Modelling the properties of composite flour of maize–Pleurotus tuber-regium sclerotium for meal production using simplex-centroid design

Abstract

This study modelled the properties of composite flour of maize and Pleurotus tuber-regium sclerotium (PTS) and the sensory properties of their meal. The simplex-centroid design was used for the experimental design. The properties of composite flour and their meal were determined. The indices indicating the validity of generated models were coefficient of determination (R²) values above 80%, except for swelling index and colour, with the average absolute deviation values close to 0 and bias factor and accuracy factor values approaching 1. The models adequately described the properties studied. The PTS flour improved crude protein and fibre contents, while maize and PTS flours had the synergistic effect of improving the antioxidant properties, of the composite flour. With an increase in maize flour, there was an improvement in taste, texture and overall acceptability of the meal. However, an increase in PTS flour resulted in an increase in colour preference and a decrease in taste, aroma and overall acceptability of the meal. PTS flour could be used to improve maize-based meal for food security, but a functionality-enhancing agent may need to be incorporated into the flour for better properties of the meal.

Keywords:

• modelling
• properties
• maize–Pleurotus tuber-regium sclerotium flour
• meal

1. Introduction

Maize (Zea mays) is one of the essential annual cereal crops used as a staple food worldwide, after rice and wheat (Shah et al., 2016). It serves as the vital basis of food security for millions of people in most developing countries, particularly in sub-Saharan Africa (Ai & Jane, 2016). Maize grain is a major source of carbohydrates (mostly starch), protein, vitamin B, vitamin A (yellow maize) and minerals. Maize is predominantly starch (60–75%), in the form of amylose and amylopectin. The protein content of maize is low, constituting only 9–12% when compared with legumes. In sub-Saharan Africa, about 65% of maize produced is utilized in the production of different food products such as dumplings, breads, fermented dough, snacks and tortilla (Ranum et al., 2014).

Maize meal is a gel-like dumpling produced by heating maize flour slurry to obtain a smooth dough, and it is one of the most important staple foods in many African and Asian countries (Adedeji & Tadawus, 2019). However, many commonly available maize varieties from which meal can be produced are poor in protein quality and quantity (Adesanmi et al., 2020). Hence, there is a need for complementing such food products with a good protein source.

Edible mushrooms are the fungi consumed as vegetable globally (Wan Rosli et al., 2015). They provide key nutrients such as amino acids, monosaccharides, dietary fibres and many bioactive compounds (Painuli et al., 2020). The findings of several studies support the fact that edible mushroom extracts exhibit promising therapeutic and health-promoting benefits, particularly in relation to diseases associated with inflammation; anticancer activities; anti-atherosclerotic, antihypertensive and cholesterol-lowering effects; and anti-aging and antioxidant properties (Akinwande & Abegunde, 2013; Muszyńska et al., 2018; Sun et al., 2019). Mushroom is considered an alternative functional food in improving the nutritional quality of some food products (Bolade et al., 2002). Pleurotus tuber-regium is a tropical sclerotal mushroom, which produces sclerotium that is rich in nutrients, especially protein (13.0–16.8%), fibre (5.0–16.7%) and potassium (2.45–9.56%) (Akindahunsi & Oyetayo, 2006). Due to its nutritional properties, it has been used to enrich food products such as orange-fleshed sweet potato-based cookies (Kolawole et al., 2020).

Simplex-centroid design (SCD) is an experimental design method commonly applied in industrial product formulations such as food processing, chemical formulations, textile fibres and pharmaceutical drugs for evaluating the effects of components on the properties of a product (Dengwu et al., 2018).

Due to endemic malnutrition in some countries of the world, there is a need for improvement in the nutritional quality of food staples through the incorporation of a suitable nutritious food source that is readily available and affordable to the malnourished populace (Hassan et al., 2020).

Considering good nutritional and antioxidant status of Pleurotus tuber-regium (the king tuber mushroom) sclerotium (PTS), it has the potential of improving the nutritional quality of other less nutritious food sources such as maize-based food products. Therefore, this study was geared towards incorporating PTS flour into maize (yellow variety) flour meant for meal production with a view of improving its nutritional and antioxidant properties.

