Modeling of Button Mushroom Drying Process by Infrared System

Document Type : Research Article


1 Ph.D. Student, Faculty of Food Science and Technology, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

2 Associate Professor, Faculty of Food Science and Technology, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran


Button mushroom (Agaricus bisporus) as a food with high nutritional value, among the 25 species of edible mushroom, is allocated the about 40% of the market share. In this study, to drying and increased shelf life of the product, infrared radiation (IR) method was used. Thus the effects of infrared lamp power (150, 250 and 375 watts), the distance of sample from lamp (5, 10, 15 and 20 cm), samples thickness (0.5 and 1 cm) and time of 120 minute on drying of button mushroom were examined. The results of infrared drying of button mushroom showed that with increasing in lamp power and decreases in sample distance from the heat source, the drying rate increases. With increase in infrared power from 150 to 375 watts, drying rate 104.9% increased. By reducing the sample thickness from 1 to 0.5 cm, drying rate 15.8% increased. By increasing in drying process time, the samples weight loss was increased. In this study also, process modeling was done with the genetic algorithm–artificial neural network (GA-ANN) method with 4 inputs (power and lamp distance, sample thickness and drying time) and 1 output for prediction of weight reduction. The GA-ANN modeling results showed a network with 11 neurons in one hidden layer with using hyperbolic tangent activation function can be well predict the weight loss in button mushroom drying by infrared system (R=0.99). Sensitivity analysis results by optimum ANN showed the infrared lamp distance from mushroom slides was the most sensitive factor to controlling the weight loss of samples.


Main Subjects

[1] صالحی، ف.؛ عباسی شاهکوه، ز.؛ گودرزی، م. (1392) مدل‌سازی خشک‌کردن اسمزی زردآلو با استفاده از الگوریتم ژنتیک - شبکه عصبی مصنوعی. مجله علوم و فناوری غذایی (در دست چاپ).
[2] FAO, (2011), Statistical Database. Available:
[3] Kotwaliwale, N., Bakane, P., Verma A. (2007). Changes in textural and optical properties of Oyster mushroom during hot air drying. J. Food Eng., 78 (4), 1207-1211.
[4] Brennan, M., Le Port, G., Pulvirenti, A., Gormley, R. (2000). Post-harvest Treatment with Citric Acid or Hydrogen Peroxide to Extend the Shelf Life of Fresh Sliced Mushrooms, LWT-Food Sci. Technol., 33, 285-289.
[5] Angle, R.Y., Tamhane, D.V. (1974). Mushrooms: An exotic source of nutritious and palatable food. Indian Food Packer, 28(5), 22-28.
[6] Giri, S.K., Prasad, S. (2007). Drying kinetics and rehydration characteristics of microwave-vacuum and convective hot-air dried mushroom. J. Food Eng., 78 (2), 512-521.
[7] Singh, U., Jain, S., Doshi, A., Jain, H., Chahar, V. (2008). Effects of Pretreatments on Drying Characteristics of Button Mushroom. Int. J. Food Eng., 4(4), 1-21.
[8] Okos, M.R., Narsimharn, G., Singh, R.K., Witnauer, A.C. (1992). Food dehydration. in: D.R. Heldman and D.B. Lund (Eds.), Handbook of Food Engineering, New York, Marcel Dekker, pp 437–562.
[9] Jun, S., Krishnamurthy, K., Irudayaraj, J., Demirci, A. (2011). Fundamentals and theory of infrared radiation. in: Pan, Z. Atungulu, G.G. (Eds.), Infrared Heating for Food and Agricultural Processing, New York, CRC press, pp 1-18.
[10] Nimmol, C., Devahastin, S. (2011). Vacuum infrared drying. in: Pan, Z. Atungulu, G.G. (Eds.), Infrared Heating for Food and Agricultural Processing, New York, CRC press, pp141-168.
[11] Afzal M.T., Abe T., Hilida Y. (1999). Energy and quality Aspect during Combined FIR Convection Drying of Barely. J. Food Eng., 42, 177-188.
[12] Yolmeh, M., Habibi Najafi, M.B., Salehi, F. (2014). GA-ANN and ANFIS modeling of antibacterial activity of annatto dye on Salmonella enteritidis. Microb. Pathogenesis, 67, 36-40.
[13] Salehi, F., Razavi, S.M.A. (2012). Dynamic modeling of flux and total hydraulic resistance in nanofiltration treatment of regeneration waste brine using artificial neural network. Desalin. Water Treat., 41, 95-104.
 [14] BahramParvar, M., Salehi, F., Razavi, S.M.A. (2013). Predicting total acceptance of ice cream using artificial neural network. J. Food Process. Pres., 38(3), 1080-1088.
[15] Amiri Chayjan, R., Tabatabaei Bahrabad S. M. Rahimi S.F. (2013). Modeling infrared-covective drying of pistachio nuts under fixed and fluidized bed conditions. J. Food Process. Pres., 38 (3), 1224-1233.
[16] Hebbar, H.U., Vishwanathan, K.H., Ramesh, M.N. (2004). Development of combined infrared and hot air dryer for vegetables. J. Food Eng., 65, 557–563.
[17]  Erenturk, S., Erenturk, K. (2007). Comparison of genetic algorithm and neural network approaches for drying process of carrot. J. Food Eng., 78, 905-912.
[18] Lertworasirikul, S., Saetan, S. (2010). Artificial neural network modeling of mass transfer during osmotic dehydration of kaffir lime peel.  J. Food Eng., 98, 214–223.