Rapid quantitative method for total brominated vegetable oil in soft drinks using ion chromatography

A simple, quantitative and rapid method for total brominated vegetable oil (BVO) using ion chromatography (IC) with suppressed conductivity detection was developed and successfully applied to soft drinks with results expressed as inorganic bromide anion. The procedure involves extraction of BVO with diethyl ether and treatment with zinc dust in a solution of acetic acid, giving recoveries ranging between 92.5 and 98.5%. The calibration curves obtained were linear with correlation coefficients (r (2)) of 0.998, a coefficient of variation (CV) of less than 5% and limit of detection (LOD) and limit of quantification (LOQ) of 250 and 750?μg?l(-1), respectively. The method was successfully applied to the determination of BVO in several commercial soft drinks which were found to contain BVO in the range 1.8-14.510?mg?l(-1). The method has less sources of error compared to previously published methods.     

Selection of wild lactic acid bacteria isolated from traditional Egyptian dairy products according to production and technological criteria

A total of 755 wild lactic acid bacteria (LAB), belonging to Lactococcus, Lactobacillus, Enterococcus, Streptcoccus, Leuconostoc and Pediococcus isolated from Egyptian raw milk and its products were screened and selected according to their production and technological properties. Many strains showed high yield of biomass in fermentation and some strains were resistant to lyophilization conditions. Most strains showed a good separation after centrifugation, 2% of Lactococcus, 13% of Lactobacillus and 1% of Enterococcus were fast acidifying strains. Aminopeptidase and autolytic activity were generally higher for most lactobacilli compared to other strains. In milk cultures many strains were able to produce pleasant flavours. Antimicrobial activity was detected in 47% of Lactococcus, 21% of Lactobacillus and 41% of Enterococcus strains and some strains produced exopolysaccharides (slime and capsule). Several strains were able to maintain a high activity of two or three technological characteristics together. Detailed information about the characteristics of each strain is available in the culture collection of Faculty of Agriculture Alexandria University (FAAU). A wide variety of LAB from traditional Egyptian dairy products that showed potentially important properties are not only valuable for practical application but they may also provide an expanded gene pool for designing genetic modified strains with improved traits.     

Effect of the defatting process,acid and alkali extraction on the physicochemical and functional properties of hemp,flax and canola seed cake protein isolates

The effect of the defatting process, acid and alkali extractions on the physicochemical and functional properties of hemp, flax and canola protein isolates was studied. The defatting process enriched the protein content from around 35 to 52–55 % in the defatted oilseed cakes. Further treatment with acid and alkali led to the highest enrichment (P < 0.05) in the protein content (>90 %). The defatting process produced the lightest (P < 0.05) colour compared to the parent oilseed cakes and the protein isolates. Alkali extraction produced protein isolates with the highest (P < 0.05) water holding capacity while the original oilseed cakes had the highest oil holding capacity. Acid and alkali extractions produced protein isolates with the highest emulsifying activity and emulsion stability. The alkaline soluble flax protein isolate and acid hemp protein isolate had the highest (P < 0.05) creaming stability and largest droplet size respectively. Amino acid profiles of protein isolates after acid and alkali extraction were improved, leading to a protein suitable for fortification in food products that meet human nutrition requirements.     

Tuned interactions of silver nanoparticles with ZSM-5 zeolite by adhesion-promoting poly(acrylic acid) deposited by electrospray ionization (ESI)

The electrospray ionization (ESI) method was used for deposition of thin films of poly(acrylic acid) (PAA) on Cu/ZSM-5 (5 wt.% Cu) and Ag–Cu/ZSM-5 (1 wt.% Ag and 4 wt.% Cu) composites. For comparative purposes, the ZSM-5 zeolite was synthesized under hydrothermal conditions and loaded with PAA under the same treating conditions as the composites. This method allowed the formation of uniform polymer films of controlled thickness on conductive substrates. The structural characteristics were characterized by X-ray photoelectron spectroscopy, Fourier-transform infrared spectroscopy, atomic force microscopy and X-ray diffraction (XRD). The deposited PAA layer over ZSM-5 acts as a common dispersing and stabilizing agent through coordination-driven guest-templated polymer via interaction of Ag⁺ and Cu²⁺ with carboxylic acid groups, thus increasing and controlling the adhesion and the release of metallic species. A short exposure to light and temperature has reduced the metal ions to Cu⁰ and Ag⁰ metallic nanoparticles. The results of XRD analysis let suggest that the interaction of Cu and Ag with carboxylic groups of PAA inhibits the formation of large metallic silver particles. These samples were being studied for their potential as antibacterial agents toward the bacterial strains such as Staphylococcus pneumonia, Bacillus subtilis, Escherichia coli and Pseudomonas aeruginosa as Gram positive and Gram negative bacteria, respectively. Aspergillus fumigatus and Candida albicans as Fungi were also evaluated. The Cu/ZSM-5 and Ag–Cu/ZSM-5 nanocomposites coated with a 10 nm thick PAA layer exhibit significant antibacterial activity.     

