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). |
- Aktaş, M., Sözen, A., Amini, A., Khanlari, A. 2017. Experimental analysis and CFD simulation of infrared apricot dryer with heat recovery. Drying Technology, 35(6), 766-783.
Amid, B.T., Mirhosseini, H. 2012. Influence of different purification and drying methods on rheological properties and viscoelastic behaviour of durian seed gum. Carbohydrate Polymers, 90(1), 452-461.
Bahramparvar, M., Salehi, F., Razavi, S. 2014. Predicting total acceptance of ice cream using artificial neural network. Journal of Food Processing and Preservation, 38(3), 1080–1088.
Cunha, R.L., Maialle, K.G., Menegalli, F.C. 2000. Evaluation of the drying process in spouted bed and spout fluidized bed of xanthan gum: focus on product quality. Powder Technology, 107(3), 234-242.
Doymaz, İ. 2012. Infrared drying of sweet potato (Ipomoea batatas L.) slices. Journal of Food Science and Technology, 49(6), 760-766.
Erenturk, S., Erenturk, K. 2007. Comparison of genetic algorithm and neural network approaches for the drying process of carrot. Journal of Food Engineering, 78(3), 905-912.
Hebbar, H.U., Vishwanathan, K., Ramesh, M. 2004. Development of combined infrared and hot air dryer for vegetables. Journal of Food Engineering, 65(4), 557-563.
Hosseini Ghaboos, S.H., Seyedain Ardabili, S.M., Kashaninejad, M., Asadi, G., Aalami, M. 2016. Changes in the physico-chemical and engineering parameters of pumpkin (C. moschata) with infrared drying method. Journal of Innovation in Food Science and Technology, 8(8), 93-102.
Lertworasirikul, S., Saetan, S. 2010. Artificial neural network modeling of mass transfer during osmotic dehydration of kaffir lime peel. Journal of Food Engineering, 98(2), 214-223.
Nep, E.I., Conway, B.R. 2011. Physicochemical characterization of grewia polysaccharide gum: Effect of drying method. Carbohydrate Polymers, 84(1), 446-453.
Nimmol, C. 2010. Vacuum far-infrared drying of foods and agricultural materials. The Journal of the King Mongkut’s University of Technology North Bangkok, 20, 37-44.
Pan, Z., Shih, C., McHugh, T.H., Hirschberg, E. 2008. Study of banana dehydration using sequential infrared radiation heating and freeze-drying. LWT-Food Science and Technology, 41(10), 1944-1951.
Rasouli, M. 2018. Convective drying of garlic (Allium sativum L.): Artificial neural networks approach for modeling the drying process. Iranian Food Science and Technology Research Journal, 14(3), 53-62.
Salehi, F. 2017. Rheological and physical properties and quality of the new formulation of apple cake with wild sage seed gum (Salvia macrosiphon). Journal of Food Measurement and Characterization, 11(4), 2006-2012.
Salehi, F. 2019a. Characterization of new biodegradable edible films and coatings based on seeds gum: A review. Journal of Packaging Technology and Research, 3(2), 193-201.
Salehi, F. 2019b. Improvement of gluten-free bread and cake properties using natural hydrocolloids: A review. Food science & nutrition, 7(11), 3391-3402.
Salehi, F. 2020a. Edible coating of fruits and vegetables using natural gums: A review. International Journal of Fruit Science, 1(1), 1-20.
Salehi, F. 2020b. Recent advances in the modeling and predicting quality parameters of fruits and vegetables during postharvest storage: a review. International Journal of Fruit Science, 1(1), 1-15.
Salehi, F. 2020c. Recent applications and potential of infrared dryer systems for drying various agricultural products: A review. International Journal of Fruit Science, 1-17.
Salehi, F., Abbasi Shahkoh, Z., Godarzi, M. 2015. Apricot osmotic drying modeling using genetic algorithm - artificial neural network. Journal of Innovation in Food Science and Technology, 7(1), 65-76.
Salehi, F., Kashaninejad, M. 2014. Effect of different drying methods on rheological and textural properties of balangu seed gum. Drying Technology, 32(6), 720-727.
Salehi, F., Kashaninejad, M. 2017. Effect of drying methods on textural and rheological properties of basil seed gum. International Food Research Journal, 24(5), 2090-2096.
Salehi, F., Razavi, S.M.A. 2012. Dynamic modeling of flux and total hydraulic resistance in nanofiltration treatment of regeneration waste brine using artificial neural networks. Desalination and Water Treatment, 41(1-3), 95-104.
Sundaram, J., Durance, T.D. 2008. Water sorption and physical properties of locust bean gum–pectin–starch composite gel dried using different drying methods. Food Hydrocolloids, 22(7), 1352-1361.
Wang, Y., Wang, L.-J., Li, D., Xue, J., Mao, Z.-H. 2009. Effects of drying methods on rheological properties of flaxseed gum. Carbohydrate Polymers, 78(2), 213-219.
Zameni, A., Kashaninejad, M., Aalami, M., Salehi, F. 2015. Effect of thermal and freezing treatments on rheological, textural and color properties of basil seed gum. Journal of Food Science and Technology, 52(9), 5914-5921.
Zhang, Y., Wang, S., Ji, G., Phillips, P. 2014. Fruit classification using computer vision and feedforward neural network. Journal of Food Engineering, 143, 167-177.
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