Browsing by Author "Bakhshabadi, Hamid"
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- Application of an adaptive neuro_fuzzy inference system (ANFIS) in the modeling of rapeseeds' oil extractionPublication . Farzaneh, Vahid; Bakhshabadi, Hamid; Gharekhani, Mehdi; Ganje, Mohammad; Farzaneh, Farahnaz; Rashidzadeh, Shilan; Carvalho, Isabel S.In the present study, the temperature and moisture content of the output seeds of the cooking pot were considered as inputs or independent variables and the insoluble fine partial content of the extracted oil, moisture content of the extracted oil and obtained meals, as well as the oil content of the achieved meals and acidity value of the extracted oil were considered as responses and were designed. Three different activation functions, including Gaussian membership and triangular as well as trapezoidal were applied and studied. The trapezoidal function with a 3-3 membership function for the three output variables including insoluble fine partials of oil, oil acidity and moisture content of the meals as well as the triangle membership function with 2-2 and 3-3 functions, respectively, for moisture content of the extracted oil and the oil content of the obtained meals were evaluated and detected as optimized models in the current study. The above mentioned models demonstrated higher correlation coefficients (R-2) between the experimental and predicted values and the lowest root mean squared errors, confirming the adaptability of the applied models in the present study. Practical applicationsToday, because of the high demand for crops for extensive application in the human diet, increases in the efficiency of the processing are attracting much more attention. In this regard, discovering and detection of the optimized conditions for processing with the minimum wastes looks very important and economic. Therefore, the uses of predictive methods in different food processes have been considered appropriate tools for improving of the efficiency of the processes as well as the enhancement of the quality of the produced products. In this respect, the ANFIS design as a novel predictive analytic tool, along with Response Surface Methodology (RSM) and Artificial Neural Network (ANN), are applied extensively. Thus regarding the above mentioned content, ANFIS design was applied to predict and optimize some of the selected physico-chemical properties of the extracted oil through the extraction process. Therefore prediction of the optimized conditions of oil extraction could improve the quality of the extracted oil and performance of the extraction process with the minimum wastes during short and logical extraction time.
- Modeling of the lycopene extraction from tomato pulpsPublication . Dolatabadi, Zahra; Rad, Amir Hossien Elhami; Farzaneh, Vahid; Feizabad, Seyed Hashem Akhlaghi; Estiri, Seyed Hossein; Bakhshabadi, HamidThe inputs of this network were the concentration of pectinase and time of incubation, and the outputs were extracted lycopene and the activity of radical scavenging activity. Two different networks were designed for the process under the sonication and without it. For optimal network, networks' transfer functions and different learning algorithms were evaluated and the validity of each one was determined. Consequently, the feedforward neural network with function of logarithmic transfer, Levenberg Marquardt algorithm and 4 neurons in the hidden layer with the correlation coefficient of 0.96 and 0.99 were respectively observed for the treatments under sonication and without it, furthermore, root mean squared error and standard error values were obtained 0.46 and 0.22 respectively for the treatments under sonication and 0.77 and 0.38 without it as respectively optimal networks. The selected networks could determine the chosen responses, individually and in combined effect of both inputs as well (R-2 > 0.98). (C) 2015 Elsevier Ltd. All rights reserved.
- Modelling of mass transfer kinetic in osmotic dehydration of kiwifruitPublication . Jabrayili, Sharokh; Farzaneh, Vahid; Zare, Zahra; Bakhshabadi, Hamid; Babazadeh, Zahra; Mokhtarian, Mohsen; Carvalho, Isabel Saraiva deOsmotic dehydration characteristics of kiwifruit were predicted by different activation functions of an artificial neural network. Osmotic solution concentration (y(1)), osmotic solution temperature (y(2)), and immersion time (y(3)) were considered as the input parameters and solid gain value (x(1)) and water loss value (x(2)) were selected as the outlet parameters of the network. The result showed that logarithm sigmoid activation function has greater performance than tangent hyperbolic activation function for the prediction of osmotic dehydration parameters of kiwifruit. The minimum mean relative error for the solid gain and water loss parameters with one hidden layer and 19 nods were 0.00574 and 0.0062% for logarithm sigmoid activation function, respectively, which introduced logarithm sigmoid function as a more appropriate tool in the prediction of the osmotic dehydration of kiwifruit slices. As a result, it is concluded that this network is capable in the prediction of solid gain and water loss parameters (responses) with the correlation coefficient values of 0.986 and 0.989, respectively.
