Mathematical Modelling and Numerical Simulation of Mass Transfer during Deep-Fat Frying of Plantain (\u3ci\u3eMusa paradisiacal\u3c/i\u3e AAB) Chips (\u3ci\u3eipekere\u3c/i\u3e)

This study developed a mathematical model following the fundamental principles of mass transfer for the simulation of the oil and moisture content change during the Deep-Fat Frying of plantain (ipekere) chip. The explicit Finite Difference Technique (FDT) was used to conduct a numerical solution to the consequential governing equation (partial differential equation) that was used to describe the mass transfer rate during the process. Computer codes that were computed in MATLAB were used for the implementation of FDT at diverse frying conditions. Samples of the plantain were cut into portions of 2 mm thickness, and these sliced portions were fried at separate frying oil temperatures (170, 180 and 190°C) between 0.5 and 4 minutes. The experimental data and the predicted outcomes were compared for the validation of the model, and the juxtaposition revealed a plausible agreement. The predicted values and the experimental values of oil and moisture transfer models produced correlation coefficients that range from 0.96 to 0.99 and 0.94 to 0.99, respectively. The predicted outcomes could be utilized for the control and design of the DFF

Mathematical Modelling and Numerical Simulation of Mass Transfer During Deep-Fat Frying of Plantain (Musa paradisiacal AAB) Chips (ipekere)

This study developed a mathematical model following the fundamental principles of mass transfer for the simulation of the oil and moisture content change during the Deep-Fat Frying of plantain (ipekere) chip. The explicit Finite Difference Technique (FDT) was used to conduct a numerical solution to the consequential governing equation (partial differential equation) that was used to describe the mass transfer rate during the process. Computer codes that were computed in MATLAB were used for the implementation of FDT at diverse frying conditions. Samples of the plantain were cut into portions of 2 mm thickness, and these sliced portions were fried at separate frying oil temperatures (170, 180 and 190°C) between 0.5 and 4 minutes. The experimental data and the predicted outcomes were compared for the validation of the model, and the juxtaposition revealed a plausible agreement. The predicted values and the experimental values of oil and moisture transfer models produced correlation coefficients that range from 0.96 to 0.99 and 0.94 to 0.99, respectively. The predicted outcomes could be utilized for the control and design of the DFF

Modeling and optimization of processing parameters of strips produced from blends of cassava and cowpea flour

Most Nigerian traditional foods have a low nutritional value, inconsistent sensory attributes, and short shelf life. Thus, upgrading becomes necessary for the technologies used in the processing, distributing, and storing of indigenous snack foods to improve the products' nutritional, sensory, and storage properties. A Box-Behnken (three-factor) response surface methodology was used to optimize the process. The effect of frying temperature (160 - 180°C), frying time (8 - 12 min) and percent cowpea flour (10 - 30%) on some attributes (moisture, fat, protein contents, texture, and color change) of cassava-cowpea strips fried snack. Data were analyzed by ANOVA and regression analysis. The moisture content ranged between 1.00% and 4.26%, fat content (8.41–11.94%), protein content (30.83–36.42%), texture (5.06–13.14 N) and color change (26.967–40.479). Frying temperature, frying time and % cowpea flour had a significant (P < 0.05) effect on moisture, fat, protein contents, texture and color change of cassava-cowpea strips. The processing conditions affected moisture, fat, protein, texture, and color change. Coefficients of determination, R2 were 0.87, 0.86, 0.79, 0.88 and 0.71, respectively. The best conditions for processing cassava-cowpea strips were 12 min frying time, 166.65 °C frying temperature, and 24.36% cowpea flour content. The desirability of optimization was 0.65. Therefore, composite flour from cassava and cowpea can be adopted or used to produce strips to prevent protein-energy malnutrition in the community