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© 2020 by the authors; licensee Asian Online Journal Publishing Group 
 

Agriculture and Food Sciences Research 
Vol. 7, No. 1, 79-88, 2020 

ISSN(E) 2411-6653/ ISSN(P) 2518-0193 
DOI: 10.20448/journal.512.2020.71.79.88 

© 2020 by the authors; licensee Asian Online Journal Publishing Group 

    
 

 
 
 
Optimization of Proximate and Minerals Compositions of Sweet Potato, Soybean 
and Rice Bran Composite Flours for Production of Low Glycemic Index Dough 
Meal 

 
Awolu, O.O.1   
Ajibola, C.F.2 
Adeloye, J.B.3 
Ogwu, F.4 

 
 

( Corresponding Author) 
 
1,2,3,4Department of Food Science and Technology, Federal University of Technology, Akure, Nigeria. 

 

 
Abstract 

Dough meal with low glycemic index was produced from sweet potato, soy bean and rice bran 
composite flours. Effect of the composite flour samples on the glycemic index, as well as 
proximate and minerals composition of the dough meal were optimised using optimal mixture 
design of response surface methodology. The dough meal had considerably high protein and fibre 
contents. The moisture and ash contents were low. The results indicated that the addition of 
sweet potato enhances the minerals composition, while soybean and rice bran had highest effect 
on the protein and fibre contents respectively. The results of the optimisation showed that the 
dough meal had glycemic index, meaning that the dough meal prepared from sweet potato (65.0-
85.0 g/100g), soy bean (10.0-25.0 g/100g) and rice bran (2.5-10.0 g/100g) had acceptable 
glycemic index. The dough meal with 75.00 g/100g sweet potato flour, 15.00 g/100g soybean 
meal flour and 10.00 g/100g rice bran flour, however had the lowest glycemic index. 

 
Keywords: Dough meal, Fibre, Glycemic index, Optimisation, Protein. 

 
Citation | Awolu, O.O.; Ajibola, C.F.; Adeloye, J.B.; Ogwu, F. 
(2018). Optimization of Proximate and Minerals Compositions of 
Sweet Potato, Soybean and Rice Bran Composite Flours for 
Production of Low Glycemic Index Dough Meal. Agriculture and 
Food Sciences Research, 7(1): 79-88. 
History:  
Received: 7 February 2020 
Revised: 16 March 2020 
Accepted: 20 April 2020 
Published: 18 May 2020 
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Acknowledgement: All authors contributed to the conception and design of 
the study. 
Funding: This study received no specific financial support. 
Competing Interests: The authors declare that they have no conflict of 
interests. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study was reported; that no vital 
features of the study have been omitted; and that any discrepancies from the 
study as planned have been explained. 
Ethical: This study follows all ethical practices during writing.   

 

 

Contents 
1. Introduction ...................................................................................................................................................................................... 80 
2. Materials and Methods ................................................................................................................................................................... 80 
3. Results and Discussion ................................................................................................................................................................... 81 
4. Conclusions ....................................................................................................................................................................................... 83 
References .............................................................................................................................................................................................. 83 
 

 
 
 
 
 

 

 

 

 

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Contribution of this paper to the literature 
This study contributes to the existing literature on low glycaemic index foods by utilizing 
locally, cheaply and widely available raw materials (sweet potato, soybean and rice bran) in the 
preparation of low glycaemic index foods, rich in protein and dietary fibre. The effects of the 
raw materials on the chemical composition of the food were optimized using response surface 
methodology in order statistically arrive at best blends rich in protein, dietary fibre and 
minerals composition. 

 
1. Introduction 

The understanding of the impact of foods on actual blood sugar is defined as the glycaemic index. It 
differs from measurement of the carbohydrate contents in food. In this wise, food are ranked as very low, 
low, medium or high in glycemic index. It has been observed that foods with very low or low glycemic 
index are very good for people with cardiovascular diseases (especially type 2 diabetes, metabolic 
syndrome, stroke and depression).  