The effects of welding parameters on the weldability of different materials using brazing alloy fillers

The objectives of the study were to investigate the microwelding conditions related to diffusion mechanism and elemental migration metallurgical and microscopy investigation, and to establish the fundamental corrosion mechanism on the properties of small welding and brazing areas that consist different materials. This study focuses on the weldability of Ti /Ni using microspot brazing technology by selection of brazing condition current, 2, 2.5, 3, 3.5 and 4 kA, voltage 2 V, load 60 N and welding time 25–50 ms, this welding condition effective on brazing temperature optimization this condition. Ti and Ni were selected as base metals. Four types of metal fillers were used as filler foils, sandwiched between Ti/Ni. First type of metal fillers was, 65Ni–35Cu foil, melting point 846 °C; the second was, 71Ag–28Cu–1Mg foil, melting point 775 °C; the third was, 80Ag–18Cu–2Ti foil, melting point 782 °C; and fourthly was, 73Ni–18Cr–9Si foil, melting point 917 °C. The electrode tip face chosen was circuitous in form. All brazed joint were made by microspot brazing method. Brazing was done under normal atmospheric condition.     

Enhancing Inflow Forecasting Model at Aswan High Dam Utilizing Radial Basis Neural Network and Upstream Monitoring Stations Measurements

The Nile River is considered the main life artery for so many African countries especially Egypt. Therefore, it is of the essence to preserve its water and utilize it very efficiently. Developing inflow-forecasting model is considered the technical way to effectively achieve such preservation. The hydrological system of the Nile River under consideration has several dams and barrages that are equipped with control gates. The improvement of these hydraulic structures’ criteria for operation can be assessed if reliable forecasts of inflows to the reservoir are available. Recently, the authors developed a forecasting model for the natural inflow at Aswan High Dam (AHD) based on Artificial Intelligence (AI). This model was developed based on the historical inflow data of the AHD and successfully provided accurate inflow forecasts with error less than 10%. However, having several forecasting models based on different types of data increase the level of confidences of the water resources planners and AHD operators. In this study, two forecasting model approach based on Radial Basis Function Neural Network (RBFNN) method for the natural inflow at AHD utilizing the stream flow data of the monitoring stations upstream the AHD is developed. Natural inflow data collected over the last 30 years at four monitoring stations upstream AHD were used to develop the model and examine its performance. Inclusive data analysis through examining cross-correlation sequences, water traveling time, and physical characteristics of the stream flow data have been developed to help reach the most suitable RBFNN model architecture. The Forecasting Error (FE) value of the error and the distribution of the error are the two statistical performance indices used to evaluate the model accuracy. In addition, comprehensive comparison analysis is carried out to evaluate the performance of the proposed model over those recently developed for forecasting the inflow at AHD. The results of the current study showed that the proposed model improved the forecasting accuracy by 50% for the low inflow season, while keep the forecasting accuracy in the same range for the high inflow season.     

Marine omega‐3 (n‐3) phospholipids: A comprehensive review of their properties,sources, bioavailability,and relation to brain health

For several decades, there has been considerable interest in marine‐derived long chain n‐3 fatty acids (n‐3 LCPUFAs) due to their outstanding health benefits. n‐3 LCPUFAs can be found in nature either in triglycerides (TAGs) or in phospholipid (PL) form. From brain health point of view, PL n‐3 is more bioavailable and potent compared to n‐3 in TAG form, as only PL n‐3 is able to cross the blood–brain barrier and can be involved in brain biochemical reactions. However, PL n‐3 has been ignored in the fish oil industry and frequently removed as an impurity during degumming processes. As a result, PL products derived from marine sources are very limited compared to TAG products. Commercially, PLs are being used in pharmaceutical industries as drug carriers, in food manufacturing as emulsifiers and in cosmetic industries as skin care agents, but most of the PLs used in these applications are produced from vegetable sources that contain less (without EPA, DPA, and DHA) or sometimes no n‐3 LCPUFAs. This review provides a comprehensive account of the properties, structures, and major sources of marine PLs, and provides focussed discussion of their relationship to brain health. Epidemiological, laboratory, and clinical studies on n‐3 LCPUFAs enriched PLs using different model systems in relation to brain and mental health that have been published over the past few years are discussed in detail.     

Sensory,physicochemical and rheological properties of plant-based milk alternatives made from soybean,peanut, adlay,adzuki bean,oat and buckwheat

This study investigated the physicochemical and sensory properties of six types of plant-based milk alternatives prepared from soybean, peanut, adlay, adzuki bean, oat and buckwheat at a 1:9 grain-to-water ratio. The results revealed that all plant-based milk types had stable hydrocolloid systems, except for the buckwheat milk alternative that had undergone a gelatinisation process that affected its rheological and sensory properties. The pH values and total solid content of the prepared plant-based milk samples ranged between 6.60 to 6.83 and 2.13 to 8.17 g/100 mL, respectively. The sensory results showed that the overall liking score of adlay, adzuki bean and peanut milk alternative was 5.32, 5.30 and 6.10, respectively, suggesting a potential for commercial production. In conclusion, this investigation provides new insights on several new plant sources for plant-based milk alternatives. Further studies are required to investigate the digestibility and the nutritional effects expected from plant-based milk alternatives such as protein bioavailability, and the effects on human health compared to other animal milks.     

Modulation classification in the presence of adjacent channel interference using convolutional neural networks

This paper investigates a vital issue in wireless communication systems, which is the modulation classification. A proposed framework for modulation classification based on deep learning (DL) is presented in the presence of adjacent channel interference (ACI). This framework begins with the generation of constellation diagrams from the received data. These constellation diagrams are fed to convolutional neural networks (CNNs) for modulation classification. The objective of this process is to eliminate the manual feature extraction from the received data and make feature extraction process as a built‐in step with CNNs. Three types of CNNs are considered in this paper and compared for this objective. These types are AlexNet, VGG‐16, and VGG‐19. The proposed classifier is applied on Rayliegh and Rician fading channels.