- Modelling of the Selected Physical Properties of the Fava Bean with Various Moisture Contents UsingFuzzy Logic DesignPublication . Farzaneh, Vahid; Ghodsvali, Alireza; Bakhshabadi, Hamid; Ganje, Mohammad; Dolatabadi, Zahra; Carvalho, Isabel S.The current paper indicates the systematic determination of the optimal conditions for the selected physical properties of the fava bean. The effects of varying moisture content of the Barkat fava bean grown in Golestan, Iran, in the range of 9.3-31.3% (Input) on the 15 selected physical properties of the crop, including geometric values as such length; width; thickness; arithmetic and geometric mean diameter; sphericity index surface and the area of the image; gravity and frictional parameters like the weight of 1000 seeds; true density; bulk density; volume and porosity as well as friction (filling and vacating angle stability) as the outputs were predicted. Afterwards, a model relying on fuzzy logic for the prediction of the 15 outputs had been presented. To build the model, training and testing using experimental results from the Barkat fava bean were conducted. The data used as the input of the fuzzy logic model are arranged in a format of one input parameter that covers the percentage of the moisture contents of the beans. In relation to the varying moisture content (input), the outcomes (15 physical parameters) were predicted. The correlation coefficients obtained between the experimental and predicted outputs as well as the Mean Standard Deviation indicated the competence of fuzzy logic design in predicting the selected physical properties of fava bean seeds. Practical ApplicationToday, because of the high demand for crops to be used extensively in the human diet, enhancements in the efficiency of the processing are getting more attention. In this way, finding and/or the determination of the optimal conditions for processing with minimum waste looks very substantial. Therefore, the use of prediction methods in food processing is considered to be a tool for improving the efficiency and the quality of the produced products. In this regard, the fuzzy logic design as a novel prediction tool, along with response surface methodology (RSM) and Artificial Neural Network (ANN), are applied extensively. Therefore Fuzzy Logic Design is optimized to predict the some of the selected physical properties of fava bean, as a function of seed's moisture content. Therefore predicting the behavior of this crop against different moisture contents can improve the quality and performance of the products with the minimum wastes during very short time.(c) 2016 Wiley Periodicals, Inc
- Screening of the aerodynamic and biophysical properties of barley maltPublication . Ghodsvali, Alireza; Farzaneh, Vahid; Bakhshabadi, Hamid; Zare, Zahra; Karami, Zahra; Mokhtarian, Mohsen; Carvalho, Isabel Saraiva deAn understanding of the aerodynamic and biophysical properties of barley malt is necessary for the appropriate design of equipment for the handling, shipping, dehydration, grading, sorting and warehousing of this strategic crop. Malting is a complex biotechnological process that includes steeping; ger-mination and finally, the dehydration of cereal grains under controlled temperature and humidity conditions. In this investigation, the biophysical properties of barley malt were predicted using two models of artificial neural networks as well as response surface methodology. Stepping time and germination time were selected as the independent variables and 1 000 kernel weight, kernel density and terminal velocity were selected as the depen-dent variables (responses). The obtained outcomes showed that the artificial neural network model, with a logarithmic sigmoid activation function, presents more precise results than the response surface model in the prediction of the aerodynamic and biophysical properties of produced barley malt. This model presented the best result with 8 nodes in the hidden layer and significant correlation coefficient values of 0.783, 0.767 and 0.991 were obtained for responses one thousand kernel weight, kernel density, and terminal velocity, respectively. The outcomes indicated that this novel technique could be successfully applied in quantitative and qualitative monitoring within the malting process.
