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).
Farhad Fatehi; Hadi Samimi Akhijahani
Abstract
Nowadays, in modern agriculture, the combination of image processing techniques and intelligent methods has been used to replace smart machine instead of humans. In this study, an artificial image processing and artificial neural network (ANN) method was used to classify strawberry fruit of Parus variety. ...
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Nowadays, in modern agriculture, the combination of image processing techniques and intelligent methods has been used to replace smart machine instead of humans. In this study, an artificial image processing and artificial neural network (ANN) method was used to classify strawberry fruit of Parus variety. In the first step, the fruit was divided into 6 classes (ANN outputs) by the expert, and 100 samples were randomly collected from each class. In the next step, the images of the samples were captured and three geometric properties with twelve color properties (as ANN inputs) were extracted. Optimum artificial neural network structures considering root mean squared error (RMSE) and correlation coefficient (R2) were investigated to classification process of the strawberry samples. Finally, the perceptron neural network with a structure of 6-18-15 was selected with an average accuracy of 83.83%.
Mohammad Vahedi Torshizi; Mohsen Azadbakht
Abstract
This study evaluated the effect of different dynamic and static loadings and different storage periods on the firmness of pear fruit. Pear fruit was first segregated into three groups of 27 pear in order to undergo three loadings: static thin-edge compression loading, static wide-edge compression loading ...
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This study evaluated the effect of different dynamic and static loadings and different storage periods on the firmness of pear fruit. Pear fruit was first segregated into three groups of 27 pear in order to undergo three loadings: static thin-edge compression loading, static wide-edge compression loading and dynamic loading. All loaded pears were stored in accordance with three storage period designs: 5-day storage, 10-day storage, and 15-day storage. Following each period, the variations of pear texture were scanned by using the CT-Scan technique as a non-destructive test. Then, the firmness of pear texture was measured using a penetrometer. Data were simulated and evaluated using MLP and RBF artificial neural networks. The results showed that with increasing storage time and loading force , the firmness significantly decreased (1% level) in all three types of loading, In addition, pear texture was destructed under dynamic compression loading in order to compare with other two loadings. Best value artificial neural network for wide edge loading (12 neuron-RBF) was (R2 Wide edge= 0.9738– RMSE Wide edge=0.3419- MAE Wide edge =0.268) and for thin edge loading (4 neuron-RBF) was (R2Thin edge = 0.9946– RMSE Thin edge =0.170977- MAE Thin edge =0.133), also for dynamic loading (8 neuron-RBF) was (R2 Dynamic loading = 0.9933– RMSE Dynamic loading =0.230- MAE Dynamic loading= 0.187).
Amir Gitiban; Narmela Asefi
Abstract
Introduction: Dried fruits are one of the most important non-oil 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 ...
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Introduction: Dried fruits are one of the most important non-oil 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 meta-innovative 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 non-random 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 non-effective 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 pre-treatment 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 Brookfield-based American LFRA-4500 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 3-7-2 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.
Zeynab Raftani Amiri; Hengameh Darzi Arbabi
Abstract
Thermal conductivity is an important property of juices in the prediction of heat- and mass-transfer coefficients and in the design of heat- and mass-transfer equipment for the fruit juice industry. An artificial neural network (ANN) was developed to predict thermal conductivity of pear juice. Temperature ...
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Thermal conductivity is an important property of juices in the prediction of heat- and mass-transfer coefficients and in the design of heat- and mass-transfer equipment for the fruit juice industry. An artificial neural network (ANN) was developed to predict thermal conductivity of pear juice. Temperature and concentration were input variables. Thermal conductivity of juices was outputs. The optimal ANN model consisted 2 hidden layers with 5 neurons in first hidden layer and the second one has only one neuron. The ANN model was able to predict thermal conductivity values which closely matched the experimental values by providing lowest mean square error (R2=0.999) compared to conventional and multivariable regression models. However this method also improves the problem of determining the hidden structure of the neural network layer by trial and error. It can be incorporated in heat transfer calculations during juices processing where temperature and concentration dependent thermal conductivity values are required.
Hossein Majidzadeh; Bagher Emadi; Abdolali Farzad
Abstract
In this study, the moisture content of kiwifruit in vacuum dryer was predicted usingartificial neural networks (ANN) method. The drying temperatures (50, 60 and 70ºC), vacuum pressures(500, 550 and 600 mmHg), thicknesses of kiwifruit slices (3, 5 and 7mm) and drying times were considered as the ...
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In this study, the moisture content of kiwifruit in vacuum dryer was predicted usingartificial neural networks (ANN) method. The drying temperatures (50, 60 and 70ºC), vacuum pressures(500, 550 and 600 mmHg), thicknesses of kiwifruit slices (3, 5 and 7mm) and drying times were considered as the independent input parameters and moisture content as the dependentparameter. Experimental data obtained from vacuum drying process, were used for training and testing the network. Several criteria such as training algorithm, learning rate, momentum coefficient, number of hidden layers, number of neurons in each hidden layer and activation function were given to improve the performance of the ANN. The total number of hidden layers and the number of neurons in each hidden layer were chosen by trial and error. The best training algorithm was LM with the least MSE value. Optimum values of learning rate and momentum for the ANN with GDM training algorithm were set at 0.2 and 0.05, respectively. The optimal topologies were 4-20-1 with Tansig activation function and MSE values of 0.0016 and 4-15-20-1 with Logsig activation function in both hidden layer and MSE values of 0.000147. The correlation between the predicted and experimental values in the optimal topologies was higher than 99.75%.
