ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY & ENVIRONMENT AZOJETE December 2023. Vol. 19(4):759-770 Published by the Faculty of Engineering, University of Maiduguri, Maiduguri, Nigeria. Print ISSN: 1596-2490, Electronic ISSN: 2545-5818 www.azojete.com.ng Corresponding author’s e-mail address: sunday.iweriolor@unidel.edu.ng 759 OPTIMIZATION OF VITAMIN A IN RED GUINEA CORN-MILLET MIX O. Eyide*1, S. Iweriolor2, B. J. Ossai3, E. T. Akhihiero4, and A. A. Olaseinde5 *1Department of Chemical Engineering, Faculty of Engineering, University of Delta, Agbor, Delta State, Nigeria. 2Department of mechanical Engineering, Faculty of Engineering, University of Delta, Agbor, Delta State, Nigeria. 3Department of Chemical Engineering, Faculty of Engineering, University of Delta, Agbor, Delta State, Nigeria. 4Department of Chemical Engineering, Faculty of Engineering, University of Benin, Benin City, Edo State, Nigeria 5Department of Materials Science and Engineering *Corresponding author's email address: sunday.iweriolor@unidel.edu.ng ARTICLE INFORMATION Submitted 27 Aug, 2023 Revised 9 Oct, 2023 Accepted 11 Oct, 2023 Keywords: Blend Millet Optimization Red guinea corn Vitamin A ABSTRACT This study focused on the optimization of vitamin A in guinea corn and millet mix. The concentration of vitamin A was investigated under the following conditions: blending time (1.5 - 5 hours), amount of red guinea corn (10 - 50g) and amount of agro residue (50-100 g) using Box-Behnken design. Statistically significant model (p<0.05) was developed to represent the relationship between the response (concentration of vitamin A) and the independent variables. The model showed a significant fit with experimental data with R2 values of 0.94. Analysis of variance (ANOVA) results showed that the concentration of vitamin A was influenced by the blending time, amount of red guinea corn and amount of millet used. Additionally, response surface methodology (RSM) was used to optimize the concentration of vitamin A. The results showed that maximum concentration of 98.76 µg/100g for vitamin A was obtained at the optimum production conditions of blending time of 5hours, 49.79g of red guinea corn and 100g of millet. The blend produced at the optimized conditions satisfied the World Health Organization (WHO), Food and Agricultural Organization (FAO) specification for recommended safe intake for all age groups, pregnant and nursing mothers 1.0 Introduction Nutrition is an integral part of human life, that deals with the study of nutrients which is the relationship between food and health. Nutrients are chemical compounds found in foods used by the body for growth, maintenance and repair. Examples of nutrients are carbohydrates, proteins, fats, vitamins, minerals and water. The right balance diets are vital for human existence, physical growth, mental development, performance and productivity throughout the entire lifetime (Akhihiero et al., 2022). One of the 17 goals of Sustainable Development Goals (SDGs) adopted by the United Nations in 2015 is to address global challenges such as hunger, food insecurity, improved nutrition and sustainable agriculture. Goal 2.1 of the SDGs targeted access to affordable, reliable, sustainable and right nutrition for all while target 2.2 has to do with eliminating all forms of malnutrition. In line with this Goal #2, the issue of hunger and malnutrition is of great concern to humans, which is linked to poor feeding habits, the high rate of junk food consumption, the high cost of nutritional foods and negligence of pregnant and nursing mothers which can be addressed via developing value-added products from millets and guinea corn. The issue of malnutrition may arise from lack of adequate or excess intake of one or more nutrients (Kunyanga et al., 2013), In 2019, two billion (25.9 percent) of the global population, http://www.azojete.com.ng/ mailto:sunday.iweriolor@unidel.edu.ng mailto:sunday.iweriolor@unidel.edu.ng mailto:sunday.iweriolor@unidel.edu.ng Arid Zone Journal of Engineering, Technology and Environment, Dec, 2023; Vol. 19(4):759-770. