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Application of novel technologies such as microwave and pulsed electric fields (PEF) might increase the speed and efficiency of oil extraction. In the present research, PEF (3.25 kV/cm electric field intensity and 30 pulse number) and microwave (540 W for 180 s) pretreatments were used to study the process of oil extraction from black cumin (Nigella sativa) seeds. After applying the selected pretreatments, the oil of seeds was extracted with the use of a screw press and the extraction efficiency, refractive index, oil density, color index, oxidative stability, and chemical components of oil and protein of meal were evaluated. The achieved results expressed that PEF and microwave pretreatments increased the oil extraction efficiency and its oxidative stability. Different pretreatments didn't have any significant influence on the refractive index of black cumin seed oil (p>.05). When microwave and PEF were used, the oil density showed an enhancement as the following: 1.51% and 0.96%, respectively in comparison with the samples with no pretreatments. Evaluation of the extracted oils, using GC/MS analysis indicated that thymoquinone was the dominant phenolic component in the black cumin oil. Finally, the SEM analysis revealed that microwave and PEF can be useful in the extraction of oil from black cumin seeds since these treatments damaged cell walls and facilitated the oil extraction process.

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

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A b s t r a c t. 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; germination 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 dependent 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.