Modelling of the Selected Physical Properties of the Fava Bean with Various Moisture Contents UsingFuzzy Logic Design

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, Incinfo:eu-repo/semantics/publishedVersio

Screening of the aerodynamic and biophysical properties of barley malt

An 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 impact of germination time on the some selected parameters through malting process

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.Erasmus Mundus program SALAMinfo:eu-repo/semantics/publishedVersio

The effect of microwave pretreatment on some physico-chemical properties and bioactivity of Black cumin seeds' oil

In 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.Gorgan university of Agricultural Sciences and Natural Resource

Modeling of the lycopene extraction from tomato pulps

The 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