2. Materials and methods

2.1. Production of maize flour

Maize flour was produced from maize (yellow variety) grains as described by Ayo et al. (2008). About 2 kg of maize grains was weighed and sorted followed by conditioning, decorticating, milling, sieving to pass through mesh with a 0.4-mm aperture and packaging in a high-density polyethylene bag.

2.2. Production of Pleurotus tuber-regium sclerotium flour

The PTS was processed into flour as described by Kolawole et al. (2020). Mushroom was washed in potable water, cut into pieces and blanched in 3% hot saline at 100°C for 3 min. Water was drained off the mushroom, followed by drying in hot air oven at 105°C for 3 h. The dried mushroom was milled and sieved to pass through mesh with a 0.4-mm aperture. The flour obtained was packaged in a ziplock bag and kept in an airtight container.

2.3. Experimental design and formulation of maize–Pleurotus tuber-regium sclerotium composite flour

SCD was used for experimental design and formulation of maize–PTS composite flour (Table 1). The design replicated 10 mixtures with design spaces of 0.60–1.00 and 0.00–0.40 for maize flour (X₁) and PTS flour (X₂), respectively. The sum of the proportions of the components was equal to 1 (Equation (1)(1) $\mathrm{\Sigma }\mathrm{X}\mathrm{i}={\mathrm{X}}_1+{\mathrm{X}}_2=1$(1) ).

(1) $\mathrm{\Sigma }\mathrm{X}\mathrm{i}={\mathrm{X}}_1+{\mathrm{X}}_2=1$(1)

Table 1. Experimental design used for maize–PTS composite flour

Treatment	Maize flour (X1)	PTS flour (X2)
1	0.60	0.40
2	0.70	0.30
3	0.60	0.40
4	0.90	0.10
5	0.90	0.10
6	1.00	0.00
7	0.70	0.30
8	1.00	0.00
9	0.80	0.20
10	0.80	0.20

2.4. Determination of nutritional and antioxidant properties

The crude protein and crude fibre of the flour were determined as described by AOAC (2023). The total flavonoid content (TFC) was determined using the method of Meda et al. (2005), while the total phenol content (TPC) and 2,2-diphenyl-1-picrylhydrazyl radical (DPPH) were determined as described by Roesler et al., (2006).

2.5. Determination of functional properties

The water absorption capacity (WAC) and swelling capacity (SC) of the flours were determined according to the method described by Onwuamanam et al. (2011), while total titratable acidity (TTA) and pH were determined according to the method of AOAC (2023).

2.6. Meal production from maize–PTS composite flour

Meal was produced from the formulated maize–PTS composite flour. A cold slurry of the flour was obtained by mixing 20% of the flour (1.0 kg) with 25% of water. About 60% of the water was heated to boiling point (100°C) and then the slurry was added with constant stirring to ensure a smooth consistency. The remaining quantity of the flour was added gradually with constant stirring. The remaining quantity of water (15 %) was added, and the gel formed was allowed to simmer for 7 min. The cooked mass was then stirred vigorously to make it smooth to form the meal.

2.7. Sensory evaluation of meal from maize–PTS composite flour

An in-house preference test was used for the evaluation of the sensory properties of the meal. Fifty panelists who were familiar with maize-based meal performed the evaluation. The meal was served randomly in coded plates to the panelists, and the products were rated on a 9-point hedonic scale, with 1 representing “dislike extremely” and 9 representing “like extremely” for the different sensory properties evaluated.

2.8. Statistical analysis

Data were subjected to mixture regression analysis using Minitab Statistical Software (Minitab Ltd. Coventry, UK) to generate mathematical models in the form of Equation (2)(2) $\mathrm{Y}={\beta }_1{\mathrm{X}}_1+{\beta }_2{\mathrm{X}}_2+{\beta }_{12}{\mathrm{X}}_1{\mathrm{X}}_2,$(2)

(2) $\mathrm{Y}={\beta }_1{\mathrm{X}}_1+{\beta }_2{\mathrm{X}}_2+{\beta }_{12}{\mathrm{X}}_1{\mathrm{X}}_2,$(2)

where Ŷ is a response; β₁, β₂ and β₁₂ are regression coefficients; X₁ and X₂ are linear factors and X₁X₂ is an interaction factor.

