Volume 17, Issue 106 (2020)                   FSCT 2020, 17(106): 23-31 | Back to browse issues page


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Amini G, Salehi F, Rasouli M. Drying process modeling of basil seed mucilage by infrared dryer using artificial neural network. FSCT 2020; 17 (106) :23-31
URL: http://fsct.modares.ac.ir/article-7-42900-en.html
1- MSc Student, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
2- Assistant Professor, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran , fs1446@yahoo.com
3- Assistant Professor, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
Abstract:   (1565 Views)
Today, plant and commercial gums are used to improve the rheological, textural and sensorial properties of food. Basil seeds have significant amounts of gum (mucilage) with good functional properties that after extracting from the seeds and drying, can be used as a powder in the formulation of various products. In this study, to drying of basil seed mucilage, infrared radiation (IR) method was used. The effect of infrared lamp power (150, 250 and 375 W), the distance of sample from lamp (4, 8 and 12 cm) and mucilage thickness (0.5, 1 and 1.5 cm) on drying time of basil seed mucilage were investigated. The results of basil seed mucilage drying using infrared method showed that with increasing lamp power and decreases in sample distance from the heat source, drying time was decreased. With increasing in the lamps distance from 4 to 12 cm, the average drying time of basil seed mucilage increased from 131.37 minutes to 336.41 minutes. With increasing sample thickness from 0.5 to 1.5 cm, the average drying time of basil seed mucilage increased from 103.67 to 367.67 minutes. The process was modeled by an artificial neural network with 3 inputs (lamp power, lamp distance and thickness) and 1 output (drying time). The results of artificial neural network modeling showed that a network with 8 neurons in a hidden layer and using the sigmoid activation function could predict the drying time of basil seed mucilage using the infrared dryer (r=0.96). The results of sensitivity analysis by optimal neural network showed that sample thickness is the most effective factor in controlling the drying time of basil seed mucilage.
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Article Type: Original Research | Subject: Statistics, modeling and response levels in the food industry
Received: 2020/05/15 | Accepted: 2020/07/18 | Published: 2020/11/30

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