Sweet potato is a popular low-fat diet and with a low glycemic index (GI), which makes it very 
healthy for diabetes patients. The most important edible parts are the roots and immature leaves, which 
are used for human consumption, animal feed and to some extent, for industrial purposes. Sweet potato 
(Ipomoea batatas) is rich protein, carbohydrates, minerals (calcium, iron, and potassium), betacarotenoids, 
dietary fibre, vitamins (especially C, folate, and B6), very little fat, and sodium [1]. As an excellent source 
of Vitamin A, it can play an important role in the fight against vitamin A deficiency (VAD).  

Rice bran is a by-product of rice processing and rich in nutrients. It has very high fibre content and 
contain proteins are of high nutritional value [2]. The proteins are also rich in essential amino acids, 
especially lysine, hence can be used as ingredients in food recipes [3]. It is rich in vitamins including 
vitamin E, thiamin, niacin and minerals like aluminum, calcium, chlorine, iron, magnesium, manganese, 
phosphorus, potassium, sodium and zinc [4, 5].  

The soybean seeds contain high quality and quantity of protein. Its amino acid composition is 
approximate to composition of animal proteins, and therefore it is often used as replacement component 
of meat protein. Lysine, a limiting amino acid in cereal proteins is abundant in soybeans hence the use of 
soybeans as supplementary and complementary lysine source in cereal-based products.  

The application of composite flour has resulted in flours and food products with improved 
compositional and nutritional qualities [6, 7]. Use of composite flour encourages the utilization of local 
crops and reduces the expenses on wheat importation. In addition, composite flour has been optimized 
using response surface methodology in order to evaluate the significant effect of the raw materials on 
some nutritional and rheological qualities of composite flour [7, 8]. 

The aim of this research was to investigate the proximate and minerals compositions of dough meal 
from composite flour comprising of sweet potato flour, rice bran flour, and soymeal flour. The 
compositions will be optimised using response surface methodology, in order to evaluate the significant 
effect of the raw materials on the minerals and proximate compositions. The glycemic index of the 
optimized meal will be evaluated. 
 

2. Materials and Methods 
2.1. Materials 

Sweet potato was obtained from a local market, Akure, Ondo state. Rice bran was purchased from 
Igbemo –Ekiti, Ekiti State. Soybean meal was obtained at JOF, Owo, Ondo state. All chemicals and 
reagents used was of analytical grade. 
 
2.2. Sweet Potato Flour Preparation  

Sweet potato tubers were sorted, washed, blanched at 80 oC for 5 min, peeled, sliced and dried at 60 oC 
for 24 h. Thereafter, it was milled using the hammer mill and passed through mesh size of 250 µm to have 
a fine flour which was then packed in an airtight container for further use.  
 
2.3. Soybean Meal Flour Preparation 

The soybean meal was milled and sieved to obtain a fine soybean meal flour according to IITA 
procedure [9].  
 
2.4. Rice Bran Flour Preparation 

The rice bran was destoned, milled and sieved through a 250 µm mesh size to produce a fine rice bran 
flour [10].  
 
2.5. Experimental Design  

The experimental design was carried out using optimal mixture model of response surface 
methodology (Design Expert 8.0.3.1 software). The responses were proximate properties (protein, 
carbohydrates, ash, crude fibre, fat and moisture) and mineral (calcium, iron, potassium, magnesium, 
phosphorus, sodium and zinc) composition, while the variables were the raw materials (sweet potato flour, 
rice bran flour and soybean meal flour), as presented in Table 1. 

 



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Table-1. Factor design and their level of mixture. 

Run Component 1 
A: g/100g 

Component 2 
B: g/100g 

Component 3 
C: g/100g 

1 65.000 25.000 10.000 
2 85.000 12.500 2.500 
3 85.000 10.000 5.000 
4 77.500 16.500 6.000 
5 72.500 25.000 2.500 
6 76.667 20.833 2.500 
7 85.000 12.500 2.500 
8 75.000 15.000 10.000 
9 85.000 10.000 5.000 
10 70.000 20.000 10.000 
11 68.750 25.000 6.250 
12 72.500 25.000 2.500 
13 65.000 25.000 10.000 
14 80.000 10.000 10.000 
15 80.000 10.000 10.000 
16 73.125 20.750 6.125 

Note: Where, A= Sweet potato flour;  B= Soybean meal flour;  C= Rice bran flour. 
 