2.9. Model validation

The different statistical parameters utilized in validating the adequacy of the models generated were coefficient of determination (R²), average absolute deviation (AAD), bias factor (Bf) and accuracy factor (Af) as represented by Equation (3(3) $\hspace{0.17em}\mathrm{A}\mathrm{A}\mathrm{D}=\frac{\left[{\sum }_{\mathrm{i}=1}^{\mathrm{N}}(\frac{\left|\mathrm{Y}\mathrm{i},\mathrm{e}\mathrm{x}\mathrm{p}-\mathrm{Y}\mathrm{i},\mathrm{c}\mathrm{a}\mathrm{l}\right|}{\mathrm{Y}\mathrm{i},\mathrm{e}\mathrm{x}\mathrm{p}})\right]}{\mathrm{N}}$(3) -5(5) $\hspace{0.17em}\mathrm{A}\mathrm{f}={10}^{\frac{1}{\mathrm{N}}{\sum }_{\mathrm{i}=1}^{\mathrm{N}}\left|\mathrm{l}\mathrm{o}\mathrm{g}\left(\frac{\mathrm{Y}\mathrm{i},\mathrm{c}\mathrm{a}\mathrm{l}}{\mathrm{Y}\mathrm{i},\mathrm{e}\mathrm{x}\mathrm{p}}\right)\right|}\hspace{0.17em}$(5) ), respectively, as described by Adebo et al. (2017).

(3) $\hspace{0.17em}\mathrm{A}\mathrm{A}\mathrm{D}=\frac{\left[{\sum }_{\mathrm{i}=1}^{\mathrm{N}}(\frac{\left|\mathrm{Y}\mathrm{i},\mathrm{e}\mathrm{x}\mathrm{p}-\mathrm{Y}\mathrm{i},\mathrm{c}\mathrm{a}\mathrm{l}\right|}{\mathrm{Y}\mathrm{i},\mathrm{e}\mathrm{x}\mathrm{p}})\right]}{\mathrm{N}}$(3)

(4) $\mathrm{B}\mathrm{f}={10}^{\frac{1}{\mathrm{N}}{\sum }_{\mathrm{i}=1}^{\mathrm{N}}\mathrm{l}\mathrm{o}\mathrm{g}\left(\frac{\mathrm{Y}\mathrm{i},\mathrm{c}\mathrm{a}\mathrm{l}}{\mathrm{Y}\mathrm{i},\mathrm{e}\mathrm{x}\mathrm{p}}\right)}\hspace{0.17em}$(4)

(5) $\hspace{0.17em}\mathrm{A}\mathrm{f}={10}^{\frac{1}{\mathrm{N}}{\sum }_{\mathrm{i}=1}^{\mathrm{N}}\left|\mathrm{l}\mathrm{o}\mathrm{g}\left(\frac{\mathrm{Y}\mathrm{i},\mathrm{c}\mathrm{a}\mathrm{l}}{\mathrm{Y}\mathrm{i},\mathrm{e}\mathrm{x}\mathrm{p}}\right)\right|}\hspace{0.17em}$(5)

3. Results

3.1. Nutritional and antioxidant properties of maize–PTS composite flours

The linear effects of maize flour (X₁), PTS flour (X₂) and their interaction effect (X₁X₂) on the nutritional and antioxidant properties were studied. The determined parameters were crude protein (Y₁), crude fibre (Y₂), TPC (Y₃), DPPH content (Y₄) and TFC (Y₅). Their models are represented by Equation (6)(6) ${\mathrm{Y}}_1=7.067{\mathrm{X}}_1+11.8{\mathrm{X}}_2+0.93{\mathrm{X}}_1{\mathrm{X}}_2$(6) –(10(10) ${\mathrm{Y}}_5=1.448{\mathrm{X}}_1+0.235{\mathrm{X}}_2+8.857{\mathrm{X}}_1{\mathrm{X}}_2$(10) ).

