Hamid Bakhshabadi

Modelling of mass transfer kinetic in osmotic dehydration of kiwifruit

Abstract Osmotic dehydration characteristics of kiwifruit were predicted by different activation functions of an artificial neural network. Osmotic solution concentration (y1), osmotic solution temperature (y2), and immersion time (y3) were considered as the input parameters and solid gain value (x1) and water loss value (x2) 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.

Screening of the aerodynamic and biophysical properties of barley malt

Abstract 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.

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 7days). The archived outcomes revealed that the highest quantity of starch has been observed in the malt resulted from 3days germination, and an enhancement in the germination period from 3 to 7days 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 7days after germination had the highest enzymatic activity(253U.kg-1). The comparison of data average using Duncan test showed that the minimum and maximum value of α-Amylase enzyme activity and diastatic power were recorded in the malts produced 3 and 7days 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 α-Amylase.

Screening of the alterations in qualitative characteristics of grape under the impacts of storage and harvest times using artificial neural network

The tested model showed that high reliability based on the obtained inputs for achieved data for RMSE and correlation coefficient between the obtained experimental and predicted values. An enhancement in the storage time, reduced the pH value and flavor index (fruit maturation), but boosted the acidity value of the fruits. On the other hand retardation in the harvest time led to an increase in pH value, total soluble solids and dextrose contents as well as flavor index of the samples. Artificial neural network design has been applied to predict the process of alterations during storage time. Back propagation feed forward neural network with the arrangement of 5:8:2 with a high correlation coefficient value (˃ 0.989) and low root mean square error value (< 0.0019) as well as sigmoid hyperbolic tangent activation function with Levenberg–Marquardt learning and learning cycle of 1000 were detected as the most reliable and appropriate neural network model in storage process.

The effect of ultrasound pretreatment on some selected physicochemical properties of black cumin (Nigella Sativa)

BackgroundIn 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.MethodsFor 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.ResultsThe 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).ConclusionsIn 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.