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.
OBJECTIVE: This study aimed to optimize the yield of pectin extracted from sweet potato residue and investigate its emulsifying properties.
METHODS: Response surface methodology (RSM) has been utilized to investigate the pectin extracted from sweet potato peels using citric acid as the extracting solvent. Investigation of the effect of different extraction conditions namely temperature (°C), time (min) and solution pH on pectin yield (%) were conducted. A Box-Benhken design with three levels of variation was used to optimize the extraction conditions.
RESULTS: The optimal conditions determined were temperature 76°C, time 64 min and pH 1.2 with 65.2% yield of pectin. The degree of esterification (DE) of the sweet potato pectin was determined using Fourier Transform Infrared (FTIR) Spectroscopy. The pectin is high-methoxyl pectin with DE of 58.5%. Emulsifying properties of sweet potato pectin were investigated by measuring the zeta-potential, particle size and creaming index with addition of 0.4 and 1.0 wt % pectin to the emulsion.
CONCLUSION: Extraction using citric acid could improve the pectin yield. Improved emulsion stability was observed with the addition of the sweet potato pectin.
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.
OBJECTIVES: To assess the effects of sweet potato for type 2 diabetes mellitus.
SEARCH METHODS: We searched several electronic databases, including The Cochrane Library (2013, Issue 1), MEDLINE, EMBASE, CINAHL, SIGLE and LILACS (all up to February 2013), combined with handsearches. No language restrictions were used.
SELECTION CRITERIA: We included randomised controlled trials (RCTs) that compared sweet potato with a placebo or a comparator intervention, with or without pharmacological or non-pharmacological interventions.
DATA COLLECTION AND ANALYSIS: Two authors independently selected the trials and extracted the data. We evaluated risk of bias by assessing randomisation, allocation concealment, blinding, completeness of outcome data, selective reporting and other potential sources of bias.
MAIN RESULTS: Three RCTs met our inclusion criteria: these investigated a total of 140 participants and ranged from six weeks to five months in duration. All three studies were performed by the same trialist. Overall, the risk of bias of these trials was unclear or high. All RCTs compared the effect of sweet potato preparations with placebo on glycaemic control in type 2 diabetes mellitus. There was a statistically significant improvement in glycosylated haemoglobin A1c (HbA1c) at three to five months with 4 g/day sweet potato preparation compared to placebo (mean difference -0.3% (95% confidence interval -0.6 to -0.04); P = 0.02; 122 participants; 2 trials). No serious adverse effects were reported. Diabetic complications and morbidity, death from any cause, health-related quality of life, well-being, functional outcomes and costs were not investigated.
AUTHORS' CONCLUSIONS: There is insufficient evidence about the use of sweet potato for type 2 diabetes mellitus. In addition to improvement in trial methodology, issues of standardization and quality control of preparations - including other varieties of sweet potato - need to be addressed. Further observational trials and RCTs evaluating the effects of sweet potato are needed to guide any recommendations in clinical practice.
Objectives: This study aimed to predict the actions of 10 compounds in I. batatas leaves, which are YGM-0a [cyanidin 3-0-sophoroside-5-0-glucosede], YGM-0f [cyanidin 3-O-(2-0-(6-0-(E)-p-coumaroyl-β-D-glucopyranosyl)-β-D-glucopyranoside)-5-0-β-D-glucopyranoside], YGM-1a [cyanidin 3-(6,6'-caffeylp-hydroxybenzoylsophoroside) -5-glucoside], YGM-1b [cyanidin 3-(6,6'-dicaffeylsophor-oside)-5-glucoside], YGM-2 [cyanidin 3-(6-caffeylsophoroside)-5-glucoside], YGM-3 [cyanidin 3-(6,6'-caffeyl-ferulylsophoroside)-5-glucoside], YGM-4b [peonidin 3-(6,6'-dicaffeylsophoroside)-5- glucoside], YGM-5a [peonidin 3-(6,6'-caffeylphydroxybenzo-ylsophoroside)-5-gluco-side], YGM-5b [cyanidin 3-6-caffeylsophoroside)-5-glucosede], and YGM-6 [peonidin 3-(6,6'-caffeylferulylsophoroside)-5-glucoside] as LOX inhibitors, and also predict the stability of ligand-LOX complex.
Materials and Methods: The compounds were screened through docking studies using PLANTS. Also, the molecular dynamics simulation was conducted using GROMACS at 310K.
Results: The results showed that the most significant binding affinity toward LOX was shown by YGM-0a and YGM-0a, and the LOX complex in molecular dynamics simulation showed stability for 20 ns.
Conclusion: Based on Docking Studies and Molecular Dynamics Simulation of I. Batatas Leaves compounds, YGM-0a was shown to be the most probable LOX inhibitor.