Volume 18, Issue 111 (2021)                   FSCT 2021, 18(111): 45-54 | Back to browse issues page

‎ 10.52547/fsct.18.111.45

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simintan khoubroo N, Asefi N. Effect of ultrasound and blanching on the quality, physical and thermodynamic properties of deep fried carrots and modeling of shrinkage by artificial neural network. FSCT 2021; 18 (111) :45-54
URL: http://fsct.modares.ac.ir/article-7-24210-en.html
Effect of ultrasound and blanching on the quality, physical and thermodynamic properties of deep fried carrots and modeling of shrinkage by artificial neural network
Nadia Simintan khoubroo1 , Narmela Asefi 2
1- no
2- Assistant Professor, Department of Food Engineering, Islamic Azad University, Tabriz Branch, Tabriz, Iran , n.asefi@iaut.ac.ir
Abstract:   (2346 Views)
Deep frying is a common practice in fast food preparation with optimal sensory properties. various factors such as process conditions (temperature and time), pH, physicochemical properties of the food, the origin of the oil, the chemical composition of the oil have affected on the amount of oil absorption. The purpose of this study was to investigate the ultrasound and blanching pre-treatment on the physical and thermodynamic quality of fried carrots under deep conditions. In this study, moisture content, oil content, bulk density, effective moisture diffusion coefficient were investigated and the rate of modeling of shrinkage was done by the neural network. The results showed that the use of a combination of ultrasound and blanching pretreatment resulted in reduced shrinkage, decreased oil absorption, increased apparent density at 160 ° C, and an increase in the effective emission factor of moisture content at 180 ° C. Moreover the modeling of neural network shrinkage showed a good correlation and overlap between the values predicted by the network versus actual values.
 
Keywords: Blanched pretreatment, Carrots, Neural network modeling, Ultrasound pretreatment
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Article Type: Original Research | Subject: Drying, frying, freezing, osmotic dehydration
Received: 2018/08/18 | Accepted: 2019/09/8 | Published: 2021/04/30
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