Food Engineering
Ghazale Amini; Fakhreddin Salehi; Majid Rasouli
Abstract
Introduction: The dispersion of water soluble hydrocolloids (gums) in the aqueous system provides great technical importance, because they can improve the gel or enhance the thickening properties of food products. Wild sage seeds have significant amounts of gum with good functional properties that after ...
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Introduction: The dispersion of water soluble hydrocolloids (gums) in the aqueous system provides great technical importance, because they can improve the gel or enhance the thickening properties of food products. Wild sage seeds have significant amounts of gum with good functional properties that after extracting from seeds (mucilage) and drying, can be used in formulation of various products (Salehi, 2017, 2020a). The physicochemical properties and rheological behaviour of seed gums depend on the method and condition of drying. Also, the color of dried product is an important quality factor, which is affected by drying conditions (Amid and Mirhosseini, 2012; Nep and Conway, 2011). For example, effect of different drying methods (oven drying (40-80°C), freeze drying and vacuum oven drying) on rheological behaviour, color and physicochemical characteristics of BSM were investigated by Salehi and Kashaninejad (2017). Drying is one of the simply available and the most common processing approach that has been used traditionally for preservation of food product. One of the best way to reduce the drying time is to use IR radiation heating. IR methods could be used as substitution to the current drying methods for producing high-quality dried hydrocolloids. IR heating has many advantages include high heat transfer rate, uniform heating, low processing time, high efficiency (80-90%), lower energy consumption, lower energy costs, and improves final product quality (Aktaş et al., 2017; Salehi, 2020c). The performance of artificial neural networks (ANN) as an analytical alternative to conventional modeling techniques was reported by some researchers. They reported that these approaches are able to estimate the drying kinetics of various fruits and vegetableswith high precision. It has been shown that nonlinear approaches based on ANN are far better in generalization and estimation in comparison to empirical models (Bahramparvar et al., 2014; Salehi, 2020b; Zhang et al., 2014). It is difficult to predict the combined effects of treatment time, IR power, lamp distance and mucilage thickness on drying kinetics (moisture content and moisture ratio) of fruits and vegetablesusing conventional models. Therefore, the target of this study was to investigate the effect of IR dryer parameters on moisture content and moisture ratio of wild sage seed mucilage during IR drying and studying the performance of ANN method for estimation of these parameters. Materials and methods: Wild sage seeds was physically cleaned and all foreign stuffs were removed. Then, the pure wild sage seeds were immersed in water for 20 min at a seed/water ratio of 1:20 at 25°C and pH = 7. In the next step, the gum was separated from the inflated seeds by passing the seeds through an extractor (M-J-376-N, Nikko Electric Industry Company, Iran) with a rotating disc which scratches the mucilage layer on the seed surface. The initial moisture content (MC) of WSSM was 99.4% (wet basis). Finally, the obtained WSSM was immediately placed into IR dryer. In this study, for wild sage 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 wild sage seed mucilage were investigated. Results and Discussion: The results of wild sage seed mucilage drying using infrared method presented that by increasing the lamp power and decreasing the sample distance from the heat source, drying time was decreased. With lamp distance increasing from 4 to 12 cm, the average drying time of wild sage seed mucilage increased from 72.04 minutes to 160.81 minutes. When it comes to sample thickness, we found that by increasing the thickness of mucilage (0.5 to 1.5 cm) drying time of sample increased from 55.59 to 173.67 min. The process was modeled by an artificial neural network with 4 inputs (radiation time, lamp power, lamp distance and thickness) and 2 output (moisture content (MC) and moisture ratio (MR)). The results presented that mucilage drying time significantly increased by decreasing power of lamp (375 up to 150 W) and increasing the heat source distance from sample (4 to12 cm). The results of artificial neural network modeling showed that the network with 8 neurons in a hidden layer and with using the sigmoid activation function could predict the moisture content and moisture ratio of wild sage seed mucilage during infrared drying in various times (r=0.974 for MC and r=0.997 for MR).
