@article { author = {Salehi, Fakhreddin and Satorabi, Maryam}, title = {Drying process modeling of peach slices coated with basil seed and xanthan gums by infrared system}, journal = {Food Research Journal}, volume = {32}, number = {3}, pages = {17-28}, year = {2022}, publisher = {University of Tabriz}, issn = {2008-515X}, eissn = {2676-5691}, doi = {10.22034/fr.2022.41996.1763}, abstract = {Introduction:One of the ways to reduce the drying time is to supply heat by infrared radiation. Infrared technique could be used as a substitution to the current drying techniques for manufacture a high quality dried hydrocolloids. Infrared technique has many advantages such as: high heat transfer rate, uniform heating, low processing time, high performance (80-90%), lower energy utilization, lower energy costs, and improving final products quality (Aktaş et al. 2017; Salehi 2020b). Edible coating applied to food slices prior to drying is a technology that can improve the nutritional and sensory qualities of dehydrated products. The edible coatings have been widely studied aiming to increase shelf life of minimally processed products and reduce the solids uptake during osmotic dehydration. Polysaccharide edible coatings present low water vapor barrier; however, they present good gas barrier properties, such as oxygen barrier, and could be used to minimize oxidative reactions in food during drying, pointing out the potential of using edible coatings prior to convective drying, since it could reduce undesirable changes due to large time of exposure of the food to oxygen (Fakhouri et al. 2007; Garcia et al. 2014; Salehi 2021; Satorabi et al. 2021). The performance of artificial neural networks (ANN) as an analytical alternative to conventional modeling techniques was reported by some researcher. They reported that this approach is able to estimated drying kinetics of various fruits and vegetables with 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; Zhang et al. 2014; Salehi 2020a). In this study, basil seed and xanthan gums were used to coating of peach slices during drying in the infrared system and the drying kinetics of the samples were investigated.Material and methods:Slices of peach (5 mm thick) were prepared with the aid of a cutter and a steel-made cutting tool, which was cylindrical in shape and pointed on one of the sides. Basil seeds was physically cleaned and all foreign stuffs were removed. Then, the pure basil seeds were immersed in water for 20 min at a seed/water relation of 1:20 at 25°C. In the next step, the gum was separated from the inflated seeds by passing the seeds through an extractor (Bellanzo BFP-1540 Juicer, China) with a rotating disc which scratches the mucilage layer on the seed surface. The initial moisture content (MC) of the basil seed gum was 99.4% (wet basis). Xanthan and basil seed gums were used to coat the fresh peach slices. A 0.6% (w/w) xanthan and basil seed gums solution were prepared at 25°C and then peach slices were immersed for 1 min in a aqueous solution. The coated peach slices were dried in an infrared dryer (infrared radiation lamp (NIR), Noor Lamp Company, Iran). The influence of infrared radiation power (at three levels 150, 250 and 375 W), and time (min) on drying kinetics of peach slices was examined. The weight changes of peach slices was measured by using Lutron GM-300p digital balance (Taiwan, the sensitivity of ±0.01 gr). All measurements were done in triplicate. In this study, the Neurosolution software (release 5, NeuroDimension, Inc., USA) was employed for making the ANN model. The experimental data order was first randomized and then total data were randomly separated into 3 partitions: training (20%), validating (20%), and testing data (60%). The testing data were used for prediction of the trained network performance on unseen data. In the hidden layers and output layer a hyperbolic tangent activation function was used. The Levenberg–Marquardt (LM) optimization method was applied to network training. Also, a sensitivity analysis was done to supply the measure of relative significance between the inputs of the ANN model and to show how the model output changed in response to input changes.Results and discussion:Fruits and vegetables drying is a commonly used process for improving product safety as it greatly decreases the microbial activity and enzymatic changes during the storage period, hence, increasing