- The effect of microwave pretreatment on some physico-chemical properties and bioactivity of Black cumin seeds' oilPublication . Bakhshabadi, Hamid; Mirzaei, Habib; Ghodsvali, Alireza; Jafari, Seid Mandi; Ziaiifar, Aman Mohammad; Farzaneh, VahidIn the current study, different processing times including 90, 180 and 270 s and microwave powers including 180, 540 and 900W were applied for optimizing of the extraction process. After microwave pre-treatments, the oil seeds were extracted with screw press with different rates (11, 34 and 57 rpm), then parameters including extraction efficiency, oxidative stability, peroxide and acidity index, DPPH free radical scavenging activity as well as the refractive index of the extracted oil were studied. Statistical analysis and process optimization was performed with the use of response surface methodology (RSM). The results revealed that enhancement in the microwave power and the processing time increased extraction efficiency, acidity index and oil peroxide value, but it decreased the oxidative stability value of the achieved oil. The achieved results also showed up that the Studied parameters had no significant impacts on the refractive index; moreover the extraction efficiency was reduced with an enhancement in the rotational rate of the screw press. According to the process optimization results, it might be stated that with applying processing time for about 185.44S, microwave pretreatment of 718.65 Wand screw-rotation speed of the press of 11 rpm, the desired outcomes are reached. (C) 2016 Elsevier B.V. All rights reserved.
- The effect of ultrasound pretreatment on some selected physicochemical properties of black cumin (Nigella Sativa)Publication . Moghimi, Masoumeh; Farzaneh, Vahid; Bakhshabadi, HamidBackground In the present study, the effects of ultrasound pretreatment parameters including irradiation time and power on the quantity of the extracted phenolic compounds quantity as well as on some selected physicochemical properties of the extracted oils including oil extraction efficiency, acidity and peroxide values, color, and refractive index of the extracted oil of black cumin seeds with the use of cold press have been studied. Methods For each parameter, three different levels (30, 60, and 90 W) for the ultrasound power and (30, 45, and 60 min) and for the ultrasound irradiation time were studied. Each experiment was performed in three replications. Results The achieved results revealed that, with enhancements in the applied ultrasound power, the oil extraction efficiency, acidity value, total phenolic content, peroxide value, and color parameters increased significantly (P < 0.01). Enhancements in ultrasound irradiation time have not significantly increased the oil extraction efficiency, acidity value, total phenolic content, and peroxide value as well as the oil refractive index (P < 0.05). As the highest oil extraction efficiency (39.93%) was obtained from the seeds when the applied ultrasound power and time were 90 W and 60 min respectively, and the lowest acidity value of oil was achieved once the applied power and time of ultrasound were 30 W and 30 min respectively. The application of ultrasound as pretreatment has not shown any significant effects on the refractive index of the extracted oils (P > 0.05). Conclusions In summary, it could be mentioned that the application of ultrasound pretreatment in the oil extraction might improve the oil extraction efficiency, the extracted oil’s quality, and the extracted phenolic compounds content.
- The impact of germination time on the some selected parameters through malting processPublication . Farzaneh, Vahid; Ghodsvali, Alireza; Bakhshabadi, Hamid; Zare, Zahra; Carvalho, Isabel S.In the present study, the impacts of germination time on the enzymes activity attributed in malting and some polysaccharides contents of the malt prepared from the Joseph barley variety have been screened using a completely random design with three levels of germination time(3, 5 and 7 days). The archived outcomes revealed that the highest quantity of starch has been observed in the malt resulted from 3 days germination, and an enhancement in the germination period from 3 to 7 days decreased the quantity of available starch. An enhancement in the germination period presented a reduction in the beta-glucan quantity in the malting seeds. The malt produced 7 days after germination had the highest enzymatic activity(253 U. kg(-1)). The comparison of data average using Duncan test showed that the minimum and maximum value of alpha-Amylase enzyme activity and diastatic power were recorded in the malts produced 3 and 7 days after germination, respectively. Increasing in the germination time led to a reduction in malting efficiency, however the efficiency of the hot water extraction showed enhancement. The outcomes of the correlation between the studied parameters showed that the beta-glucan and starch quantities are negatively affected by the activities of beta-Glucanase and alpha-Amylase. (C) 2016 Elsevier B.V. All rights reserved.