Shelf-Life Prediction Model of Bambara Nut (Vigna Subterranea) Flour: Polynomial Model, Sorption Isotherms and Physico-Mechanical Properties (e-Proceedings of the Faculty of Agriculture International Conference, 2024)
Bambara nut samples were obtained, milled, packaged in Low Density Polyethylene and stored for a period of 12 weeks under controlled temperatures of 20°C, 30°C and 40°C respectively. At weekly intervals, the flours were analyzed for proximate composition, Physicomechanical properties and sorption isotherms. The data obtained from the study were analyzed using the Design-Expert software (Version 7.0.0, Stat-Ease Inc., Minneapolis, USA). The experimental data generated was fitted to a polynomial regression model for predicting maximum shelf-life. In order to correlate the response variables to the independent variables, multiple regressions were used to fit the coefficient of the polynomial model. The quality of fit of the model was evaluated using analysis of variance (ANOVA). The suitability of the models was compared and evaluated using correlation coefficient (R2). The study showed that all the parameters studied were significant in predicting the shelf-life of Bambara nut flour. The results obtained in the study showed that the response surface model developed is a good one. The model correlation coefficient (R2) of the responses was found to be 0.9983, 0.9701, 0.9688, 0.9862, 0.9138 and 0.9531 for the flour moisture, ash, protein, fat, fibre and carbohydrate contents, respectively. Levels of significance obtained were 0.001, 0.02, 0.03, 0.01, 0.03 and 0.02 for the flour moisture, ash, protein, fat, fibre and carbohydrate contents which were high and attested to the fitness of the model in evaluating the responses. Optimum moisture content and storage time were found to be 6.32% (wb) and 23.62 weeks. The study confirmed that the model developed is adequate to optimize these process conditions.
Keywords: Bambara nut; Model; Multiple Regression; Polynomial; Shelf-life