIS predictions were close to the experimental data (R2 = 0.9967 and RMSE = 0.0161). The optimized GA-ANN, which included 7 hidden neurons, predicted the MR with a good precision (R2 = 0.9936 and RMSE = 0.0220). The effective diffusivity for papaw slices was within the range of 6.93 ×10-10 to 1.50×10-9 m2/s over the temperature range. The activation energy was found to be 32.5 kJ/mol indicating the effect of temperature on diffusivity.

Keywords

ANFIS; GA-ANN; Modeling; Papaw

References

Afzal, T., Abe, T. 1998. Diffusion in potato during far infrared radiation drying. Journal of food engineering. 37, 353-365.

Aghdam, S., Yousefi, A., Mohebbi, M., Razavi, S., Orooji, A., Akbarzadeh-Totonchi, M. 2015. Modeling for drying kinetics of papaya fruit using fuzzy logic table look-up scheme. International Food Research Journal. 22.

Akgun, N.A., Doymaz, I. 2005. Modelling of olive cake thin-layer drying process. Journal of Food Engineering. 68, 455-461.

Al-Mahasneh, M., Aljarrah, M., Rababah, T., Alu’datt, M. 2016. Application of Hybrid Neural Fuzzy System (ANFIS) in Food Processing and Technology. Food Engineering Reviews. 8, 351-366.

Bahramparvar, M., Salehi, F., Razavi, S. 2014. Predicting total acceptance of ice cream using artificial neural network. Journal of Food Processing and Preservation. 38, 1080-1088.

Bala, B., Ashraf, M., Uddin, M., Janjai, S. 2005. Experimental and neural network prediction of the performance of a solar tunnel drier for drying jackfruit bulbs and leather. Journal of Food Process Engineering. 28, 552-566.

Crank, J. 1975. The Mathematics of Diffusion: 2d Ed. Clarendon Press.

Demirtas, C., Ayhan, T., Kaygusuz, K. 1998. Drying behaviour of hazelnuts. Journal of the Science of Food and Agriculture. 76, 559-564.

Diamante, L., Munro, P. 1991. Mathematical modelling of hot air drying of sweet potato slices. International journal of food science & technology. 26, 99-109.

Doymaz, I. 2007. The kinetics of forced convective air-drying of pumpkin slices. Journal of food engineering. 79, 243-248.

Erenturk, K., Erenturk, S., Tabil, L.G. 2004. A comparative study for the estimation of dynamical drying behavior of Echinacea angustifolia: regression analysis and neural network. Computers and Electronics in Agriculture. 45, 71-90.

Falade, K.O., Solademi, O.J. 2010. Modelling of air drying of fresh and blanched sweet potato slices. International journal of food science & technology. 45, 278-288.

Fathi, M., Mohebbi, M., Razavi, S.M. 2011. Effect of osmotic dehydration and air drying on physicochemical properties of dried kiwifruit and modeling of dehydration process using neural network and genetic algorithm. Food and bioprocess technology. 4, 1519-1526.

Fernandes, F.A., Rodrigues, S., Gaspareto, O.C., Oliveira, E.L. 2006. Optimization of osmotic dehydration of papaya followed by air-drying. Food Research International. 39, 492-498.

Haghi, A., Amanifard, N. 2008. Analysis of heat and mass transfer during microwave drying of food products. Brazilian Journal of Chemical Engineering. 25, 491-501.

Irani, R., Nasimi, R. 2011. Evolving neural network using real coded genetic algorithm for permeability estimation of the reservoir. Expert Systems with Applications. 38, 9862-9866.

Izadifar, M., Mowla, D. 2003. Simulation of a cross-flow continuous fluidized bed dryer for paddy rice. Journal of Food Engineering. 58, 325-329.

Kaleemullah, S., Kailappan, R. 2005. Drying kinetics of red chillies in a rotary dryer. Biosystems Engineering. 92, 15-23.

Kaymak‐Ertekin, F. 2002. Drying and rehydrating kinetics of green and red peppers. Journal of Food Science. 67, 168-175.

