مدل‌سازی سفتی بافت و سینتیک خشک شدن میوه به در خشک‌کن همرفتی مادون قرمز توسط شبکه عصبی مصنوعی

به یکی از میوه‌های سرشار از مواد معدنی و ویتامین بوده و یکی از راهیهای نگهداری این محصول خشک کردن مییباشد. امروزه شبکهیهای عصبی مصنوعی در مد‌ل‌سازی پارامترهای خشک کردن در حال رشد و توسعه است. پژوهش حاضر با هدف مدل‌سازی سفتی بافت و زمان خشک شدن میوه به توسط شبکه عصبی انجام گردید. آزمایشیهای خشک کردن توسط خشک کن همرفتی مادون قرمز در سه توان 400، 800 و 1200 وات و جریان هوای ثابت 5/0 متر بر ثانیه تا رسیدن به رطوبت ثابت 22% بر پایه مرطوب، خشک گردید. به‌منظور مدل‌سازی از شبکه عصبی چندلایه (mlp) با توابع آستانه مختلف، تعداد نورون مختلف و الگوریتم آموزش لونبرگ – مارکوارت برای آموزش شبکهیها استفاده گردید. نتایج نشان داد که شبکه عصبی با ساختار (273) با توابع آستانه لگاریتمی با ضریب تعیین (9980/0 و 9867/0) به‌ترتیب برای زمان خشک شدن و سفتی بافت و مقدار میانگین مربعات خطا (008881/0 و 0009693/0) در مقایسه با سایر ساختارهای شبکه، نتایج بهتری را ارائه مییکند.

Modeling of hardness and drying kinetics of quince fruit drying in an infrared convection dryer using the artificial neural network

Introduction: Dried fruits are one of the most important nonoil exports and the efforts should be made to grow the economy of the country by increasing their exports to world markets. Meanwhile, quince juice contains various minerals including iron, phosphorus, calcium, potassium and rich in vitamins such as vitamins A, C and B vitamins. Drying of food is one of the ways to keep its quality and increase its shelflife. During this process, the removal of moisture through the simultaneous transfer of heat and mass occurs. By transferring heat from the environment to the foodstuff, the heat energy evaporates the surface moisture. The drying process has a great impact on the product. In recent years, new and innovative techniques have been considered that increase the drying rate and maintain the quality of the product and infrared drying is one of these novel techniques.. Infrared systems are emitting electromagnetic waves with a wavelength of 700 nm to 1 mm. The advantage of using infrared is to minimize waste and prevent product quality loss due to reduced drying time can be mentioned. The need to predict product quality in each process makes it necesary to model and discover the relationship between factors that can affect the final quality of the product. Artificial neural networks have been considered as a metainnovative algorithm for modeling and prediction, which can be favored by the ability of these networks to model and predict processes The complexity and discovery of nonrandom fluctuations in data and the ability to discover the interactions between variables, economical savings in the use and disconnection of classical model abusive constraints (Togrul et al., 2004), the ability to reduce The effect of noneffective variables on the model by setting internal parameters is the ability to predict the desired parameter variations with minimum parameters (Bowers et al., 2000).; ;Materials and methods: In this research, quince fruit (Variety of Isfahan) was purchased as the premium product of Isfahan Gardens and was kept at 0°C in the cold room prior to further experiments. The fruits were removed from the refrigerator one hour before processing and exposed to ambient temperature. After washing, surface moisture was removed by moisture absorbent paper and turned into slices with a constant thickness of 4 mm. The specimens were subjected to pretreatment with an osmotic solution (vacuum for 70 minutes at a temperature of 40 ° C for 5 hours). For drying the samples, an infrared convective dryer with three voltages (800.400 and 1200 watts) and a constant speed of 0.5 m / s was used. In this way, the samples were placed under infrared lamps on a plate made from a grid and the weight of the samples was measured in a scale of 10 minutes by means of a scale and recorded on the computer. In order to achieve stable conditions in the system, the dryer was switched on 30 minutes before the process. The distance between the samples and the infrared lamp was fixed in all treatments at 16 cm. The drying process continued to reach a moisture content of 0.22 basis. To perform a puncture tests, quince slices were used in a Brookfieldbased American LFRA4500 tissue analysis device. In order to model these parameters in the drying process, the results of examining the quality of the samples, including the firmness of the tissue as well as the drying time, were used as network outputs. The power, concentration and pressure parameters were considered as network inputs. In this research, a multilayer perceptron network (MLP) was used. Due to its simplicity and high precision, this model has a great application in modeling the drying of agricultural products. Many functions in transmitting numbers from the previous layer to the next layer may be used (Tripathy et al., 2008).; ;Result & discussion: The results indicated that the stiffness of the tissue is reduced in vacuum conditions with increased power. So, the least amount of stiffness was related to osmotic sample dried at 1200 watts. By increasing the infrared power, the stiffness of the tissue decreases, the reason for this is probably the volume increase phenomenon that occurs during the rapid evaporation of moisture through infrared rays from inside the tissue. The results showed that at the start of the drying process, due to the high moisture content of the product, the moisture loss rate is high. Gradually, with the advent of time and reduced initial moisture content, the rate of moisture reduction naturally decreases. At lower power, the drying time is longer and with increasing power, the drying time decreases due to the increase of the thermal gradient inside the product and consequently the increase in the rate of evaporation of the moisture content of the product. The results of this study showed that the neural artificial network, as a powerful tool, can estimate the stiffness parameters of the tissue and the drying time with high precision. The most suitable neural network structure to predict these parameters with a 372 topology along with logarithmic activation functions with a total explanation coefficient above 0.9923 represent the best results. Also, by increasing the drying capacity and using osmotic dehydration, the drying time and the stiffness of the tissue samples is decreased.