METHODS: In the previous study, the azeotropic drying of non-carrier-added (n.c.a) 18F-Fluorine in the reactor was conducted at atmospheric pressure (0 atm) and shorter duration time. In this study, however, the azeotropic drying of non-carried-added (n.c.a) 18FFluorine was made at a high vacuum pressure (- 0.65 to - 0.85 bar) with an additional time of 30 seconds. At the end of the synthesis, the mean radiochemical yield was statistically compared between the two azeotropic drying conditions so as to observe whether the improvement made was significant to the radiochemical yield.
RESULTS: From the paired sample t-test analysis, the improvement done to the azeotropic drying of non-carrier-added (n.c.a) 18F-Fluorine was statistically significant (p < 0.05). With the improvement made, the 18F-Fluorcholine radiochemical yield was found to have increase by one fold.
CONCLUSION: Improved 18F-Fluorocholine radiochemical yields were obtained after the improvement had been done to the azeotropic drying of non-carrier-added (n.c.a) 18F-Fluorine. It was also observed that improvement made to the azeotropic drying of non-carrier-added (n.c.a) 18F-Fluorine did not affect the 18F-Fluorocholine quality control analysis.
RESULTS: The relationship between dimensionless moisture content and shrinkage of sweet potato in terms of volume, surface area, perimeter and illuminated area was found to be linearly correlated. The results also demonstrated that the shrinkage of sweet potato based on computer vision and backscattered optical parameters is affected by the product thickness, drying temperature and drying time. A multilayer perceptron (MLP) artificial neural network with input layer containing three cells, two hidden layers (18 neurons), and five cells for output layer, was used to develop a model that can monitor, control and predict the shrinkage parameters and moisture content of sweet potato slices under different drying conditions. The developed ANN model satisfactorily predicted the shrinkage and dimensionless moisture content of sweet potato with correlation coefficient greater than 0.95.
CONCLUSION: Combined computer vision, laser light backscattering imaging and artificial neural network can be used as a non-destructive, rapid and easily adaptable technique for in-line monitoring, predicting and controlling the shrinkage and moisture changes of food and agricultural crops during drying. © 2017 Society of Chemical Industry.
RESULTS: Studies were carried out on drying of papaya leaves using hot air (60, 70 and 80 °C), shade and freeze drying. Effective diffusivities were estimated ranging from 2.09 × 10-12 to 2.18 × 10-12 m2 s-1 from hot air drying, which are within the order of magnitudes reported for most agricultural and food products. The activation energy to initiate drying showed a relatively low value (2.11 kJ mol-1 ) as a result of the thin leave layer that eased moisture diffusion. In terms of total polyphenols content and antioxidant activities, freeze-dried sample showed a significantly higher (P
RESULTS: The gas pressure for CIR-HAD was higher centrally and decreased gradually towards the surface of the product. This implies that drying force is stronger at the product core than at the product surface. A phase change from liquid water to vapour occurs almost immediately after the start of the drying process for CIR-HAD. The evaporation rate, as expected, was observed to increase with increased drying time. Evaporation during CIR-HAD increased with increasing distance from the centreline of the sample surface. The simulation results of water and vapour flux revealed that moisture transport around the surfaces and sides of the sample is as a result of capillary diffusion, binary diffusion, and gas pressure in both the vertical and horizontal directions. The nonuniform dominant infrared heating caused the heterogeneous distribution of product temperature. These results suggest that CIR-HAD of food occurs in a non-uniform manner with high vapour and water concentration gradient between the product core and the surface.
CONCLUSIONS: This study provides in-depth insight into the physics and phase changes of food during CIR-HAD. The multiphase model has the advantage that phase change and impact of CIR-HAD operating parameters can be swiftly quantified. Such a modelling approach is thereby significant for further development and process optimization of CIR-HAD towards industrial upscaling. © 2020 Society of Chemical Industry.