Lenart, A. 1996. Osmo-convective drying of fruits and vegetables: technology and application. Drying technology. 14, 391-413.

Liu, Y.A., Baughman, D.R. 1995. Neural Networks in Bioprocessing and Chemical Engineering. Academic Press, Inc., San Diego.

Midilli, A., Kucuk, H., Yapar, Z. 2002. A new model for single-layer drying. Drying technology. 20, 1503-1513.

Mohebbi, M., Shahidi, F., Fathi, M., Ehtiati, A., Noshad, M. 2011. Prediction of moisture content in pre-osmosed and ultrasounded dried banana using genetic algorithm and neural network. Food and Bioproducts Processing. 89, 362-366.

Park, K.J., Vohnikova, Z., Brod, F.P.R. 2002. Evaluation of drying parameters and desorption isotherms of garden mint leaves (Mentha crispa L.). Journal of Food Engineering. 51, 193-199.

Prakash, O., Kumar, A. 2014. ANFIS modelling of a natural convection greenhouse drying system for jaggery: an experimental validation. International Journal of Sustainable Energy. 33, 316-335.

Sacilik, K., Keskin, R., Elicin, A.K. 2006. Mathematical modelling of solar tunnel drying of thin layer organic tomato. Journal of food Engineering. 73, 231-238.

Sadin, R., Chegini, G.-R., Sadin, H. 2014. The effect of temperature and slice thickness on drying kinetics tomato in the infrared dryer. Heat and Mass Transfer. 50, 501-507.

Salehi, F., Kashaninejad, M., MAHOONAK, A., Ziaiifar, A. 2017. Drying of Button Mushroom by Infrared-Hot Air System. Iranian Jounal of Food Science and Technology. 13, 151-159.

Simal, S., Mulet, A., Tarrazo, J., Rossello, C. 1996. Drying models for green peas. Food Chemistry. 55, 121-128.

Tao, Y., Li, Y., Zhou, R., Chu, D.-T., Su, L., Han, Y., Zhou, J. 2016. Neuro-fuzzy modeling to predict physicochemical and microbiological parameters of partially dried cherry tomato during storage: effects on water activity, temperature and storage time. Journal of food science and technology. 53, 3685-3694.

Tripathy, P., Kumar, S. 2009. Neural network approach for food temperature prediction during solar drying. International Journal of Thermal Sciences. 48, 1452-1459.

Vergara, F., Amezaga, E., Barcenas, M., Welti, J. 1997. Analysis of the drying processes of osmotically dehydrated apple using the characteristic curve model. Drying Technology. 15, 949-963.

Yolmeh, M., Najafi, M.B.H., Salehi, F. 2014. Genetic algorithm-artificial neural network and adaptive neuro-fuzzy inference system modeling of antibacterial activity of annatto dye on Salmonella enteritidis. Microbial pathogenesis. 67, 36-40.

Yousefi, A., Asadi, V., Nassiri, S.M., Niakousari, M., Aghdam, S.K. 2013a. Comparison of mathematical and neural network models in the estimation of papaya fruit moisture content. The Philippine Agricultural Scientist. 95.

Yousefi, A., Niakousari, M., Moradi, M. 2013b. Microwave assisted hot air drying of papaya (Carica papaya L.) pretreated in osmotic solution. African Journal of Agricultural Research. 8, 3229-3235.

Yousefi, A., Razavi, S.M. 2017. Modeling of glucose release from native and modified wheat starch gels during in vitro gastrointestinal digestion using artificial intelligence methods. International Journal of Biological Macromolecules. 97, 752-760.

Yuzgec, U., Becerikli, Y., Turker, M. 2009. Comparison of different modeling concepts for drying process of baker’s yeast. IEEE Transactions on Neural Networks. 19, 1231-1242.

Ziaforoughi, A., Yousefi, A.R., Razavi, S. 2016. A Comparative Modeling Study of Quince Infrared Drying and Evaluation of Quality Parameters. International Journal of Food Engineering. 12, 901-910.