(6) ${\mathrm{Y}}_1=7.067{\mathrm{X}}_1+11.8{\mathrm{X}}_2+0.93{\mathrm{X}}_1{\mathrm{X}}_2$(6)

(7) ${\mathrm{Y}}_2=1.1274{\mathrm{X}}_1+3.33{\mathrm{X}}_2+0.43{\mathrm{X}}_1{\mathrm{X}}_2$(7)

(8) ${\mathrm{Y}}_3=2.0961{\mathrm{X}}_1+6.31{\mathrm{X}}_2-3.86{\mathrm{X}}_1{\mathrm{X}}_2$(8)

(9) ${\mathrm{Y}}_4=83.904{\mathrm{X}}_1+82.38{\mathrm{X}}_2+82.38{\mathrm{X}}_1{\mathrm{X}}_2$(9)

(10) ${\mathrm{Y}}_5=1.448{\mathrm{X}}_1+0.235{\mathrm{X}}_2+8.857{\mathrm{X}}_1{\mathrm{X}}_2$(10)

As shown in Table 2, the R² values for the parameters ranged from 94.10% to 97.71%, while the Bf and Af values were close to unity and the AAD values were close to zero. With these values, there was an agreement between the experimental and calculated (predicted) values as shown in Table 3. Furthermore, the linear factors of X₁ and X₂ had significant (p ≤ 0.05) effects on crude protein, crude fibre, DPPH and TFC, while they had no significant (p ≥ 0.05) effect on TPC. However, their interaction factor, X₁X₂, had a significant (p ≤ 0.05) effect on DPPH and TFC only.

Table 2. Coefficient of regression for nutritional and antioxidant properties of maize–PTS composite flours

Coefficient	Crude protein	Crude fibre	TPC	DPPH	TFC
β1	7.067*	1.1274*	2.0961	83.904*	1.448*
β2	11.8*	3.33*	6.31	82.38*	0.235*
β12	0.93	0.43	−3.86	32.2*	8.857*
R^2 (%)	94.39	97.59	95.95	97.35	94.1
AAD	0.003	0.03	0.02	0.0005	0.06
Bf	1	1	1	1	1
Af	1.02	1.03	1.02	1	1.06

*Significant at p ≤ 0.05.

β₁ = linear effects of maize flour, β₂ = linear effects of PTS flour, β₁₂ = interaction effects of maize flour and PTS flour, R² = coefficient of determination, AAD = average absolute deviation, Bf = bias factor, Af = accuracy factor, TFC = total flavonoid content, TPC = total phenol content, DPPH = 2,2-diphenyl-1-picrylhydrazyl radical.

Table 3. The experimental and predicted values for nutritional and antioxidant properties of maize–PTS composite flours

Maize flour (X1)	PTS flour (X2)	Crude protein		Crude fibre		TPC		DPPH		TFC
		Exp.	Pre.	Exp.	Pre.	Exp.	Pre.	Exp.	Pre.	Exp.	Pre.
0.8	0.2	8.03	8.16	1.69	1.64	2.24	2.32	89.85	89.07	2.43	2.32
0.9	0.1	7.42	7.62	1.48	1.39	2.19	2.17	86.08	86.83	2.27	2.17
0.7	0.3	8.54	8.68	1.91	1.88	2.64	2.55	90.24	90.63	2.71	2.55
0.8	0.2	8.53	8.16	1.59	1.64	2.24	2.32	89.05	89.07	2.71	2.32
0.6	0.4	9.14	9.18	2.14	2.11	2.82	2.86	91.74	91.50	3.25	2.86
1.0	0.0	7.00	7.07	1.17	1.13	2.10	2.09	84.19	83.90	1.38	2.10
0.6	0.4	9.16	9.18	2.10	2.11	2.83	2.86	91.54	91.50	3.07	2.86
0.7	0.3	8.88	8.68	1.81	1.88	2.64	2.55	90.19	90.63	2.93	2.55
0.9	0.1	7.46	7.62	1.32	1.39	2.19	2.17	87.38	86.83	2.37	2.17
1.0	0.0	7.28	7.07	1.07	1.13	2.10	2.10	83.59	83.90	1.34	2.10

TFC = total flavonoid content, TPC = total phenol content, DPPH = 2,2-diphenyl-1-picrylhydrazyl radical.

3.2. Functional properties of maize–PTS composite flours

The functional properties were WAC (Y₆), SC (Y₇), TTA (Y₈) and pH (Y₉) with their models represented by Equation (11)(11) ${\mathrm{Y}}_6=190.57{\mathrm{X}}_1-350{\mathrm{X}}_2+480{\mathrm{X}}_1{\mathrm{X}}_2$(11) –(14(14) ${\mathrm{Y}}_9=6.6034{\mathrm{X}}_1+6.191{\mathrm{X}}_2-1.321{\mathrm{X}}_1{\mathrm{X}}_2$(14) ).

