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This study examines the potential of applying computational intelligence modelling to describe the drying kinetics of persimmon fruit slices during vacuum drying (VD) and hot-air-drying (HAD) under different drying temperatures of 50 °C, 60 °C and 70 °C and samples thicknesses of 5 mm and 8 mm. Kinetic models were developed using selected thin layer models and computational intelligence methods including multi-layer feed-forward artificial neural network (ANN), support vector machine (SVM) and k-nearest neighbors (kNN). The statistical indicators of the coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the suitability of the models. The effective moisture diffusivity and activation energy varied between 1.417 × 10−9 m2/s and 1.925 × 10−8 m2/s and 34.1560 kJ/mol to 64.2895 kJ/mol, respectively. The thin-layer models illustrated that page and logarithmic model can adequately describe the drying kinetics of persimmon sliced samples with R2 values (>0.9900) and lowest RMSE (<0.0200). The ANN, SVM and kNN models showed R2 and RMSE values of 0.9994, 1.0000, 0.9327, 0.0124, 0.0004 and 0.1271, respectively. The validation results indicated good agreement between the predicted values obtained from the computational intelligence methods and the experimental moisture ratio data. Based on the study results, computational intelligence methods can reliably be used to describe the drying kinetics of persimmon fruit.

A b s t r a c t. This paper evaluate the use of a tangent curve mathematical model for representation of the mechanical behaviour of sunflower bulk seeds. Compression machine (Tempos Model 50, Czech Republic) and pressing vessel diameter 60 mm were used for the loading experiment. Varying forces between 50 and 130 kN and speeds ranging from 10, 50, and 100 mm min -1 were applied respectively on the bulk seeds with moisture content 12.37±0.38% w.b. The relationship between force and deformation curves of bulk seeds of pressing height 80 mm was described. The oil point strain was also determined from the different deformation values namely 30, 35, 40, and 45 mm at speed 10 mm min -1 . Based on the results obtained, model coefficients were determined for fitting the experimental load and deformation curves. The validity of these coefficients were dependent on the bulk seeds of pressing height, vessel diameter, maximum force 110 kN, and speed 10 mm min -1 , where optimal oil yield was observed. The oil point was detected at 45 mm deformation giving the strain value of 0.56 with the corresponding force 16.65±3.51 kN and energy 1.06±0.18 MJ m -3

The effect of heating and freezing pretreatments on rapeseed oil yield and the volume of oil energy under uniaxial compression loading was investigated. Four separate experiments were carried out to achieve the study objective. The first and second experiments were performed to determine the compression parameters (deformation, mass of oil, oil yield, oil expression efficiency, energy, volume of oil and volume of oil energy). The third and fourth experiments identified the optimal factors (heating temperatures: 40, 60 and 80 °C, freezing temperatures: −2, −22 and −36 °C, heating times: 15, 30 and 45 min and speeds: 5, 10 and 15 mm/min) using the Box–Behnken design via the response surface methodology where the oil yield and volume of oil energy were the main responses. The optimal operating factors for obtaining a volume of oil energy of 0.0443 kJ/mL were a heating temperature of 40 °C, heating time of 45 min and speed of 15 mm/min. The volume of oil energy of 0.169 kJ/mL was reached at the optimal conditions of a freezing temperature of −36 °C, freezing time of 37.5 min and speed of 15 mm/min. The regression model established was adequate for predicting the volume of oil energy only under heating conditions.