isa hazbavi

Milk is the only known substance in nature that can provide the body with complete and balanced nutrition. Recombined milk is a milk replacement product. Recombined milk components are more easily adjustable than milk components. Using electrical conductivity, valuable information is available about the quality of different materials, including food. In addition, by this method, as a simple and practical tool, the quality of many foods can be controlled. In this study, Response Surface Methodology (RSM) was used for modeling and optimization of the electrical conductivity of recombined milk. The effects of protein (1, 2, 3, 4 and 5%), Lactose (4, 6, 8, 10 and 12%), fat (3 and 6%) and temperature (50, 55, 60, 65 and 70ºC) independent variables on electrical conductivity of recombined milk as dependent variables (response) were evaluated. Process variables were statistically significant (p<0.01) as quadratic regression model for this response. The R2 factor for the modeling of the electrical conductivity of Recombined milk was 0.92 by the response surface method. Optimum conditions for the electrical conductivity of the Recombined milk in the usual range (4 to 5.5 mS/cm) contained protein 3.4%, lactose 7.7%, fat 3% and temperature of 63.9 °C.

Ohmic heating, also known as Joule heating, is an electric heating method for food products. Ohmic heating is an advanced heating process in which foods act as electrical resistances. Its laboratory system usually involves electrodes that are in contact with food, whereby electricity is transmitted through the foodstuff by a voltage change. In this research, the effect of ohmic heating on the specific energy consumption, total color change (ΔE) and percentage of pH change of carrot juice in different voltage gradients (15, 20 and 25V/cm) using different electrodes (steel, copper, aluminum and zinc) were investigated. The results showed that with increasing the voltage gradient, the special energy consumption decreased. The copper electrode has the best performance in percentage of pH change, specific energy consumption and total color change (ΔE). The specific energy consumption was changed from 3.45 to 3.87 MJ/kg for the copper electrode on gradient of 25V/cm and the steel electrode on voltage gradient of 15V/cm, respectively. Ohmic heating, also known as Joule heating, is an electric heating method for food products. Ohmic heating is an advanced heating process in which foods act as electrical resistances. Its laboratory system usually involves electrodes that are in contact with food, whereby electricity is transmitted through the foodstuff by a voltage change. In this research, the effect of ohmic heating on the specific energy consumption, total color change (ΔE) and percentage of pH change of carrot juice in different voltage gradients (15, 20 and 25V/cm) using different electrodes (steel, copper, aluminum and zinc) were investigated. The results showed that with increasing the voltage gradient, the special energy consumption decreased. The copper electrode has the best performance in percentage of pH change, specific energy consumption and total color change (ΔE). The specific energy consumption was changed from 3.45 to 3.87 MJ/kg for the copper electrode on gradient of 25V/cm and the steel electrode on voltage gradient of 15V/cm, respectively. Ohmic heating, also known as Joule heating, is an electric heating method for food products. Ohmic heating is an advanced heating process in which foods act as electrical resistances. Its laboratory system usually involves electrodes that are in contact with food, whereby electricity is transmitted through the foodstuff by a voltage change. In this research, the effect of ohmic heating on the specific energy consumption, total color change (ΔE) and percentage of pH change of carrot juice in different voltage gradients (15, 20 and 25V/cm) using different electrodes (steel, copper, aluminum and zinc) were investigated. The results showed that with increasing the voltage gradient, the special energy consumption decreased. The copper electrode has the best performance in percentage of pH change, specific energy consumption and total color change (ΔE). The specific energy consumption was changed from 3.45 to 3.87 MJ/kg for the copper electrode on gradient of 25V/cm and the steel electrode on voltage gradient of 15V/cm, respectively. Ohmic heating, also known as Joule heating, is an electric heating method for food products. Ohmic heating is an advanced heating process in which foods act as electrical resistances. Its laboratory system usually involves electrodes that are in contact with food, whereby electricity is transmitted through the foodstuff by a voltage change. In this research, the effect of ohmic heating on the specific energy consumption, total color change (ΔE) and percentage of pH change of carrot juice in different voltage gradients (15, 20 and 25V/cm) using different electrodes (steel, copper, aluminum and zinc) were investigated. The results showed that with increasing the voltage gradient, the special energy consumption decreased. The copper electrode has the best performance in percentage of pH change, specific energy consumption and total color change (ΔE). The specific energy consumption was changed from 3.45 to 3.87 MJ/kg for the copper electrode on gradient of 25V/cm and the steel electrode on voltage gradient of 15V/cm, respectively. Ohmic heating, also known as Joule heating, is an electric heating method for food products. Ohmic heating is an advanced heating process in which foods act as electrical resistances. Its laboratory system usually involves electrodes that are in contact with food, whereby electricity is transmitted through the foodstuff by a voltage change. In this research, the effect of ohmic heating on the specific energy consumption, total color change (ΔE) and percentage of pH change of carrot juice in different voltage gradients (15, 20 and 25V/cm) using different electrodes (steel, copper, aluminum and zinc) were investigated. The results showed that with increasing the voltage gradient, the special energy consumption decreased. The copper electrode has the best performance in percentage of pH change, specific energy consumption and total color change (ΔE). The specific energy consumption was changed from 3.45 to 3.87 MJ/kg for the copper electrode on gradient of 25V/cm and the steel electrode on voltage gradient of 15V/cm, respectively. Ohmic heating, also known as Joule heating, is an electric heating method for food products. Ohmic heating is an advanced heating process in which foods act as electrical resistances. Its laboratory system usually involves electrodes that are in contact with food, whereby electricity is transmitted through the foodstuff by a voltage change. In this research, the effect of ohmic heating on the specific energy consumption, total color change (ΔE) and percentage of pH change of carrot juice in different voltage gradients (15, 20 and 25V/cm) using different electrodes (steel, copper, aluminum and zinc) were investigated. The results showed that with increasing the voltage gradient, the special energy consumption decreased. The copper electrode has the best performance in percentage of pH change, specific energy consumption and total color change (ΔE). The specific energy consumption was changed from 3.45 to 3.87 MJ/kg for the copper electrode on gradient of 25V/cm and the steel electrode on voltage gradient of 15V/cm, respectively.

