The global increase in demand for vegetable oils due to their nutritional, pharmaceutical, and industrial importance necessitates investigation on exploitation of new oil bearing crops. This study was undertaken to determine the influence of processing conditions on physical properties of oil expressed mechanically from African oil bean kernels. Central composite rotatable design of response surface methodology (RSM) at 3-factors, 5-levels was adopted; independent variables considered were: moisture content (MC; 8, 10, 12, 14, and 16% dry basis), heating duration (HD; 5, 10, 15, 20, and 25 min), and heating temperature (HT; 50, 70, 90, 110, 130 C) while dependent variables (physical characteristics) include: oil impurities, density, kinematic viscosity, dynamic viscosity, specific gravity, refractive index, and color; all determined using standard methods. Optimum values were obtained using RSM. Optimum oil quality (0.044%m/m impurity, 0.92 kg/m 3 density, 43.86 mm 2 /s kinematic viscosity, 40.8 mPa s dynamic viscosity, 0.9 specific gravity, 1.469 refractive index, and 7.7 color units) was obtained at 8% db, 11.7 min HD, and 59.3 C HT. The quality of the expressed oil falls within permissible limits for edible oils and high nutritional importance. The processing conditions influenced the quality of the mechanically expressed African oil bean oil. Practical applicationsAfrican oil bean is an underutilized plant with potentials for meeting increasing global demand for vegetable oil for nutritional and industrial importance. The application/usage of oil is dependent on its quality attributes which is otherwise dependent on processing parameters like moisture content, heating duration, and heating temperature. These factors need to be controlled during oil expression processes to actually the optimum conditions that will yield good quality oil. The degree of influence of the parameters on oil quality is a useful data to vegetable oil processors, food, and agricultural engineers. The quality parameters of the oil were determined and compared with international standard parameters. Mathematical models were developed to predict the yield and quality attributes of expressed oil from African oil bean kernels at various processing conditions.
Purpose: New low-cost oilseeds are needed to meet an ever-increasing demand for oil for food, pharmaceutical, and industrial applications. African oil bean seed is a tropical crop that is underutilized and has high oil yields, but there have been no studies conducted on its mechanical oil expression up to now. The objective of this work was to investigate the effect of moisture content and seed dimensions on mechanical oil expression from the seeds. Methods: Fresh oil bean seeds were procured, de-hulled, and cleaned. Initial seed moisture content, obtained in accordance with the ASAE standard, was 12% dry basis (db). The seeds were further conditioned by dehydration and rehydration prior to oil expression to obtain four other moisture levels of 8, 10, 14, and 16% db. The major diameter of the seeds was measured using digital vernier calipers, and the seeds were classified into size dimensions (< 40, 41-45, 46-50, 51-55, and > 55 mm). The oil yield and expression efficiency were obtained in accordance with standard evaluation methods. Results: The highest oil yield and expression efficiency (47.74% and 78.96%, respectively) were obtained for a moisture content of 8% db and seed dimensions of < 40 mm, while the lowest oil yield and expression efficiency (41.35% and 68.28%, respectively) were obtained for a moisture content of 14% db and seed dimensions between 51-55 mm. A mathematical model was developed to predict oil yield for known moisture content and seed dimensions, with a coefficient of determination R 2 of 95% and the confidence level of the predictive model of 84.17%. The probability of prediction F ratio showed that moisture content influence was more significant than seed dimensions. Conclusions: The higher the moisture content and larger the seed dimensions, the lower the oil yield from African oil bean seeds.
: Waste from a forest environment constitutes an enormous quantity of renewable energy resources. In this study undesirable forest materials, such as jatropha seed shells (JSSs) and Eucalyptus camaldulensis wood shavings (EcWSs) were used in the production of briquettes with Acacia senegal as the binder using mixing proportions of 0 : 100, 25 : 75, 50 : 50, 75 : 25 and 100 : 0 while the binder was varied from 50, 60, 70, 80 to 90 g. Some physical properties, such as the density, moisture content, water resistance and shatter index, were optimised using the response surface methodology at these mixing proportions. The outcome of the production showed the briquettes to have mean values of 0.66 kg·m<sup>–3</sup>, 11.51, 91.12 and 99.7 % for the density, moisture content, water resistance and shatter index, respectively. The optimum mixing ratio and binder quantity of 75 : 25 and 60 g, respectively, would result in a briquette having a 0.70 kg·m<sup>–3</sup>, 10.88, 98.11 and 99.86% density, moisture content, water resistance and shatter index, respectively. It has been revealed that the JSS and EcWS are potential organic wastes which could be used as a feedstock for the production of briquettes. It could be concluded that the variation in the mixing proportion of the JSSs, EcWSs and A. senegal significantly affected the properties of the produced briquettes.
The relationship existing between operating parameters like speed of operation, applied pressure, feed rate and dependent variables like oil recovery and throughput capacity was investigated while mechanically expressing oil from kernels of Pentaclethra macrophylla. A 3-factor, 5-levels central composite design of response surface methodology was used for modeling and optimization of the process. The speed was varied over 15, 30, 45, 60 and 75 rpm using pulley arrangement, applied pressure was varied over 5, 10, 15, 20 and 25 MPa by adjusting the wormshaft distance of the oil expeller and the feed rate was varied over 100, 200, 300, 400 and 500 g/min by regulating the quantity of kernels fed into the expeller. Developed models were validated by comparing predicted values with experimental values. In optimizing the process, the oil recovery and throughput capacity were maximized while the independent variables were set at ranges. Optimum oil recovery of 73.2% and throughput capacity of 4.18 kg/h was obtained at 45 rpm speed of operation, 20 MPa applied pressure and 300 g/min feed rate. A quadratic polynomial model was developed for the oil recovery while a two-factorial-interaction model was developed for the throughput capacity. The results showed that speed of operation, applied pressure and feed rate had significant influence on the oil recovery and throughput capacity. The models developed were valid as they showed a good agreement between the variable due to low variations between calculated and predicted values.
This study investigated the degree of influence of moisture content on some physical properties of nutmeg. The nutmeg seeds were subjected to six different levels of moisture content (5, 7, 9, 11, 13 and 15% db). Moisture content had a significant effect on the physical properties (p <0.05). A decrease in moisture content led to a decrease in length, width, thickness, geometric, arithmetic, square and equivalent mean diameters. Moisture content had a linear relationship with sphericity, projected and surface area, bulk and true density while it had an inverse relationship with porosity and angle of repose. Moisture content had a significant effect on coefficient of friction of nutmeg on the four surfaces considered (glass, stainless steel, plywood and rubber). Glass, stainless steel, plywood and rubber have an increasing coefficient of friction respectively; this implies that materials will move easily with lesser resistance on glass and stainless steel compared to more resistance on plywood. The data obtained will guide engineers, food processors and technicians in accurate selection of machine parts in design and constructions of sorting, separating, cleaning equipment and post harvest machines which will eventually aid commercialization and efficient processing of the spice crop.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.