(11) ${\mathrm{Y}}_6=190.57{\mathrm{X}}_1-350{\mathrm{X}}_2+480{\mathrm{X}}_1{\mathrm{X}}_2$(11)

(12) ${\mathrm{Y}}_7=8.637{\mathrm{X}}_1+19.19{\mathrm{X}}_2-13.64{\mathrm{X}}_1{\mathrm{X}}_2$(12)

(13) ${\mathrm{Y}}_8=0.05986{\mathrm{X}}_1-0.274{\mathrm{X}}_2+0.357{\mathrm{X}}_1{\mathrm{X}}_2$(13)

(14) ${\mathrm{Y}}_9=6.6034{\mathrm{X}}_1+6.191{\mathrm{X}}_2-1.321{\mathrm{X}}_1{\mathrm{X}}_2$(14)

As shown in Table 4, the R² value for the properties is above 80 except for that of SC. However, all the Bf and Af values were close to unity, while those of AAD were close to zero, which shows that there was an agreement between the experimental and calculated values as shown in Table 5. The linear factors of X₁ and X₂ had significant effects on all the functional properties, while the interaction factor, X₁X₂, had a significant effect on WAC and pH only. However, the interaction effect, X₁X₂, of maize and PTS flour resulted in an increase in the WAC and a decrease in the pH.

Table 4. Coefficient of regression for models for functional properties of maize–PTS composite flours

Coefficient	WAC	SC	TTA	pH
β1	190.57*	8.637*	0.05986*	6.6034*
β2	−350*	19.19*	−0.274*	6.191*
β12	480*	−13.64	0.357	−1.321*
R^2 (%)	95.78	46.64	80.73	98.95
AAD	0.009	0.008	0.008	0.014
Bf	1	1	1	1
Af	1.1	1.04	1.02	1

*Significant at p ≤ 0.05.

β₁ = linear effects of maize flour, β₂ = linear effects of PTS flour, β₁₂ = interaction effects of maize flour and PTS flour, R² = coefficient of determination, AAD = average absolute deviation, Bf = bias factor, Af = accuracy factor, WAC= water absorption capacity, SC = swelling capacity, titratable acidity = TTA.

Table 5. The experimental and predicted values for functional properties of maize–PTS composite flours

Maize flour (X1)	PTS flour (X2)	WAC		SC		TTA		pH
		Exp.	Pre.	Exp.	Pre.	Exp.	Pre.	Exp.	Pre.
0.8	0.2	152.5	157.73	8.75	8.57	0.06	0.05	6.33	6.31
0.9	0.1	168.84	176.65	9.32	8.47	0.06	0.06	6.45	6.44
0.7	0.3	142.17	128.55	8.64	8.93	0.03	0.04	6.20	6.20
0.8	0.2	151.36	157.73	8.55	8.57	0.06	0.05	6.29	6.31
0.6	0.4	83.51	89.11	9.80	9.59	0.02	0.01	6.14	6.12
1.0	0.0	191.67	185.31	8.95	8.64	0.07	0.06	6.60	6.60
0.6	0.4	82.87	89.11	9.80	9.59	0.01	0.01	6.12	6.12
0.7	0.3	141.67	128.55	8.32	8.94	0.02	0.03	6.17	6.20
0.9	0.1	172.67	176.65	8.30	8.47	0.05	0.06	6.47	6.44
1.0	0.0	186.71	185.31	7.95	8.64	0.05	0.06	6.59	6.60

WAC= water absorption capacity, SC = swelling capacity, titratable acidity = TTA.

3.3. Sensory properties of meal produced from maize–PTS composite flours

The sensory properties of meal produced from maize–PTS composite flours, i.e. colour (Y₁₀), taste (Y₁₁), aroma (Y₁₂), texture (Y₁₃) and overall acceptability (Y₁₄), are represented by the models in Equation (15)(15) ${\mathrm{Y}}_{10}=8.317{\mathrm{X}}_1+11.48{\mathrm{X}}_2-8.57{\mathrm{X}}_1{\mathrm{X}}_2$(15) –(19(19) ${\mathrm{Y}}_{14}=8.95{\mathrm{X}}_1-0.8{\mathrm{X}}_2-2.6{\mathrm{X}}_1{\mathrm{X}}_2$(19) ).