2.6. Dough Preparation 

Dough was made from the composite flours in the right proportion Table 1 with hot water in a ratio 
of 100 g of flour to 25 g of water to form a thick paste and then was placed on a gas cooker at a 
temperature of 45 oC for about 10 min. A good stirring was done and dough obtained was allowed to cool.  

 
2.7. Determination of the Proximate Composition of Dough Meal 

The proximate composition including moisture, ash content, crude protein, crude fat and crude fibre 
of the dough meal were determined using AOAC methods [11]. The carbohydrate was estimated by 
difference. 
 
2.8. Mineral Analysis of the Dough Meal 

Determination of calcium, magnesium, zinc and iron was by Atomic Absorption Spectrophotometer 
(AAS), while potassium, phosphorus and sodium was determined by flame photometry method following 
AOAC procedures [11]. 
 
2.9. Glycemic Index of the Dough Meal 

The glycemic index was determined by using Wolever method [12]. The glycemic index of the food 
samples were determined by feeding the samples to rat for a period of 2 h. The bloods of the rats were 
then used to calculate for the glycemic index level in the food samples.  
 
2.10. Statistical Data Analysis 

All analysis was done in triplicate and results presented as the average of triplicate determinations, 
expressed as mean ± standard deviation (S.D). The data were subjected to the statistical analysis of 
variance (ANOVA) using SPSS software version 17.0. 
 

3. Results and Discussion 
3.1. Proximate Compositions of Dough Meal 

The result of the proximate composition is presented in Table 2, and the contour plots presented in 
Figures 1a to 1f. The carbohydrate content had no significant model terms, while the R2 and adjusted R2 
values were also low Figure 1a, despite the fact that the values of the carbohydrate contents vary from 
57.32 to 62.52%. The values could indicate that although, the dough meal would be a good source of 
energy, yet, it would not be a high glycemic index food.  

The fat content was moderately high (9.09-13.19 g/100g). Soybean supports the fat content of the 
composite flour as shown in the contour plot Figure 1b. The significant model terms (linear mixture, AB, 
A2BC) also showed that sample C (rice bran) did not significantly (p> 0.05) contribute to the fat content 
of the composite flour Table 2. The fat content of the composite flour is pointer to the fact that it would 
be good as flavour retainer, and a carrier of fat-soluble vitamins.   
 
 
 
 
 
 
 



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Table-2. ANOVA results for proximate composition. 

Parameter Model (significant level) R2 Adj 
R2 

Lack of fit 

(P  0.05) 
Significant (P  0.05) 
terms 

Carbohydrate cubic 0.8253 0.5633 0.3988 nil 
Crude Fat Special quartic 

(< 0.0001) 
0.9948 0.9889 < 0.0001 Linear mixture, AB, 

A2BC 
Crude 

Protein 
Cubic 

(< 0.0001) 
0.9998 0.9995 0.4135 Linear mixture, AB, 

AC, BC, ABC, AB(A-
B), AC (A-C), BC (B-
C) 

Crude Fibre Special quartic 
(< 0.0001) 

0.9998 0.9995 0.0002 Linear mixture, AC, 
BC, ABC2 

Total Ash Linear mixture 
(0.3131) 

0.1636 0.0349 0.9189 nil 

Moisture Special quartic 
(0.0003) 

0.9607 0.9157 0.5897 Linear mixture, AC, 
AB, AB2C, ABC2 

 
The dough meal showed significant (p <0.05) increase in crude protein content ranging from 8.75- 

