Food Technology
Azadeh Ranjbar Nedamani
Abstract
In recent years, cold plasma is one of the expected alternatives for post-harvest treatments and post-harvest management of products. A surface discharge plasma system was used for investigating the destruction time of Bacillus cereus, Bacillus coagulans, Bacillus stearothermophilus, and Clostridium ...
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In recent years, cold plasma is one of the expected alternatives for post-harvest treatments and post-harvest management of products. A surface discharge plasma system was used for investigating the destruction time of Bacillus cereus, Bacillus coagulans, Bacillus stearothermophilus, and Clostridium botulinum in bottled milk. The simulation was performed by COMSOL a3.5 software for a two-dimensional geometry. The collected experimental data were simulated in COMSOL software. The k factor of microorganism deactivation data was used to validate the simulated data. Results showed that the production of reactive oxygen species during plasma treatment increases with time and extends to the entire container. The concentration of reactive oxygen species (at the output of the plasma probe) at the beginning of the production was high, and at the end when they leave the free surface of the milk, the concentration decreased. Increasing the initial temperature of milk sample, from 50 to 80℃, can cause significant changes in the amount of ozone from 125 mol/m3 to 266 mol/m3, respectively (p <0.05). However, voltage changes in these two temperatures did not show a significant effect on ozone concentration. Also, immediately upon the initiation of plasma treatment, plasma destruction begins where the concentration of active species is higher. It is shown that among the four studied bacteria, Bacillus stearothermophilus has the highest resistance against cold plasma, and after that other bacteria have shown similar resistance. Finally, it can be concluded that the deep plasma treatment in bottle can make it possible to overcome the surface limitation of cold plasma treatment.
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.
Elnaz Ghasemtabar; Amir Hossein Goli; Ali Nasirpour
Abstract
Milk and fruit juice due to the high nutritional value, are widely consumed by society’s people. Pomegranate juice in spite of nutritional value has oldness in consumption and milk-pomegranate juice product could be desirable specifically for the consumers who are keen to experience new formulation ...
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Milk and fruit juice due to the high nutritional value, are widely consumed by society’s people. Pomegranate juice in spite of nutritional value has oldness in consumption and milk-pomegranate juice product could be desirable specifically for the consumers who are keen to experience new formulation and taste. Milk-juice beverage is a type of acidified milk drink that one of the main problems in production of the beverage is its low pH. In this study, in order to optimize the milk-pomegranate juice beverage formulation, three factors of milk, pomegranate juice and pectin content were selected. Formulation optimization and determination of optimum levels of each factor was carried out using response surface methodology (RSM) and to study the effect of storage time, optimum milk-pomegranate juice formulations were stored in refrigerator for 42 days and their physicochemical properties were evaluated. The results showed that the effect of milk and pectin content and factors interaction were significant on sedimentation and separation responses. The treatment of 50% pomegranate juice, 20% milk, 30% water and 0.63% pectin had the most nutritional value and can be recommended to be produced. The results revealed that production of milk –pomegranate juice beverage without any sedimentation and seperation by using pectin as stabilizer is possible and the product storage in the refrigerator had no negative effect on its nutritional value.
Food Engineering
Seyed Mohammad Ali Razavi
Abstract
Viscosity (µ) and density (ρ) are important physical roperties for analysis of membrane processes performance and for designing a new membrane process. In addition, the energy requirement for fluid pumping is depend on these two physical properties magnitiude. In this study, firstly, the effects ...
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Viscosity (µ) and density (ρ) are important physical roperties for analysis of membrane processes performance and for designing a new membrane process. In addition, the energy requirement for fluid pumping is depend on these two physical properties magnitiude. In this study, firstly, the effects of different process factors such as transmembrane pressure (51, 101, 152, 203 and 253 kPa), temperature (30,40 and 50ºC) and the effects of physico-chemical properties such as milk pH (6.67, 6.43, 6.25 and 5.97), milk fat percent (0.09, 1.19, 2.4, 3.26) on the viscosity and density of permeate have been considered. Two linear multiple regression models were then developed by Sigmastat software for prediction of µ and ρ during milk ultrafiltration. The experimental results showed that µ and ρ decreased as fat percent or temperature increased. pH had no considerable effect on µ and ρ. Furtheremore, increasing transmembrane pressure to 152 kPa led to an increase in both µ and ρ, while further increasing to 253 kPa resulted in a decrease in both µ and ρ. The statistical modeling results showed that the viscosity is only significantly depend on temperature and there was an excellent agreement between actual and predicted data (R=0.976), whereas the density is siginificantly depends on both temperature and fat percent and there was a good agreement between experimental and predicted data (R=0.904).