(15) ${\mathrm{Y}}_{10}=8.317{\mathrm{X}}_1+11.48{\mathrm{X}}_2-8.57{\mathrm{X}}_1{\mathrm{X}}_2$(15)

(16) ${\mathrm{Y}}_{11}=8.01{\mathrm{X}}_1-7.48{\mathrm{X}}_2+12.71{\mathrm{X}}_1{\mathrm{X}}_2$(16)

(17) ${\mathrm{Y}}_{12}=6.326{\mathrm{X}}_1-23.4{\mathrm{X}}_2+33.5{\mathrm{X}}_1{\mathrm{X}}_2$(17)

(18) ${\mathrm{Y}}_{13}=7.897{\mathrm{X}}_1+5.35{\mathrm{X}}_2-2.36{\mathrm{X}}_1{\mathrm{X}}_2$(18)

(19) ${\mathrm{Y}}_{14}=8.95{\mathrm{X}}_1-0.8{\mathrm{X}}_2-2.6{\mathrm{X}}_1{\mathrm{X}}_2$(19)

The R² values for the properties were above 80 except for that of colour, but all the Bf and Af values were close to unity while AAD values were close to zero (Table 6). This is an indication that there was an agreement between the experimental and calculated values (Table 7).

Table 6. Coefficient of regression for models for sensory properties from maize–PTS meals

Coefficient	Colour	Taste	Aroma	Texture	Overall acceptability
β1	8.317*	8.010*	6.326*	7.897*	8.951*
β2	11.48*	−7.48*	−23.4*	5.35*	−0.8*
β12	−8.57	12.71	33.5	−2.36	−2.6
R^2 (%)	54.78	91.44	80.11	85.65	80.74
AAD	0.03	0.04	0	0.03	0.08
Bf	1	1	1.01	1	1
Af	1.03	1.04	1.12	1.03	1.1

*Significant at p ≤ 0.05.

β₁ = linear effects of maize flour, β₂ = linear effects of PTS flour, β₁₂ = interaction effects of maize flour and PTS flour, R² = coefficient of determination, AAD = average absolute deviation, Bf = bias factor, Af = accuracy factor.

Table 7. The experimental and predicted values for sensory properties of maize–PTS meals

Maize flour (X1)	PTS flour (X2)	Colour		Taste		Aroma		Texture		Overall acceptability
		Exp.	Pre.	Exp.	Pre.	Exp.	Pre.	Exp.	Pre.	Exp.	Pre.
0.8	0.2	7.41	7.58	6.5	6.95	5.39	5.74	6.87	7.01	6.88	6.59
0.9	0.1	7.82	7.86	7.27	7.61	5.96	6.37	7.4	7.43	9.08	7.74
0.7	0.3	7.65	7.47	6.43	6.03	4.71	4.44	6.34	6.64	5.24	5.48
0.8	0.2	7.73	7.58	7.3	6.95	7.51	5.74	7.03	7.01	6.37	6.59
0.6	0.4	8.1	7.53	4.61	4.86	2.35	2.48	6.62	6.31	4.74	4.43
1.0	0.0	8.33	8.32	8.6	8.01	6.60	6.33	8.11	7.90	10.07	8.95
0.6	0.4	6.9	7.53	4.93	4.86	2.87	2.48	6.05	6.31	4.43	4.43
0.7	0.3	7.39	7.47	6.05	6.03	3.29	4.44	6.89	6.64	5.08	5.48
0.9	0.1	7.82	7.86	7.65	7.61	5.85	6.37	7.67	7.43	6.94	7.74
1.0	0.0	8.35	8.32	7.58	8.01	6.29	6.33	7.60	7.90	7.55	8.95

The linear factors of X₁ and X₂ had significant effects on all the sensory properties, while the interaction factor, X₁X₂, had no significant effect on the properties.

4. Discussion

An increase in PTS flour may lead to a greater increase in crude protein and crude fibre contents than when the quantity of maize flour is increased. Conversely, an increase in maize flour may lead to a greater increase in DPPH and TFC than when PTS flour is increased. The increase in the crude protein and crude fibre contents of the composite flour could be due to the inclusion of PTS flour as several species of mushroom are edible and serve as a good source of nutrients, especially protein and dietary fibre (Thakur, 2020). This result is similar to the increase in protein and fibre contents of cookies reported for orange-fleshed sweet potato supplemented with 30% sclerotium of Pleurotus tuber-regium by Kolawole et al. (2020). The lower fibre content of maize flour compared to that of PTS flour could be a result of decortication of the maize grains used in the formulation (Oghbaei & Prakash, 2013). Since the interaction effect of the components resulted in an increase in TPC and DPPH, it follows that both maize and PTS flours may be rich in the antioxidants as it was previously reported (Lu et al., 2018; Van Hung, 2016).