10.87% with increase in soybean meal and rice bran flours. However, dough meal with high sweet potato 
flour and low soymeal flour had low protein content [13]. The optimisation results Table 2 showed that 
the protein had high R2 (0.9998) and adjusted R2 (0.9995) values. The model (cubic) and model terms 

(linear mixture, AB, AC, BC, ABC, AB(A-B), AC (A-C), BC (B-C)) were significant (p 0.05) with a non-
significant lack of fit. This R2 and adjusted R2 values together with the significant model terms for protein 
contents indicated that the composite flour prepared in this study had reasonable and acceptable protein 
contents. High R2 and adjusted R2 values (close to one) indicates statistical models that are fit [10, 14]. 
The contour plot representing the effect of the raw materials on the protein content is shown in Figure 

1c. These results showed that the composite flours making the dough significantly (p 0.05) effect the 
protein content. The areas shaded red in the contour plot are the areas with highest protein content. It 
could be seen that the areas with high protein content were as a result of the soy bean and potato flours 
incorporation.  

The crude fibre ranged from 1.96-5.83%, with high R2 and adjusted R2 values Table 2. Rice bran flour 
was the best source of crude fibre as indicated by the R2 value, adjusted R2 value, and significant model 
terms Table 2. The contour plot is shown in Figure 1d. Rice bran is rich in soluble fiber like beta-glucan, 
pectin, gums helpful in reduction of serum cholesterol, certain forms of cancer and constipation [15]. 
Nutritional and functional properties of rice bran are well suited for baked products like cookies, muffins, 
breads, crackers, pastries and pancakes [16].  

The ash contents too had no significant model terms together with very low R2 and adjusted R2 
values Figure 1e and Table 2. Although the total ash contents ranged from 1.49 to 3.00%. 

The moisture content decreased significantly (p 0.05) as in soybean meal and rice bran flour contents 
increased. The R2 and adjusted R2 values were 0.9607 and 0.5897 respectively. The drastic reduction in 
the adjusted R2 value is an indication that the composite flour does not strongly support water content, 
which indicates that the dough meal would be shelf stable. The contour plot for carbohydrate content is 
shown in Figure 1f.  
 
3.2. Minerals Compositions of Dough Meal 

The optimisation results of the minerals composition, consisting the model, R2 value, adjusted R2 
value, lack of fit and significant model terms is presented in Table 3. The contour plot showing the 
graphical illustration of the effect of the raw materials on the dough meal minerals composition is shown 
in Figures 2a to 2g. From the optimisation results in Table 2, only potassium (k), Magnesium (Mg) and 
Calcium (Ca) had considerable R2 and adjusted R2 values meaning that they were enhanced by the raw 
materials. In addition, the dough meal had considerable effect on iron (Fe) content Figure 2a. From the 
contour plot in Figure 2a, sweet potato and soy bean flours were the main sources of iron in the dough 
meal. It has been reported that sweet potato is a reliable source of mineral iron [17].  

The dough meal had low sodium content, as it had no significant (P0.05) model term Table 3. The 
sodium content of the dough meal ranged from 23.62-58.72 mg/kg while the potassium content ranged 
from 180.77-266.79 mg/kg. The contour plot Figure 2b also showed that the raw materials contributed 
very little to the dough meal. None of the raw material increased the sodium content. The considerable 
high potassium content in relation to the considerable low sodium content is an indication that the dough 
meal would be suitable for better cardiovascular functions. High K/Na ratio is recommended for patients 
with high blood pressure (K/Na < 1.0). Sweet potato had positive effect on the potassium content Figure 
2c. The plot also indicated that sweet potato was the main source of potassium.  

 
 

 
 



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Table-3. ANOVA results for minerals composition. 

