Development of binary models for prediction and optimization of nutritional values of enriched kokoro: a case of response surface methodology (RSM) and artificial neural network (ANN)
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Babatunde Kazeem Adeoye
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Emmanuel Olusola Oke
Abstract
Kokoro as a broadly acknowledge maize snack is being consumed every day by both the children and grown-ups, but it is characterized by the low protein content required for survival. The blending of maize flour (MF), sesame flour (SF) and moringa flour (mF) to enhance the nutritional values of kokoro was optimize with response surface technique (RSM) and artificial neural network (ANN). MF, SF and mF were mixed at diverse proportion and the optimal blending ratio was gotten using D-optimal design method. The protein and carbohydrate actual contents were compared with their predicted values using RSM and ANN models. The performance of the developed RSM and ANN models were validated with coefficient of determination (R2) and mean square error (MSE). The optimal blending ratio of MF: SF: mF was 54.11: 37.06: 8.83. The optimal blending ratio gave 25.53% of protein content and 45.99% of carbohydrate content. The statistical analysis of the experimental data obtained using different statistical techniques shows that regression models by RSM gave R2 of 0.999 for protein yield and 0.983 for carbohydrate yield while ANN gave R2 of 0.999 with MSE 9.24184 × 10−1. Therefore, it can be concluded from the results that both the RSM and ANN gave good prediction of the model.
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Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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Artikel in diesem Heft
- Frontmatter
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- Development of binary models for prediction and optimization of nutritional values of enriched kokoro: a case of response surface methodology (RSM) and artificial neural network (ANN)
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Artikel in diesem Heft
- Frontmatter
- Research Articles
- Experimental and simulation assessment to mitigate the emission of sulfide toxic gases and removing main impurities from Zn + Pb + Cu recovery plants
- Dynamic behavior of CO2 adsorption from CH4 mixture in a packed bed of SAPO-34 by CFD-based modeling
- Competitive adsorption of heavy metals in a quaternary solution by sugarcane bagasse – LDPE hybrid biochar: equilibrium isotherm and kinetics modelling
- Estimation of 2,4-dichlorophenol photocatalytic removal using different artificial intelligence approaches
- Design of a new synthetic nanocatalyst resulting high fuel quality based on multiple supports: experimental investigation and modeling
- Numerical study on thermal-hydraulic characteristics of flattened microfin tubes
- Development of binary models for prediction and optimization of nutritional values of enriched kokoro: a case of response surface methodology (RSM) and artificial neural network (ANN)
- A mathematical model for the activated sludge process with a sludge disintegration unit
- Process design and economic assessment of large-scale production of molybdenum disulfide nanomaterials
- Review
- Modified optimal series cascade control for non-minimum phase system