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: sunday.iweriolor@unidel.edu.ng 760 experienced hunger or did not have daily access to nutritious and enough food. While in Nigeria, malnutrition is a significant public health concern. According to World Food Program in Nigeria, an estimated 22 million people are affected by acute food insecurity and malnutrition, including 2.5million children under the age of five. Also, malnutrition is estimated to contribute to more than one-third of all child deaths (Younis et al., 2022). It is noteworthy that food insecurity affects diet quality, such as human diet and people’s health. Therefore, child malnutrition remains a global challenge. In 2019, it was reported that 21.3 percent (144.0 million) of children fewer than 5 years of age were estimated to be stunted, 6.9 percent (47.0 million) wasted and 5.6 percent (38.3 million) were overweight, while at least 340 million children suffered from micronutrient deficiencies. One of the micronutrients of interest in this study is vitamin A (retinol) which is an essential nutrient needed in small amounts by humans for the normal functioning of the visual system, growth, bone development, reproduction, protective effect on the skin, immune function, contributes to the development of normal teeth and hair ((Sabina et al., 2019, D’Ambrosio et al., 2011, Gutierrez-Mazariegos et al., 2011) Vitamin A is a fat soluble micronutrient that is vital for pregnant women and their fetus, they are needed for the maintenance of maternal night vision and fetal ocular health, also for the development of organs and the fetal skeleton and maintenance of the fetal immune system (Sabina et al., 2019; El-Khashab et al., 2013; Sommer et al., 2012,). During the period of pregnancy, there is a high demand for vitamin A, particularly in the third quarter because of the fast development of the fetus (WHO, 2013). According to the WHO report of 2009, in some developing countries, Vitamin A deficiency (VAD) is still addressed as a public health issue affecting approximately 19 million pregnant women. According to Younis et al. (2015), the issue of VAD among age groups in developing countries is linked to poor feeding habits and almost 130 million of all preschool children are suffering from a deficiency of vitamin A (UNSCN., 2005). The issue of malnutrition can be addressed via a composite blend of cereals such as millet and guinea corn processed into valuable food products (Akhihiero et al., 2022). According to Nwokem et al. (2019) report that composite blends from locally available food commodities such as cereals have great potential in providing nutritious foods that can address the issues of malnutrition among age groups, pregnant mothers and nursing mothers in Nigeria in particular, and developing countries in general. In a recent study carried out by Akhihiero et al. (2022), revealed that guinea corn and millet can be processed into valuable food products with an additional quantity of the vitamins and minerals needed to boost our immune system. Thus, value-added products produced from cereals are good sources of micronutrients that are needed for body building, they can be used to formulate nutritious and more cost-effective complementary food and drink products that will improve child nutrition, reduce morbidity and mortality rates (Ikokoh et al., 2019). To the best of our knowledge, none of these studies have attempted to statistically optimize the concentration of vitamin A, in Red guinea corn and Millet. This study, thus, aims to investigate the optimization of vitamin A in red guinea corn-millet mix using the Response Surface Methodology. 2.0 Materials and Methods 2.1 Collection and Pretreatment of Raw Materials The millet (Pennisetum glaucum) and red guinea corn (Sorghum bicolor) grains used in this study were purchased from the midwifery market, a local market, in Asaba, Delta State, Nigeria. The millet and red guinea corn grains were sorted to remove sand, dust, dirt and other unwanted materials, and then washed in clean water and sun-dried for 7 to 14 days. file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:amusatramoni@gmail.com Eyide et al: Optimization of Vitamin A in Red Guinea Corn-Millet Mix. AZOJETE, 19(4):759-770. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: sunday.iweriolor@unidel.edu.ng 761 2.2 Experimental Design A three-factor Box-Behnken design for response surface methodology was used to study the combined effect of the blending ratio of the mass of red guinea corn, the mass of millet and blending time on the concentration of Vitamin A. The range and levels of the independent variables are shown in Table 1 (Eyide et al., 2023). Table 1: Coded and actual levels of the factors for three factors of Box-Behnken design for optimization of vitamin A. Independent Variables Symbols Coded and Actual Levels -1 0 +1 Mass of red guinea corn (g) X1 10.00 30.00 50 Mass of millet (g) X2 50.00 75.50 100.00 blending time(hours) X3 1.50 3.25 5.00 The Box-Behnken design has been established to be appropriate for the investigation of quadratic response surfaces