Mahood Sadeghi; Masoud Yavarmanesh; Mostafa Shahidi Noghabi
Abstract
Nowadays, it has demonstrated that viruses can be transmitted by water and foods. Therefore, it causes the research to develop for detecting different viruses in water and foods. Among foods, milk can transfer potentially pathogenic viruses. On the other hand, to achieve every method for recovery and ...
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Nowadays, it has demonstrated that viruses can be transmitted by water and foods. Therefore, it causes the research to develop for detecting different viruses in water and foods. Among foods, milk can transfer potentially pathogenic viruses. On the other hand, to achieve every method for recovery and extraction of viruses in raw milk it needs to know about impact of milk components on viruses. Artificial neural network (ANN) and Adaptive Nero Fuzzy Inference System (ANFIS) can help to estimate recovery efficiency of viruses in raw milk. The objective of this study was to evaluate the application of ANN and Adaptive Nero Fuzzy Inference System (ANFIS) to predict the impact of milk components on recovery and extraction of viral RNA in raw milk. Therefore, to run the model the amount of milk components (casein, whey protein, fat and lactose) and viral RNA extraction were as the input and the output of the network respectively. Also, to evaluate the efficiency of the network for the prediction, variables such as training, validating and test subsets as well as the hidden layers, transfer functions, learning rules and the hidden neurons were used. Based on the results, the best models in ANN were linear sigmoid transfer function, levenberg learning rule (r: 0.919) and linear sigmoid transfer function, levenberg learning rule (r: 0.956) for spiked model solution and (spiked – non spiked) model solution respectively and in Adaptive Nero Fuzzy Inference System (ANFIS) the best model were membership function Gaussian , Adaptive Nero Fuzzy Inference System (ANFIS) model TSK, linear tanh axon transfer functions and momentum learning rule (r: 0.879) and membership function Gaussian, Adaptive Nero Fuzzy Inference System (ANFIS) model TSK, linear axon transfer function, and step learning rule (r: 0.889) for spiked model solution and (spiked – non spiked) model solution respectively.
Emad Aydani; Mahdi Kashani-Nejad; Mohsen Mokhtarian; Hamid Bakhshabadi
Abstract
In this study, Response Surface Methodology (RSM) was used to optimize osmo-dehydration of orange slice. Effect of osmotic solution temperature in the range of 30 to 60 °C, immersion time from 0 to 300 min and sucrose concentration from 35 to 65 brix degree on water loss, solid gain, moisture content, ...
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In this study, Response Surface Methodology (RSM) was used to optimize osmo-dehydration of orange slice. Effect of osmotic solution temperature in the range of 30 to 60 °C, immersion time from 0 to 300 min and sucrose concentration from 35 to 65 brix degree on water loss, solid gain, moisture content, water loss to solid gain ratio and brix change were investigated by Central Composite Design (CCD). Applying response surface and contour plots optimum for osmotic dehydration were found to be at temperature of 30 °C, immersion time of 229.2 minute and sucrose concentration of 65%. At this optimum point, water loss, solid gain, WL/SG ratio, moisture content (dry base) and brix difference were found to be 30.316 (g/100 g initial sample), 13.51 (g/100 g initial sample), 2.45, 2.77 % and 15.79, respectively. The result of artificial neural network indicated that the perceptron neural network with one hidden layer is able to anticipate the dehydration characteristics. This network predicted solid gain and moisture content with 5 neuron per hidden layers with R2 values of 0.937 and 0.959, respectively and brix difference and water loss with 30 neuron per hidden layer with R2 values of 0.961 and 0.942, respectively.
Alireza Ghodsvali; Mohsen Mokhtarian; Hamid Bakhshabadi; Fatemeh Arabamerian
Abstract
Malting is a complex biotechnological process that includes steeping; germination and drying of cereal grains
under controlled conditions of temperature and humidity. In this research malting process parameters were
predict by modular neural network with different activation function included, logsig-logsig, ...
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Malting is a complex biotechnological process that includes steeping; germination and drying of cereal grains
under controlled conditions of temperature and humidity. In this research malting process parameters were
predict by modular neural network with different activation function included, logsig-logsig, tanh-tanh, logsigtanh,
logsig-identity and tanh-identity. Steeping time (x1) and germination time (x2) were used as input
parameters and hot water extract (y1), malting yield (y2) and enzyme activity (β-Gluconase) (y3) were selected as
output parameters. The results showed that using perceptron neural network with tanh-identity activation
function had the best result among all of activation functions to predict effective parameters of malting process.
As well, this network was able to predict hot water extract, malting yield and enzyme activity (β - Gluconase)
with R2 value of 1, 0.984 and 0.995, respectively.
