Basil (Ocimum basilicum L.) is the mucilaginous native plant and its seeds have a high amount of mucilage (gums) with outstanding useful characteristics that are comparable with marketable industrial gums. BSM is a gum extracted from this seeds using water extraction (Salehi, 2019; Zameni et al., 2015). Major properties of BSM as a new source of gum have been newly reported (Salehi, 2020a).

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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 seeds and drying, can be used as a powder in formulation of various products. In this study, for basil seed mucilage drying, infrared radiation (IR) method was used. The effect of infrared lamp power (150, 250 and 375 W), distance of samples 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 increase in lamp power and decrease in sample distance from the heat source, drying time was decreased. With lamp distance increasing from 4 to 12 cm, the average drying time of basil seed mucilage increased from 131.37 minutes to 336.41 minutes. With sample thickness increasing 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 the network with 8 neurons in a hidden layer and using the sigmoid activation function could predict the drying time of basil seed mucilage using infrared dryer (r=0.96). The results of sensitivity analysis by optimal neural network showed that samples thickness is the most effective factor in controlling the drying time of basil seed mucilage.