Paper Information

Journal:   IRANIAN JOURNAL OF FOOD SCIENCE AND TECHNOLOGY   JANUARY 2017 , Volume 13 , Number 59; Page(s) 85 To 96.
 
Paper:  DETERMINATION OF OPTIMAL CONDITIONS USING RESPONSE SURFACE METHOD AND COMPARISION OF NAURAL NETWORK AND REGRESSION METHOD OF DRYING GAMMA IRRADIATED POTATO
 
Author(s):  YOOSEFIAN S.H.*, RAZDARI A.M., SEYHOON M., KIANI H.
 
* DEPARTMENT OF BIOSYSTEMS ENGINEERING, UNIVERSITY OF BU-ALI SINA, HAMADAN
 
Abstract: 

By irradiation as Food processing method, Food quality is maintained and control of microorganism causes prevention of corruption. Simultaneously, Methods of drying, which leads to maintaining the quality and texture, are used. In this study, optimal conditions were determined by drying gamma rays irradiated potatoes with response surface method. For sample, the source of 60 Co irradiation (Gamma Cell 220) was used. Samples with 0, 2, 5 and 8 kGy doses were irradiated. Drying using microwave power at 200, 400 and 600 W and in three thicknesses of 5, 7 and 9 mm of sample was performed. Optimal conditions for radiation dose of 5 kGy, sample thickness of 7 mm and a microwave power of 400 W and L*, a*, b*, color changing, hue angle, Croma and browning index were proposed to 57.87, -0.95, 42.19, 10.73, -1.53, 42.22 and 113.59, respectively. In addition to the effects of radiation variables, the thickness of the sample and the microwave drying effect on indicators such as L*, a*, b*, browning index, Croma and hue angle was investigated. With increasing irradiation dose, increasing the thickness of the sample and the microwave power, the color index decrease, Hue angle color and color density increases and decrease, respectively. Finally, using the neural network model, drying of irradiated potato was modeling and ability of the model to predict of the color changes in regression and response surface method was compared. In this comparison the neural network model was capable for prediction higher than the regression model (R2=0.9706).

 
Keyword(s): GAMMA RAY, DRYING, NEURAL NETWORK, RSM, REGRESION