Parameter Model (significant 
level) 

R2 Adj R2 Lack of fit 

(P  0.05) 

Significant (P 

 0.05) terms 

Fe Cubic 0.8241 0.5603 0.8907 nil 
K Linear mixture 

(p< 0.0001) 
0.7734 0.7385 < 0.0001 Linear mixture 

Mg Linear mixture 
(p< 0.0001) 

0.8150 0.7866 0.9625 Linear mixture 

Ca Special quartic 
(p< 0.0001) 

0.9968 0.9931 0.0028 Linear mixture 

P Linear mixture 
(p=0.0579) 

0.3549 0.2557 0.9267 nil 

Na Quadratic 
(p=0.1890) 

0.4817 0.2225 0.1485 nil 

Zn Mean 0.0000 0.0000 0.8917 nil 

 
The calcium content of the dough meal significantly increased (p<0.05) as the level of soybean meal 

flour increased Figure 2d. The contour plot Figure 2d clearly shown that soybean was the main source of 
calcium, followed by rice bran flour. The phosphorus content of the dough meal significantly (p<0.05) 
increased as the substitution levels of sweet potato in the dough meal increased Figure 2e. The 3D plot 
showed revealed that sweet potato was the richest source of calcium among the raw materials, followed 
by rice bran.  

The zinc content ranged from 0.34-2.09 mg/kg. It was observed from the 3D plot Figure 2f that the 
raw materials were not rich in mineral zinc. Zinc is a micronutrient; hence, the content could be sufficient 
for daily nutritional needs. Soybean flour was the best source of magnesium followed by rice bran flour 
Figure 2g. The magnesium contents for the samples varied from 30.58 to 43.44 mg/Kg.    
 
3.3. Glycemic Index of the Dough Meal 

The results of the optimisation of the proximate and minerals composition has given pointers to the 
possibility of the dough meal to have cardiovascular benefits. The R2 and adjusted R2 values for the 
optimisation carbohydrate composition were 0.5633 and 0.3988 respectively, while it has no significant 
model terms. This showed that the raw materials may not support sugar (from carbohydrate metabolism) 
contents in the dough meal. It implies that the dough meal would be useful for consumers against 
incidences or prevention of diabetic mellitus.  

The glycemic index (GI) of the dough meal ranged from 22.66 to 45.37. They can however, be 
classified as low GI meal. Low GI foods have been shown to release glucose slowly and steadily [18]. 
They are slowly digested, absorbed and metabolised, leading to a slower rise in blood glucose level.  

The sample with 75.00 g/100g sweet potato flour, 15.00 g/100g soybean meal flour and 10.00 
g/100g rice bran had the least glycemic index. The profile of glycemic evaluation of the dough meal from 
composite flour blends of sweet potato, soybean meal and rice bran in their different mixing ratio is 
shown in Figure 3.  
 

4. Conclusions  
The dough meal prepared from flour blends of sweet potato, soybean meal and rice bran flour 

exhibited good and acceptable glycemic index properties. In addition, the dough had high protein and 
fibre contents. The Na/K ratio of the dough meals were lower than 1; a pointer to antihypertensive 
capacity. The composite flour with 75.00 g/100g sweet potato flour, 15.00 g/100g soybean meal flour 
and 10.00 g/100g rice bran, however had the lowest glycemic index.  
 

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Figure-1a. Contour plot showing the effect of variables on carbohydrate content. 

      

 
Figure-1c. Contour plot showing the effect of variables on protein content. 

               

 
Figure-1d. Contour plot showing the effect of variables on fibre content. 



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Figure-1e. Contour plot showing the effect of variables on ash content. 

                         

 
Figure-1f. Contour plot showing the effect of variables on carbohydrate. 

 
 

 
Figure-2a. Contour plot showing the influence of variables on iron (Fe). 

                      

 



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Figure-2b. Contour plot showing the influence of variables on sodium (Na). 

                         

 

 
Figure-2c. Contour plot showing the influence of variables on potassium (k). 

                           

 
Figure-2d. Contour plot showing the influence of variables on calcium (Ca). 

                
 



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Figure-2e.Contour plot showing the influence of variables on phosphorus (P). 

                                    

 
Figure-2f. Contour plot showing the influence of variables on Zinc (Zn). 

                    

 
Figure-2g. Contour plot showing the influence of variables on magnesium (Mg). 

                         
 



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Figure-3. The glycemic index of the dough meal. 

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