and this design generates a second-degree polynomial model which can be used for optimization purposes (Amenaghawon et al., 2013). The number of experimental runs for this design was obtained from Equation (1). N= k2 + k + cp (1) Where k is the number of factors and cp is the number of replications at the center point. The design for the evaluation of the concentration of vitamin A in millet and guinea corn was developed using Design Expert® 7.0.0 (Stat-ease, Inc. Minneapolis, USA) and 17 experimental runs were obtained. The coded and actual values of the independent variables were calculated using Equation (2). − =  i o i i X X x X (2) Where xi and Xi are the coded and actual values of the independent variable respectively. Xo is the actual value of the independent variable at the center point and ΔXi is the step change of Xi. The following generalized second-degree polynomial equation was used to estimate the response of the dependent variable (Amenaghawon et al., 2013). = + + + +   2 i o i j ij i j ii i iY b b X b X X b X e (3) Where Yi is the dependent variable or predicted response, Xi and Xj are the independent variables, bo is the offset term, bi and bij are the single and interaction effect coefficients and ei is the error term. The Design Expert software was used for regression and graphical analysis of the experimental data. The goodness of fit of the models for the concentration of vitamin A was evaluated by the coefficient of determination (R2) and analysis of variance (ANOVA). The grains were weighed separately, then mixed together and finally homogenized to get each blend according to the design of experiment in Table 2. http://www.azojete.com.ng/ mailto:amusatramoni@gmail.com Arid Zone Journal of Engineering, Technology and Environment, Dec, 2023; Vol. 19(4):759-770. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: sunday.iweriolor@unidel.edu.ng 762 Table 2: Box Behnken Experimental Design 2.3 Analysis of millet, red guinea corn and their blends The analyses were carried out at the Central Research and Diagnostic Laboratory, Tanke, Ilorin, Kwara State, Nigeria. The proximate analysis was done on the grains of millet and red guinea corn respectively as well as the determination of the concentration of vitamin A for each blend according to the Box Behnken experimental design in Table 2. The concentration of vitamin A was determined using Agilent 6890 Gas Chromatography (GC). 2.4 Proximate Analysis The method of the Association of Official Analytical Chemists (AOAC 2005) was used to determine the amount of moisture, ash, fat, protein, crude fiber, and carbohydrate contents of the pure grains of millet and red guinea corn. The percentage of carbohydrates was determined by Eq (4) Carbohydrate (%) = 100- (protein (%) + Moisture (%) + Ash (%) + crude Fibre (%) + Fat (%)). (4) While the Energy or Caloric Value was determined by Eq (5) (KJ/100g) = (Protein X 16.7) + (Lipids X 37.7) + (Carbohydrate X 16.7) (5) 2.4 Preparation and analysis of each blend using 6890 GC Based on the experimental design in Table 2, each blend was converted into a more volatile derivative for analysis. Firstly, mix grains were milled into homogenized sizes. Thereafter the milled grains were placed inside flat-bottomed flasks and were suspended in 20 to 40 ml of water according to the mixture times for the respective experimental runs. Care was taken to ensure that no lumps were formed; the blend must be fully wetted with water. While spinning the flask, 100 ml ethanol, 1g sodium ascorbate or L-ascorbic acid and 25 ml potassium hydroxide solution (60%) were added to the suspended sample. The flat-bottomed flasks were sealed with a glass stopper and shaken vigorously. The reaction mixture is now heated to boiling point under reflux in a water bath that has been preheated to 80 - 90°C and maintained at boiling point for 30 minutes under a slow flow of nitrogen to hydrolyze the ester bonds in the vitamin A. The saponified solutions were transferred quantitatively to a separating funnel through multiple rinses with water. To avoid the formation of emulsions, sufficient water was Std Run Block Factor 1 A: mass of red guinea corn (g) Factor2 B: mass of millet (g) Factor3 C: Blending Time (hours) 13 1 Block1 30 75 3.25 15 2 Block1 30 100 5 10 3 Block1 50 100 3.25 16 4 Block1 30 75 3.25 9 5 Block1 10 75 5 11 6 Block1 10 50 3.25 7 7 Block1 30 75 3.25 1 8 Block1 50 75 1.5 5 9 Block1 30 50 1.5 6 10 Block1 10 75 1.5 8 11 Block1 30 50 5 12 12 Block1 30 75 3.25 2 13 Block1 30 100 1.5 17 14 Block1 50 75 5 14 15 Block1 50 50 3.25 3 16 Block1 10 100 3.25 4 17 Block1 30 75 3.25 file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:amusatramoni@gmail.com Eyide et al: Optimization of Vitamin A in Red Guinea Corn-Millet Mix. AZOJETE, 19(4):759-770. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: sunday.iweriolor@unidel.edu.ng 763 added to the saponified sample solution to ensure that the alcohol/water ratio in the resulting solution was about 1:1; thereafter, 150 ml petroleum ether was added and shaken mechanically for about 10 minutes to extract the vitamin A. The extraction solution is now washed neutrally with purified water through multiple shaking and the washing water is allowed to drain away after the phases have separated. The extraction solution was filtered through a phase separation filter into a 50 ml volumetric flask to remove any potential suspended water drops. Finally, the extracted vitamin A was derivatized at 70 - 80°C for 2hours to form a more volatile derivative for GC analysis. After derivatization, the volatile derivative of vitamin A, was analyzed using Agilent 6890 gas chromatography (GC) with a suitable stationary phase and detector. 3. Results and Discussion 3.1 Proximate Analysis Results Table 3 shows that millet has a moisture content of 20.04% which is slightly higher than that of red guinea corn, 17.28%. similarly, the ash content of 2.07% observed with red guinea corn was higher when compared to 1.38% observed in millet. Red guinea corn has 55.23% of carbohydrates while millet has 49.54%. Table 3: Proximate Composition of Red Guinea corn and Millet grain samples Proximate Parameter Samples Red Guinea Corn (A) Millet (B) Moisture % 17.28 20.04 Ash % 2.07 1.38 CHO% 55.23 49.54 Crude Fibre % 1.48 2.44 Crude Lipids% 14.59 17.84 Crude Protein % 9.34 8.76 Calorific Value kJ/100g 1628.62 1646.02 A calorific value of 1646.02kJ/100g was observed in millet which was higher than 1628.62kJ/100g in red guinea corn. The crude fiber was higher in millet (2.44%) as compared to that of red guinea corn (1.48%). Crude lipids content was higher in millet (17.84%) as compared to the one observed with red guinea corn (14.59%). Red guinea corn was higher in crude protein contents (9.34%) compared to that millet of 8.76%). Figure 1 is a chromatogram showing the different peak identification for the vitamers present in red guinea corn. the major vitamers present are rentinol and dihydroretinol with highest peaks identified for retinol and dihydroretinol at retention time of 1.798mins and 2.573mins with a corresponding voltage values of 13mv and 2mv respectively. The other vitamers was unidentified by the GC analyzer because they are present in minute amount. http://www.azojete.com.ng/ mailto:amusatramoni@gmail.com Arid Zone Journal of Engineering, Technology and Environment, Dec, 2023; Vol. 19(4):759-770. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: sunday.iweriolor@unidel.edu.ng 764 Figure 1: Chromatogram for sample A (red guinea corn) Figure 2 is a chromatogram showing the different peak identification for the vitamers present in millet. the major vitamers present are rentinol and dihydroretinol, with highest peaks identified for retinol and dihydroretinol at retention time of 1.648mins and 2.032mins with a corresponding voltage value of 12mv and 7mv respectively. The other vitamers was unidentified by the GC analyzer because they are present in minute amount. Figure 2: Chromatogram for sample B (millet) Table 4: Concentration of vitamin A vitamers present in sample A (red guinea corn) file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:amusatramoni@gmail.com Eyide et al: Optimization of Vitamin A in Red Guinea Corn-Millet Mix. AZOJETE, 19(4):759-770. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: sunday.iweriolor@unidel.edu.ng 765 Table 5: Concentration of vitamin A vitamers present in sample B (millet) From Tables 4 and 5, it can be depicted that sample B (millet) has a higher amount of retinol and dihydroretinol of 52.7872 (µg/100g) and 46.7549 (µg/100g) compared to red guinea corn with 33.0855 (µg/100g) of retinol and 13.7448 (µg/100g) of dihydroretinol respectively. Due to high Concentration of A vitamers present in millet than red guinea corn, we decided to used 1:2 of red guinea corn and millet in our design of experiment as shown in Table 2, in other to optimized the concentration of vitamin A in the cereals blends. 