the shelf life of the product. In this study, the effect of coating variables (control, basil and xanthan) and infrared lamp power (150, 250 and 375 W) on drying time and moisture content of samples in three replications were investigated. The concentration of used gum was 0.6% (w/w), the thickness of peach slices were 0.5 cm, and the distance of samples from lamp were 10 cm. The results of peach samples drying by infrared method showed that with increase in lamps power the drying time decreases. Coating pretreatment increased the drying time of peaches and the drying time of samples coated with basil seed gum was longer. The average drying time of the control samples, coated with basil and xanthan gums was 52.78 min, 76.22 min and 62.00 min, respectively. This process was modeled by an artificial neural network with 3 inputs (radiation time, type of coating and radiation lamp power) and 1 output (moisture content). The results of artificial neural network modeling showed that the network with 7 neurons in a hidden layer and using the Hyperbolic tangent activation function can predict the moisture content of coated peach during drying using infrared dryer (r=0.999). Also, the values of mean squared error (MSE), normalized mean squared error (NMSE) and mean absolute error (MAE) for optimum network were 0.3123, 0.0004 and 0.4065, respectively.Conclusion:Polysaccharide-based edible coating can be useful as a pretreatment for drying, since they prevents the oxidation of nutritional compounds, thereby improving the quality of the dried product. In this study, the effects of polysaccharide coating (xanthan and basil seed gums) on the drying kinetics of peach slices were investigated. In addition, artificial neural network model was used for prediction of moisture content of coated peach slices in an infrared dryer. With increasing infrared intensity, due to the increase in peach slices temperature and increasing evaporation rate and the decrease in drying time, the specific energy for drying of peach slices decreases. The results of this study indicated that ANN approach could give good estimation of moisture content of coated peach slices by xanthan and basil seed gums during infrared drying.}, keywords = {Artificial neural network,Drying,Infrared radiation,Moisture content}, title_fa = {مدل‌سازی فرآیند خشک کردن برش‌های هلو پوشش‌ داده‌شده با صمغ‌های دانه ریحان و گزانتان با سامانه فروسرخ}, abstract_fa = {زمینه مطالعاتی: یکی از مهمترین روش‌های فرآوری سبزی‌ها و میوه‌ها در سرتاسر جهان خشک‌کردن آنها است که باعث تسهیل حمل‌ونقل، افزایش قابلیت نگهداری و کاهش فعالیت‌های میکروبی می‌گردد. در همین راستا استفاده از تابش فروسرخ باعث افزایش سرعت خشک‌کردن، حفظ کیفیت محصول نهایی و کاهش هزینه‎های فرآیند به دلیل کاهش مصرف انرژی می‎شود. پوشش‌دهی محصولات کشاورزی قبل از فرآیند خشک‌کردن نیز باعث بهبود کیفیت ظاهری محصول، و درنتیجه افزایش مشتری‌پسندی آن می‌شود. هدف: هدف از این پژوهش استفاده از پرتوهای فروسرخ جهت تسهیل فرآیند خشک‏کردن و صمغ‌های دانه ریحان و گزانتان جهت پوشش‌دهی و افزایش کیفیت برش‌های هلو هنگام خشک‌کردن، و بررسی سینتیک خشک شدن نمونه‌ها است. روش کار: در این مطالعه اثر متغیرهای پوشش‌دهی (شاهد، ریحان و گزانتان) و توان لامپ فروسرخ (150، 250 و 375 وات) بر زمان خشک شدن و محتوای رطوبت نمونه‌ها در سه تکرار مورد بررسی قرار گرفت. غلظت صمغ‌های استفاده شده 6/0 درصد (وزنی/وزنی)، ضخامت برش‌های هلو 5/0 سانتی‌متر و فاصله نمونه‌ها از لامپ برابر 10 سانتی‌متر در نظر گرفته شد. نتایج: نتایج نشان داد که با افزایش توان لامپ زمان خشک‌کردن کاهش می‌یابد. پیش تیمار پوشش‌دهی باعث افزایش زمان خشک‌کردن نمونه‌ها شد که زمان خشک شدن نمونه‌های پوشش داده‌شده با صمغ دانه ریحان طولانی‌تر بود. میانگین زمان خشک شدن نمونه‌های شاهد، پوشش داده‌شده با صمغ‌های ریحان و گزانتان به ترتیب برابر 78/52 دقیقه، 22/76 دقیقه و 00/62 دقیقه بود. این فرآیند توسط یک شبکه عصبی مصنوعی با 3 ورودی (زمان پرتودهی، نوع پوشش و توان لامپ پرتودهی) و 1 خروجی (محتوای رطوبت) مدل‌سازی شد. نتیجه‌گیری نهایی: بر اساس تحلیل‌های صورت گرفته روی داده‌های مدل‌سازی با استفاده از نرم‌افزار شبکه عصبی نروسولوشن، شبکه عصبی مصنوعی پرسپترون انتشار برگشتی با ساختار 1-7-3، با ضریب همبستگی 999/0 و مقدار میانگین مربعات خطای 3123/0 مناسب‌ترین شبکه برای تخمین محتوای رطوبت هلو پوشش داده‌شده هنگام خشک شدن درون خشک‌کن فروسرخ بود.}, keywords_fa = {پرتودهی فروسرخ,خشک کردن,شبکه عصبی مصنوعی,محتوای رطوبت}, url = {https://foodresearch.tabrizu.ac.ir/article_14732.html}, eprint = {https://foodresearch.tabrizu.ac.ir/article_14732_1b2a905f16beae0c8cacc38ab247afb3.pdf} }