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  The purpose of this study was prediction of thermal (effective moisture diffusivity and specific energy consumption), physical (shrinkage and color) and mechanical properties (rupture force) of terebinth fruit in a semi industrial continuous dryer using artificial neural networks (ANNs). Three effective factors on thermal, physical and mechanical properties, were air temperature, air velocity and belt linear speed as independent variables. Experiments were conducted with a semi industrial continuous dryer in temperature levels of 45, 60, 75 °C, air velocity levels of 1, 1.5 and 2 m/s and belt linear speed levels of 2.5, 6.5, 10.5 mm/s. Necessary data were collected using a the semi-industrial continuous dryer. Feed and cascade forward back propagation networks with learning algorithms of Levenberg-Marquardt and the Bayesian regulation were used to train the patterns. To predict the effective moisture diffusivity, feed forward networks with the Bayesian regulation, topology of 3-10-13-1 and 108 training cycles with R²=0.9999 was optimal arrangement. The optimal topology to predict the specific energy consumption was 3-10-1 with feed forward network, Levenberg-Marquardt algorithm, 117 training cycles and R²=0.9961. The best network for shrinkage prediction was feed forward network with the Bayesian regulation algorithm, topology of 3-6-4-1, 101 training cycles and R²=0.9926. To predict the total color change, feed forward networks with the Levenberg-Marquardt algorithm, topology of 3-6-7-1, 24 training cycles and R²=0.9139 was the optimal arrangement. The best network to predict the rupture force was feed forward network trained with the Bayesian regulation, topology of 3-8-6-1, 69 training cycles and R²=0.9990.

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β-galactosidase is one of the most important and widely used enzymes in three areas of health, Food Industry and environment, so kinetic modeling of this enzyme could be playing an important role in the optimization of its industrial production process. First, in this study kinetic of β-galactosidase production by Bacillus licheniformis bacteria in batch fermentation was evaluated during 22 hours, in the range of 20-50 g/l of initial lactose concentration as a limiting substrate, at 30 ° C. Then, with the observation of inhibition at the highest concentration of this range, logistic and Haldane kinetic models were selected to model and determine the kinetic parameters of fermentation. These models were obtained a good approximation of the experimental results of substrate utilization in all phases and microbial growth data in the exponential growth phase and the stationary phase, but minor deviations of the experimental data were observed in the decelerating growth phase. In addition, β-galactosidase activity results were in good agreement with experimental data, and the maximum deviation in this data was observed in initial concentrations 30 and 40 g/l of substrate simultaneously with the end of the exponential phase and beginning of decelerating phase of microbial growth (The fourth hours of starting inoculum). The linear regressions between experimental data and results obtained from the models, in all initial concentration of lactose and for each variable biomass concentration, substrate concentration and enzyme activity, was more than 0.95.