Among the foods consumed on a daily basis, milk has the most appropriate and balanced ingredients, that is the reason milk called whole food. Milk is the only known substance in nature that can provide the human body with complete and balanced nutrition. Recombined milk is a milk replacement product. Recombined milk components are more easily adjustable than milk components. The electrical conductivity is referred to as conductivity of specific material against the electric current, which is expressed in micro Siemens units per cm (mS/cm). Using electrical conductivity, valuable information is available about the quality of different materials, including food. In addition, by this method, as a simple and practical tool, the quality of many foods can be controlled. The aim of this study was to investigate the electrical conductivity of recombined milk affected by temperature, protein percentage, and lactose content.

In order to investigate the effect of protein percentage (1, 2 and 3%) and percentage of lactose or sugar content (4, 6 and 8%) on the electrical conductivity of milk, pure dry milk powder without dietary supplementation was used. Lactose powder was used to increase the lactose content of dry milk powder. Sodium caseinate was used to increase the protein content of dry milk powder. Distilled water was used to increase the volume of samples. Total experiments were carried out at three temperature levels (50, 55 and 60 ºC). Data analysis was also done using SPSS 16 software.

The results showed that temperature, protein percentage, and lactose percentage had a significant effect on the electrical conductivity of recombined milk. The electrical conductivity of the recombined milk ranged from 2.31 to 5.7 mS/cm at 50°C, 8% lactose, 1% protein, and 60°C, 4% lactose, 3% protein, respectively. The greatest and least effect on the electrical conductivity of recombined milk was related to the effect of protein percentage and lactose percentage, respectively. By increasing the temperature, the electrical conductivity of the reconstituted milk has increased significantly. The greatest changes in electrical conductivity (16%) of recombined milk occurred by the influence of temperature factor in protein 1% and lactose 4% and its value ranged from 2.44 to 2.83 mS/cm. In addition, the lowest changes in electrical conductivity (6%) of recombined milk were obtained by the temperature factor of 3% protein and 8% lactose, and it was increased from 4.68 to 4.95 mS/cm. By increasing protein content, the electrical conductivity of recombined milk has increased significantly. The most changes in electrical conductivity (107%) of recombined milk occurred by the influence of protein percentage at 55 °C and 6% lactose and its value ranged from 2.42 to 5.6 mS/cm. In addition, the lowest changes in the electrical conductivity (100%) of reconstituted milk occurred by the influence of protein percentage at 55 °C and 4% lactose, and its content increased from 2.5 to 5 mS/cm 5. These results indicate that the protein percentage factor has the most effect on the electrical conductivity of recombined milk (compared to two temperature factors and lactose percentage). By increasing lactose content, the electrical conductivity of recombined milk has decreased significantly. The greatest changes in electrical conductivity (13%) of recombined milk occurred by the influence of lactose percentage at 60 °C and protein 3% and its content decreased from 5.7 to 4.95 mS/cm. Also, the smallest changes in electrical conductivity (1.5%) of reconstituted milk occurred by the influence of lactose percentage at 55 °C and 2% protein, and its content decreased from 5.55 to 4.48 mS/cm. The maximum and minimum amount of electrical conductivity of reconstituted milk was 5.7 mS/cm at 60°C, 4% lactose and 3% protein, and 2.31 mS/cm at 50°C, 8% lactose and 1% protein, respectively.

