Rheology is the science of deformation and flow behavior of fluid. Knowledge of rheological properties of fluid foods and their variation with temperature and concentration have been globally important for industrialization of food technology for quality, understanding the texture, process engineering application, correlation with sensory evaluation, designing of transport system , equipment design (heat exchanger and evaporator ), deciding pump capacity and power requirement for mixing. The aim of this study was to determine the rheological behavior of pomelo juice at different concentrations (20-60.4%) and temperatures (23-60°C) by using a rotational rotational Haake Rheostress 600 rheometer. Pomelo juice was found to exhibit both Newtonian and Non-Newtonian behavior. For lower concentration the Newtonian behavior is observed while at higher concentration Non-Newtonian behavior was observed. Standard error (SE) method was selected on the basis to carry out the error analysis due to the best fit model. For the four models the values of SE show that the Herschel-Bulkley and Power Law models perform better than the Bingham and Casson models but Herschel-Bulkley model is true at higher concentration. The rheological model of pomelo juice, incorporating the effects of concentration and temperature was developed. The master-curve was investigated for comparing data from different products at a reference temperature of 40°C. Multiple regression analysis indicated Master-Curve presents good agreement for pomelo juice at all concentrations studied with R2>0.8.
As Malaysia is one of the world's largest producer of palm oil, large amounts of palm oil mill effluent (POME) is generated. It was found that negatively charged components are accountable for POME color. An attempt was made to remove residual contaminants after conventional treatment using anion base resin. Adsorption experiments were carried out in fixed bed column. Various models such as the Thomas, the Yoon-Nelson, the Wolborska and BDST model were used to fit the experimental data. It was found that only the BDST model was fitted well at the initial breakthrough time. A wavelet neural network model (WNN) was developed to model the breakthrough curves in fixed bed column for multicomponent system. The results showed that the WNN model described breakthrough curves better than the commonly used models. The effects of pH, flow rate and bed depth on column performance were investigated. It was found that the highest uptake capacity was obtained at pH 3. The exhaustion time appeared to increase with increase in bed length and decrease in flow rate.