The effect of shape,blanching methods and flour on characteristics of restructured sweetpotato stick

The blanching methods and controlling dry matter content used in the preparation of mashed sweetpotato (SP) for producing restructured sweetpotato stick (RSS) were studied. The two shapes of trimmed tuber were sliced and diced. Blanching was conducted by (i) boiling for 2 min in water at 100 °C and (ii) blanching in water at 100 °C containing 1% (w/v) sodium tripolyphosphate (STP) for 2 min. Controlling the dry matter content of dough was conducted by adding SP flour (0%, 5%, 10%, 15% and 20%). Chemical and physical characteristics were determined on the final product. The results from this study showed that blanching in 1% STP for 2 min improves colour and ash content of fried stick. Mixing of 5% SP flour with mashed SP produces suitable dough for further processing. RSS can be produced by a combination of blanching in 1% STP and mixing with 5% SP flour produce an intermediate hardness, high lightness and low redness colouration of RSS.     

Utilization of Yellowfin Tuna and Red Snapper Roe Protein Concentrate as Emulsifier in Mayonnaise

Roes of yellowfin tuna （Thunnus albacares） and red snapper （Lutjanus champecanus） are considered as abundant and underutilized by-product in Indonesia. The production of roe protein concentrate （RPC） is expected to increase the economic value and potency of their usage. Tuna and red snapper RPC was defatted using etano195% with one, two, three and four times of repetition. Both RPC made with four times repetition of defatting had the highest protein content （79.90% and 80.72%）, showed high emulsion activity （97.46% and 99.62%） and emulsion stability （97.10% and 99.48%）, and decreased the interfacial tension of 51% and 55.9%, respectively. Mayonnaise was made with 0, 25, 50, 75 and 100% substitution level of each RPC with four times repetition ofdefatting. The physical, chemical and organoleptic properties of mayonnaise were studied. The best mayonnaise formulation was obtained from 50% substitution level of tuna and red snapper RPC. Mayonnaise with tuna and red snapper RPC had good viscosity （5,920 cPs and 5,845 cPs）, high emulsion stability （90.5% and 91.73%） and small fat globule size （±2.5 lam and ± 2.25 lam）, respectively. These mayonnaises also showed high score of spreadability and low intense of fishy odor. However they still had quite strong of fishy flavor based on scoring test.     

Neural computing for walking gait pattern identification based on multi-sensor data fusion of lower limb muscles

The use of neural computing for gait analysis widely known as computational intelligent gait analysis is addressed recently. This research work reports multilayer feed-forward neural networks for walking gait pattern identification using multi-sensor data fusion; electromyography (EMG) signals and soft tissue deformation analysis using successive frames of video sequence extracted from lower limb muscles according to each gait phase within the considered gait cycle. Neural computing framework for walking gait pattern identification consists of system hardware and intelligent system software. System hardware comprises a wireless surface EMG sensor unit and two video cameras for measuring the neuromuscular activity of lower limb muscles, and a custom-developed artificial neural network for classifying the gait patterns of subjects during walking. The system uses root mean square and soft tissue deformation parameter as the input features. Multilayer feed-forward back propagation neural networks (FFBPNNs) with different network training functions were designed, and their classification results were compared. The intelligent gait analysis system validation has been carried out for a group of healthy and injured subjects. The results demonstrated that the overall accuracy of 98 % prediction is achieved for gait patterns classification established by multi-sensor data fusion of lower limb muscles using FFBPNN with Levenberg–Marquardt training function resulting better performance over FFBPNN with other training functions.

     

Microbial Contributions for Rice Production: From Conventional Crop Management to the Use of ‘Omics’ Technologies

Rice, the main staple food for about half of the world’s population, has had the growth of its production stagnate in the last two decades. One of the ways to further improve rice production is to enhance the associations between rice plants and the microbiome that exists around, on, and inside the plant. This article reviews recent developments in understanding how microorganisms exert positive influences on plant growth, production, and health, focusing particularly on rice. A variety of microbial species and taxa reside in the rhizosphere and the phyllosphere of plants and also have multiple roles as symbiotic endophytes while living within plant tissues and even cells. They alter the morphology of host plants, enhance their growth, health, and yield, and reduce their vulnerability to biotic and abiotic stresses. The findings of both agronomic and molecular analysis show ways in which microorganisms regulate the growth, physiological traits, and molecular signaling within rice plants. However, many significant scientific questions remain to be resolved. Advancements in high-throughput multi-omics technologies can be used to elucidate mechanisms involved in microbial–rice plant associations. Prospectively, the use of microbial inoculants and associated approaches offers some new, cost-effective, and more eco-friendly practices for increasing rice production.     

Performance study and optimization of multi-channel allocation in VANET under physical channel model

Wireless Networks - Vehicular networking has a promising potential as a communication medium to relay the information related with the road safety, traffic efficiency and infotainment and thus many...     

Electrorheological and Magnetorheological Duct Flow in Shear-Flow Mode Using Herschel-Bulkley Constitutive Model

A quasi-steady duct flow through a parallel plate model for electrorheological (ER) and magnetorheological (MR) fluids under shear-flow mode is investigated mathematically. To do so Herschel-Bulkley power law constitutive model for ER and MR fluid is adopted to account for postyield shear thinning or shear thickening conditions as indicated in recent research. This approach is selected in order to obtain a more flexible representation of ER or MR postyield behavior rather than using the mostly adopted Bingham plastic model. This will lead to developing a theoretical method for prediction of ER or MR force characteristics.