A study on the energy and exergy of Ohmic heating (OH) process of sour orange juice using an artificial neural network (ANN) and response surface methodology (RSM).

The nonmodern statistical methods are often unusable for modeling complex and nonlinear calculations. Therefore, the present research modeled and investigated the energy and exergy of the ohmic heating process using an artificial neural network and response surface method (RSM). The radial basis function (RBF) and the multi-layer perceptron (MLP) networks were used for modeling using sigmoid, linear, and hyperbolic tangent activation functions. The input consisted of voltage gradient; weight loss percentage, duration ohmic, Input flow, Power consumption, electrical conductivity and system performance coefficient and the output included the energy efficiency, exergy efficiency, exergy loss, and improvement potential. The response surface method was also used to predict the data. According to the result, the best prediction amount for energy and exergy efficiencies, exergy loss and improvement potential were in RBF network by sigmoid activation function and after this network, RSM had the best amount for energy efficiency, Also for exergy efficiencies, exergy loss and improvement potential obtained acceptable results in MLP network by a linear activation function. The worst amount was at MLP network by tangent hyperbolic. In general, the neural network can have more ability than the response surface method.

The use of CT scan imaging technique to determine pear bruise level due to external loads.

X-ray computed tomography (CT) is an effective noninvasive tool to visualize fresh agricultural commodities' internal components and quality attributes, also imaging via X-ray CT is a non-destructive and well-developed method applied in measuring the internal effects of agricultural products. In the present research, 120 healthy pears with their health verification through the CT were selected. Next, 81 healthy pears were selected and subjected to quasi-static and dynamic loading. The impact of the incoming pressures was investigated within 5, 10, and 15 days of storage. After loading and storing with the use of CT method, the total volume and the bruise volume of the pears were measured and the ratio of the bruise volume to the volume of each pear was calculated. Quasi-static loads were pressurized over a period of two ways; the pressure of wide edge was exerted at three force levels of 70, 100, and 130 N while the pressure of the thin edge was applied at 15, 20, and 25 N. Dynamic loading was performed by utilizing a pendulum and 300, 350, and 400 g mass. The results of the experiments indicated that in the quasi-static loading, the maximum and minimum amounts of pear bruise were 45.138% and 0.094% of the fruit, respectively. Besides, in the case of thin edge pressures, the minimum and maximum bruise levels were 0.007% and 19.88%, respectively. These values were obtained through 5 and 15 days of storage, respectively. In the dynamic loading, the maximum and minimum amounts of pear bruise were 47.36% and 0.21% of the total fruit, respectively, occurring at 400 and 300 g mass impact.