An increase in maize in the formulation may lead to an increase in the WAC, SC and pH, while an increase in PTS flour in the formulation may result in an increase in the SC and pH but a decrease in WAC and TTA. Starch contributes significantly to water absorption and swelling capacity in any food system (Awuchi et al., 2019). Maize flour, being a starchy food product, may be responsible for the increase in WAC and SC in the formulation. The decrease in the WAC with an increase in PTS flour in the composite flour could be a result of possible molecular interactions between dietary fibre (glycans) in PTS and starch (Zhao et al., 2022). This is in agreement with a decrease in WAC reported for orange-fleshed sweet potato flour as a result of supplementation with sclerotium of Pleurotus tuber-regium flour (Kolawole et al., 2018). The decrease in TTA with the corresponding increase in the pH of the composite flour could be a result of the addition of PTS flour. The same trend was noted in complementary porridges containing Agaricus bisporus (a species of mushroom) (Ishara et al., 2018). Furthermore, Salah et al. (2022) reported a gradual increase in pH as the amount of mushroom powder increased in a low-fat yoghurt supplemented with dried mushroom powder. Therefore, foods from maize–PTS composite flour in this study may be ideal for the consumers who cannot tolerate high acid foods or who are living with ulcer.

With an increase in maize flour, there may be improvement in terms of taste, texture and overall acceptability. However, an increase in PTS flour in the formulation may result in an increase in colour acceptability but a reduction in taste, aroma and overall acceptability. A similar trend of decrease in sensory properties was reported for bread enriched with mushroom powder (Okafor et al., 2011) and cereal flours enriched with mushroom flour (Siyame et al., 2021). The sensory properties of the meal from the composite flour suggested that a suitable functionality-enhancing agent such as cassava flour could be incorporated into the composite flour for better sensory properties of the meal.

5. Conclusion

This study modelled the nutritional, antioxidant and functional properties of maize–PTS composite flours and sensory properties of meal produced from them using SCD. The mathematical models generated adequately described the parameters and could be utilized for optimizing meal production. The inclusion of PTS flour into maize flour improved most of the properties of the composite flours. The results of sensory evaluation suggest that a suitable functionality-enhancing agent such as cassava starch could be incorporated into the composite flour for better sensory properties of the meal. PTS flour could, therefore, be incorporated into maize flour for the production of maize-based meal for improved nutritional quality and antioxidant property.

Acknowledgement

The authors acknowledge the assistance rendered by the technical staff of the Department of Food Science, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.

Disclosure statement

The authors declare that they have no conflict of interest.

Additional information

Notes on contributors

Bolanle Aishat Akinwande

Bolanle Aishat Akinwande is a faculty member in the Faculty of Food and Consumer Sciences, Department of Food Science, Ladoke Akintola University of Technology, Ogbomoso, Nigeria. She is a Professor of Food Science. Her passion is on healthy foods for rural poor. She uses simple technologies that are affordable for smallholder farmers for food processing. As an academic, she fulfils service components of academics by training farm families on the utilization options for crops for health and wealth initiatives. Her research activities revolve around the enhancement of the use of locally produced functional foods of plant and animal origins, such as mushroom, soybean, ginger, turmeric, traditional cheese and fish. The present research centres on the utilization of an edible mushroom for improving the nutritional quality of meal from yellow maize.

James Oyedokun

James Oyedokun is a lecturer and researcher in the Department of Food Science and Technology, Osun State University, Osogbo, Nigeria. He specializes in Food Microbiology and Food Product Development.

Abiola Esther Olajide

Abiola Esther Olajide and Olajumoke Esther Afolabi are graduates of Food Science and Engineering from Ladoke Akintola University of Technology, Ogbomoso, Nigeria. Their research area is the enhancement of the use of locally produced functional foods of plant and animal origins, such as mushroom, soybean, ginger, turmeric, traditional cheese and fish.