Arslan, O., Erzengin, M., Sinan , S. & Ozensoy, O. (2004). Purification of mulberry (Morus alba L.) polyphenol oxidase by affinity chromatography and investigation of its kinetic and electrophoretic properties, Food chemistry. 88(3): 479-484.
Amirnejat, H., & Khoshtaghaza, M. (2011). Mathematical modeling of drying thin layer of edible mushrooms. In: Fifth National Congress of Agricultural Engineering and Mechanisation. 61-53. (In Farsi)
Behroozie khazayi, N. (2007). Using artificial neural network to predict the quality parameters raisins. M.Sc thesis Mechanics of Agricultural Machinery. Faculty of Agriculture. Tarbiat Modarres University. (In Farsi)
Bonazzi, C. & Dumoulin, E. (2011). Quality Changes in Food Materials as Influenced by Drying Processes. Modern Drying Technology Volume 3: Product Quality and Formulation, First Edition. Wiley-VCH Verlag GmbH & Co. KGaA.
Bowers, J. A. & Shedrow, C.B. (2000). Predicting stream water quality using artificial neural networks.WSRC-MS-2000-00112.
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” Journal of Food Engineering. 84: 534–543.
Dayhoff, J. E. (1990). Neural Network Principles. Prentice-Hall International, U.S.A.
Duke, J. A. (1983). Handbook of Energy Crops, Centrer for New Crops & Plants Products, Purdue University.
Doymaz, I. 2004. Pretreatment effect on sun drying of mulberry fruits (Morus.alba). J. Food engineering. 65: 205-209.
Esmaeili Adabi, M., Nikbakht, A, M., Motevali, A. & Mousavi Seyedi,S, R. (2013). Investigation of Black Mulberry Drying Kinetics Applying Different Pretreatments. Journal of Agricultural Science and Technology. 61: 23-44.
Fazaeli, M., Emam, Z., Omid, M. & Kalbasi, A. (2013). Prediction of the Physicochemical Properties of Spray-Dried Black Mulberry (Morus nigra) Juice using Artificial Neural Networks. Food Bioprocess Technol. 6:585–590.
Guine, R. (2006). Influence of drying method on density and porosity of pears. Food and Bioproducts Processing. 84(3): 179-185.
Hornik, K., Stinchcombe, M. & White, H. (1989). "Multilayer Feed Forward Networks Are Universal Approximators", Neural Networks. 2: 359-366.
Hoseini, Z. (1994). Conventional methods in food analysis. Shiraz University Press. (In Farsi)
Hoseini, Z. (2000). Common methods for food analysis. Tabriz University Press. (In Farsi)
Jamshidi, N., Hoseinpoor, A., Zaki, H., & Forooghirad, A. (2014). The use of artificial neural networks in evaluating the Hayward variety kiwifruit firmness to sonication. In: Twenty-first National Congress of Food Science and Technology. Shiraz University. (In Farsi)
Kianmehr, M. H. & Aghbashlo, M. (2011). Prediction of carrot cubes drying kinetics during fluidized bed drying by artificial neural network. J Food Sci Technol. 48(5): 542–550.
Krulis, M., Kuhnert, S., Leiker, M. & Rohm, H. (2005). Influence of energy input and initial moisture on physical properties of microwave-vacuum dried strawberries.
Kassem, A.S. (1998). Comparative studies on thin layer drying models for wheat. 13th international congress on agricultural engineering, Morocco. 6: 2-6.
Khoshtaghaza, M. H., Hosseinzadeh, B., Fayyazi, A. & Amirnejat, H. (2015). Prediction of thin layer drying of edible mushroom moisture content by feed forward artificial neural networks method. Journal of Food Science and Technology. 50(13): 171-182. (In Farsi)
Lahsasni, S., M. Kouhila & M. Mahrouz. (2004). Thin layer convective solar drying and mathematical modeling of prickly pear peel (Opuntia ficus indica). Journal of Food Engineering. 29: 211-224.
Martin-Diana, A., Rico, D., Barat, J.M. & Barry-Ryan, C. (2009). Orange juices enriched with chitosan: Optimisation for extending the shelf-life. Innovative Food Science and Emerging Technologies. 10:590–600.
Mokhtarian, M. & Shafafi, M. (2012). CRM to help predict the kinetics of osmotic dehydration process of neural network intelligent tools in a static state. In: Journal of Food Science and Technology. 7 (1): 73-61 (In Farsi).
Mokhtarian, M. & Coushki, F. (2013). Estimation of tomato drying parameters using artificial neural networks. In: Journal of Food Science and Technology. 1(1): 74-61. (In Farsi)
Prats-Montalban, J.M. & Ferrer, A. (2008). "Integration of Color and Textural Information in Multivariate Image Analysis: Defect Detection and Classification Issues", Journal of Chemometrics. 21 (2): 10-23.
Petrucci, V., Canata, N., Bolin, H. R., Fuller, G. & Stafford, A. E. (1974). Use of oleic acid derivatives to accelerate drying of Thompson seedless grapes, J. American oil chemistry. 51: 77-80.
Rahman, M. S. (2007). Handbook of food preservation .2nd ed. CRC press. P. 408,409,420.
Rahman, M. S. & Perera, C. O. (1999). Drying and food preservation. In Handbook of food preservation. Marcel Dekker New York. 173-216.
Saini, R.S., Sharma, K.D., Dhankhar, O.P. & Kaushik, R. A. (2001). Laboratory manual of analytical techniquesin in Horticulture. Agrobios. Publisher India. 135P.
Schalkoff, R. J. (1997). Artificial neural networks, McGraw-Hill.
Sacmi, C. (1989). "From Technology Through Machinery to Kilns for SACMI Tile, Italy", SACMI Press.
Tzempelikos, D. A., Vouros, A. P., Bardakas, A. V., Filios, A. E. & Margaris, D. P. (2014). Case studies on the effect of the air drying conditions on the convective drying of quinces. Case Studies in Thermal Engineering, 3, 79–85.
Togrul, I. T. & pehlivan, D. (2004). Modelling of thin layer drying kinetic of some fruits under open-air sun drying process. Journal of Food Engineering. 65: 413-425.
Yaldiz, O. C. & Ertekinn, H. I. Uzun. (2001). Mathematical modeling of thin layer solar drying of sultana grapes. Energy. 26: 457-465.
Yilmaz, F., M. Yuksekkaya, S., Vardin, H. & Karaaslan, M. (2015). The effects of drying conditions on moisture
transfer and quality of pomegranate fruit leather (pestil), Journal of the Saudi Society of Agricultural Sciences
Zarein, M. & Jaliliantabar, F. (2014). ANN Modeling of White Mulberry Drying by Microwave Oven, Advances in Environmental Biology. 8(16): 172-178.
)