Drying of food products using microwave can be a good replacement to hot air dryers. In this study, Response Surface Methodology (RSM) was used for optimization of the conditions for microwave drying of persimmon slices. The effects of microwave power (300, 500 and 700 W) and slice thickness (3, 5 and 7mm) as independent variables on shrinkage percentage, processing time and total color change of persimmon as dependent variables (responses) were evaluated. All process variables were statistically significant as quadratic regression models for all responses. As microwave power increased, the shrinkage percentage and total color change of persimmon slice increased but processing time decreased. As the thickness of persimmon slice increased, the processing time and total color change of persimmon slice increased but shrinkage percentage decreased. The optimum conditions obtained for minimum shrinkage percentage, processing time and total color change were 3 mm as slice thickness and the microwave power of 312 W. In optimized condition, the shrinkage percentage, processing time and total color change of dried persimmon slices were 72.5 %, 5.97 min and 15.2, respectively.

In this study, the moisture content of kiwifruit in vacuum dryer was predicted using artificial neural networks (ANN) method. The protein (1, 2, 3 and 4%), lactose (4, 6, 8 and 10%), fat (3 and 6%) and temperature (50, 55, 60 and 65ºC) were considered as the independent input parameters and electrical conductivity of recombined milk as the dependent parameter. Experimental data obtained from electrical conductivity meter, were used for training and testing the network. In order to develop neural network firstly experimental data were randomly divided into three sets of training (70%), validating (15%) and testing model (15%).  In order to develop ANN models, we used multilayer perceptron with back propagation with momentum algorithm. MLP models trained as two, three and four layers. 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. The highest coefficient of determination (R2) and lowest mean squared error (MSE) were considered as the criterion for selecting the best network. The network having three layers with a topology of 4-4-1 had the best results in predicting the electrical conductivity of recombined milk. This network has two hidden layers with 8 neurons in the first hidden layer and 5 neurons in the second hidden layer.  For this network, R2 and MSE were 0.992 and 0.011, respectively. These results can be used in milk processing factories. The correlation between the predicted and experimental values in the optimal topologies was higher than 99%.

Ohmic Heating is an advanced temperature process in which foods act as electrical resistances. The purpose of this study was to investigate the changes in the processing time or concentration and energy consumption of carrot juice due to voltage gradient changes.In this study, the behavior of the ohmic heating of the carrot juice in different gradients of voltage 15, 20 and 25 V/cm using different electrodes steel, copper, aluminum and zinc were investigated.The results showed that the interaction between the electrode and the voltage gradient on the specific energy consumption and the processing time was significant with P<0.01. By increasing the voltage gradient from 15 to 25 V/cm, the minimm and maximm of heating time were 3.95 min for the steel electrode and 13.09 min for the aluminum electrode, respectively. The highest with 3.87 MJ/kg and the lowest with 3.45 MJ/kg of the specific energy of carrot juice were also obtained using a stainless steel electrode on the gradient of 15 V/cm and an copper electrode on the gradient voltage of 25 V/cm, respectively.

Groundwater resources along with surface water supply the needs for municipal, industrial and agriculture uses, and their quantity and quality should be investigated. Salinity is one of the most important parameters in assessing the quality of groundwater. In this study, application of artificial neural networks and Bayesian network in predicting the electrical conductivity in 8 observation wells in Mazandaran plain was investigated. For this purpose, hydrogen carbonate, chloride, sulfate, calcium and magnesium were selected as input and output parameters for electrical conductivity at monthly a scale during 2003-2013. The criteria of correlation coefficient, mean absolute error and Nash Sutcliff coefficient were used to evaluate the performance of the model. The results showed that artificial neural network model has the highest correlation coefficient (0.989), the lowest mean absolute error (0.019 ds/m) and the highest standard of Nash Sutcliffe (0.970) ranked the first priority in the validation phase. The results indicate acceptable capability of artificial neural network models to estimate the electrical conductivity of groundwater.