3.2 Statistical model equation for the concentration of vitamin A The Box-Behnken design resulted in 17 experimental runs as shown in Table 2. Eq. (6) was obtained after applying multiple regression analysis to the experimental data. These second- degree polynomial equations were used to estimate the response (concentration of Vitamin A). Y1=+82.34 + 9.56X1 + 9.47 X2 +0.75 X3+5.45X1X2-0.75 X1X3+0.75 X2X3-11.20 X1 2-1.22 X2 2 +3.56X3 2 (6) Where, Y1 = predicted responses for the concentration of Vitamin A (µg/100g), X1X2X3 = A, B, C coded values for the mass of red guinea corn, mass of millet and blending time respectively. Results from the laboratory for the 17 experimental runs where inputted and tally according to the runs in the Design Expert® 7.0.0 (Stat-ease, Inc. Minneapolis, USA) software, thereafter the data were analyzed to generate the statistical model equation and anova for the concentration of vitamin A. The inputted values are referred to as the actual values while the values gotten from the model equation are referred to as predicted values. Table 6: Box Behnken Design Matrix for the optimization variables and response values of concentration of vitamin A (µg/100g). The values of the concentration of vitamin A (µg/100g), as predicted by model Equation (6), were shown in Tables 6, alongside with their experimental. It was observed from the data in the table 6, that there is correlation between the actual and predicted values. Run No Variables Response Coded levels Actual values Vitamin A (µg/100g) X1 X2 X3 X1 X2 X3 Actual Predicted 1 0 0 0 30 75 3.25 84.34 82.34 2 0 1 1 30 100 5.00 92.32 94.9 3 1 1 0 50 100 3.25 96.21 94.4 4 0 0 0 30 75 3.25 84.34 82.34 5 -1 0 1 10 75 5.00 65.12 66.64 6 -1 -1 0 10 50 3.25 54.53 56.34 7 0 0 0 30 75 3.25 79.34 82.34 8 1 0 -1 50 75 1.50 85.78 85.76 http://www.azojete.com.ng/ mailto:amusatramoni@gmail.com Arid Zone Journal of Engineering, Technology and Environment, Dec, 2023; Vol. 19(4):759-770. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: sunday.iweriolor@unidel.edu.ng 766 9 0 -1 -1 30 50 1.50 78.53 75.21 10 -1 0 -1 10 75 1.50 62.12 64.39 11 0 -1 1 30 50 5.00 78.53 75.21 12 0 0 0 30 75 3.25 84.33 82.34 13 0 1 -1 30 100 1.50 89.32 85.53 14 1 0 1 50 75 5.00 85.78 84.26 15 1 -1 0 50 50 3.25 59.71 64.56 16 -1 1 0 10 100 3.25 69.23 64.38 17 0 0 0 30 75 3.25 79.36 82.34 Table 7: ANOVA for a model representing the concentration of vitamin A (µg/100g). ANOVA result depicted that the model for the concentration of vitamin A (µg/100g) was statistically significant with p values of 0.0016, as shown in Table 7. The model did not show a lack of fit as seen from the “lack of fit” p values (0.0822). For the model, the terms representing the mass of red guinea corn, mass of millet and blending time were significant for response (the concentration of vitamin A (µg/100g). Sources Sum of Squares Df Mean Squares F value p-value [Prob>F] Model 2153.40 9 239.27 12.24 0.0016 X1 731.15 1 731.15 37.40 0.0005 X2 717.83 1 717.83 36.71 0.0005 X3 4.50 1 4.50 0.23 0.6460 X1X2 118.81 1 118.81 6.08 0.0431 X1X3 2.25 1 2.25 0.12 0.7444 X2X3 2.25 1 2.25 0.12 0.7444 X1 2 527.93 1 527.93 27.00 0. 0013 X2 2 6.29 1 6.29 0.32 0.5882 X3 2 53.29 1 53.29 2.73 0.1427 Residual 136.86 7 19.55 Lack of Fit 107.06 3 35.69 4.79 0.0822 Pure Error 29.80 4 7.45 Cor Total 2290.27 16 Table 8: Statistical information for ANOVA concentration of Vitamin A (µg/100g), in the mix. Statistical information for ANOVA shows that the model describing the concentration of vitamin A (µg/100g) had a high coefficient of determination (R2) of 0.94 as shown in Table 8. This shows that the model was able to adequately represented the relationship between the chosen factors (mass of red guinea corn, mass of millet and blending time) and response (concentration of vitamin A). R2 values of 0.94 means that the model was able to account for 94.02% of the variability observed in the values of concentration of vitamin A. The standard deviation was observed to be relatively small compared to the mean. The coefficient of variation of 5.65 was obtained for the model. This parameter shows the degree of precision with which the runs were carried out. The values obtained show high reliability as recommended by (Montgomer, 2005). The Adequate precision for the model signifies adequate signal meaning that the model can be used to navigate the design space (Cao, 2009). file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:amusatramoni@gmail.com Eyide et al: Optimization of Vitamin A in Red Guinea Corn-Millet Mix. AZOJETE, 19(4):759-770. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: sunday.iweriolor@unidel.edu.ng 767 Parameter Response Concentration of Vitamin A (µg/100g). R-Squared 0.94 Mean 78.17 Standard Deviation 4.42 C.V% 5.66 Adeq. Precision 11.59 Adjusted R-Square 0.86 3.3 Optimization of concentration of Vitamin A (µg/100g) in the blend. Response surface methodology was used to optimise the process. This was achieved by generating response surface plots showing the three-dimensional relationships among the mass of red guinea corn, mass of millet and blending time on the concentration of vitamin A. Figure 3, shows the effect of blending ratios of the mass of red guinea corn and millet on the concentration of vitamin A. The trend observed shows that the concentration of vitamin A, increased with an increase in the mass of red guinea corn and mass of millet, this is due to the resultant amount of the vitamers (retinol and dihyroretinol) present in the respective cereals, with millet having the highest contribution of vitamers concentration to the blend as shown in tables 4 and 5. Similar trends were observed in Figure 4 and Figure 5, shows that an increase in the blending time against the amount of the red guinea corn and amount of millet has a significant effect on the concentration of Vitamin A which is due to the interacting effect during homogenization between the mass of red guinea and millet. Figure 3. Effect of amount of red guinea corn and millet on concentration of vitamin A. Concentration of vitamin A (µg/100g) Millet loading (g) Red guinea loading (g) http://www.azojete.com.ng/ mailto:amusatramoni@gmail.com Arid Zone Journal of Engineering, Technology and Environment, Dec, 2023; Vol. 19(4):759-770. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: sunday.iweriolor@unidel.edu.ng 768 Figure 4. Effect of amount of red guinea corn and blending time on concentration of vitamin A. Figure 5. Effect of amount of millet and blending time on concentration of vitamin A. The optimum levels of the independent factors and the response (Concentration of Vitamin A) were determined from numerical optimisation of the statistical model (Eq 6) and the top five results were obtained as shown in Table 9. The best optimal result of 98.79 (µg/100g) of Vitamin A, was obtained at a blending time of 5.00 hours, 49.79g of red guinea corn and 100g of millet. Table 9: Solutions for optimum conditions for concentration of vitamin A Solution Number Red guinea corn (g) Millet(g) Blending Time(hours) Concentration of Vitamin A (µg/100g) 1 49.79 100.00 5.00 98.79 2 50.00 100.00 5.00 98.71 3 50.00 100.00 4.99 98.64 4 50.00 99.77 5.00 98.59 5 50.00 100.00 4.96 98.51 Concentration of vitamin A (µg/100g) Concentration of vitamin A (µg/100g) Red guinea loading (g) Blending time (hours) Millet loading (g) Blending time (hours) file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:amusatramoni@gmail.com Eyide et al: Optimization of Vitamin A in Red Guinea Corn-Millet Mix. AZOJETE, 19(4):759-770. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: sunday.iweriolor@unidel.edu.ng 769 3.4 Validation of Statistical Models Three validation experimental runs were performed at the chosen optimum conditions to validate the statistical model representing concentration of Vitamin A (µg/100g). The result shows that the maximum Concentration value of 98.76 µg/100g for vitamin A, obtained was close to the predicted values of 98.79 µg/100g. The excellent correlation between the predicted and measured values of these experiments shows the validity of statistical models. 4. Conclusions The concentration of vitamin A, in the blends were influenced at a blending time of 5hours, 49.79g of red guinea and 100 g of millet. A quadratic statistical model developed to represent concentration of Vitamin A, showed a good fit with the experimental data with R2 values of 0.94. The best blend was produced at the optimized value of 98.79 µg/100g which conforms with World Health Organization (WHO)/Food and Agricultural Organization (FAO), specification for recommended safe intake for all age groups, pregnant and nursing mothers. Conflict of Interest The authors declare no conflict of interest, financial or otherwise References Akhihiero, ET., Olaseinde, AA., Eyide, O. 2022. Effect of Blending Ratio on the Nutritional Value of Millet and Guinea Corn using Mixture Design. International Journal of Innovative Science and Research Technology, 976(7): 757-765. Amenaghawon, NA., Nwaru, KI., Aisien, FA., Ogbeide, SE. and Okieimen, CO. 2013. Application of Box-Behnken Design for the Optimization of Citric Acid Production from Corn Starch Using Aspergillusniger Br. Biotechnology Journal, (3): 236. AOAC. 2005. Official method of Analysis. 18th Edition, Association of Officiating Analytical Chemists, Washington DC, Method 935.14 and 992.24. 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