INTRODUCTION
Conventional yogurt is a product made by
fermentation of milk. Bacteria ferment milk sugars
and produce acid which can act on milk protein
and produce textured yogurt [1]. Nowadays, many
people have lactose intolerance and are allergic to dairy
products. For them, plant-based yogurt is an alternative.
In this work, we used oats, peanut, or coconut milk
as an alternative to cow milk. These ingredients increase
the nutritive value of yogurt and provides it with an
honest flavor. Fermentation of plant materials using
mixed cultures was found to be mutually beneficial for
the human body. Mutualism was found to exist between
proteolytic Lactobacillus bulgaricus and non-proteolytic
Streptococcus thermophilus as the former releases free
amino acids and peptides as a nitrogen source, while the
latter supplies growth factors such as pyruvic acid, folic
acid, formic acid, and carbon dioxide [2]. L. bulgaricus
and S. thermophilus are used in yogurt as starter
cultures [3].
The fermentation time determines the acidity
level of yogurt. Longer fermentation produces highly
acidic yogurt [4]. A low sugar content in plant milk
embarrasses acid production by carboxylic acid bacteria,
which requires sucrose addition. Stabilizers and
gelling agents are used to improve yogurt texture and
creaminess, mostly pectin, starch, gelatin, and gums.
They turn into a gel when heated in the presence of
liquid. They are widely utilized in jams and jellies [5].
Proper heat treatment of plant-based milk is
important before fermentation for starch to gelatinize.
It increases the viscosity of yogurt and helps prevent
phase separation. In addition, it decreases the quantity of
endogenous microbes before starter inoculation [1].
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Coconut milk has a milky white color. Its nutritional
content includes fat, ash, water, carbohydrate, protein,
and their derivatives. The effectiveness of extraction
and the composition of coconut milk rely on the
processing parameters such as temperature of added
water and pressing conditions. The fat content also
plays an important role in the flow properties of milk [6].
According to the National Centre for Biotechnology
Information, lauric acid has antifungal and antiviral
properties which fight against many human diseases.
Lauric acid also reduces cholesterol and triglycerides,
which is helpful in treating cardiovascular diseases [7].
Oat grains are a rich source of beta-glucan, a dietary
soluble fiber. They have a 5–9% lipid content and are
rich in polyunsaturated fatty acids, including linoleic
acid, an essential fatty acid. In addition, oats contain
avenanthramide, an antioxidant, as well as tocotrienols
and tocopherols, vitamin E-like compounds. Oats have
cardiovascular benefits due to their cholesterol-reducing
properties. They have a high content of starch (60%),
protein (11–15%), and lipids (5–9%). Their essential
amino acids include oleic acid (45.60 g/kg), linoleic acid
(36.2–40.4%), and linolenic acid (38.4–41.6%). Thus,
oats milk plays a key role in competing with numerous
substitutes of dairy milk in the continuously expanding
market of dairy and non-dairy products [8].
Peanut milk and its products have high dietary
benefits for all age groups due to a high content
of protein, essential fatty acids (linoleic and oleic
acids), and minerals. Peanut milk also contains
hexanal, an important component responsible for its
undesirable beany flavor which is entirely eliminated
by fermentation or cooking. The stability of peanut
milk and its products is highly enhanced by heating
at 66–87°C for about 15–20 min and homogenization.
This increase in stability is caused by the solubility of
proteins [9].
We aimed to formulate a plant-based yogurt,
optimize its ingredients and process conditions, as
well as analyze its physicochemical, rheological, and
nutritive qualities.
STUDY OBJECTS AND METHODS
Raw peanut, coconut, and oats were procured
from the local market in Erode, India. We used the
Vegan Greek Yogurt Starter Culture (Alla’s Posh Flavors,
Uttar Pradesh, India) that contained live cultures
of Lactobacillus bulgaricus and Streptococcus
thermophilus stored in a freezer at –18°C. Xanthan gum,
pectin, and corn starch were of the Urban Platter brands.
Peanut milk was prepared by immersing raw peanuts
in potable water for about 8–10 h at room temperature.
Then, the soaked peanuts were blended using a food
processor with an adequate amount of water and filtered
with cheesecloth/muslin or a strainer. The supernatant was
collected.
Coconut milk was prepared from fresh and matured
coconut endosperm which was cut into pieces and blended
using a food processor with an adequate amount of water
and filtered with cheesecloth/muslin or a strainer. The
supernatant was collected.
Oats milk was prepared by soaking freshly bought
oats in water for about 30 min until they absorbed enough
moisture for milk extraction. Then, they were blended and
filtered using cheesecloth/muslin. The milk was extracted
by ensuring enough beta-glucan was present in the
supernatant.
To prepare a cow milk yogurt alternative, the milk
samples were pasteurized at an optimum temperature
of 72°C for 20 min by a double boiling method to avoid
gelatinization. This method uses the steam from the
simmering water to warm the milk in the bowl gently
with indirect heat. Then, the milk was cooled to 45°C. The
starter cultures (L. bulgaricus and S. thermophilus)
were added as 0.4% of the milk mixture weight. After
inoculation, 10% of sucrose was added to the milk
mixture to optimize the growth of lactic acid bacteria.
To strengthen the gel network of the yogurt, we added
corn starch (5%) at above 60°C, xanthan gum (0.15%) at
above 70°C under continuous stirring, and pectin (0.75%)
at above 25°C. The milk was incubated at 41°C for 18 h
to maintain the humidity and temperature in favorable
conditions for the growth of microorganisms. The formed
yogurt was cooled to a room temperature of 27°C and
stored in a refrigerator at 4°C for 1 h.
The physiochemical properties of the yogurt were
analyzed using the AOAC method, 1995. They included
pH, titratable acidity, moisture content, total solids, fat, ash,
protein, and carbohydrates.
Viscosity was measured using a Brookfield DV-III
Ultra rheometer, with a CPE 40 spindle. The samples
were measured at different RPM at different shear rates.
Particularly, we measured the shear rate, shear stress,
viscosity, and torque. The shear rate was kept constant for
all the trials to measure changes in viscosity.
The flow behavior was determined by plotting the
shear rate versus viscosity and the n-value was determined
from the power equation. The power equation was
generated by a power line in a trendline model graph. The
n-value was determined from the negative power value in
the equation. The n-value was estimated to be less than 1 to
determine the flow behavior of the yogurt [10].
The centrifugal acceleration test was performed
to determine the syneresis rate of the yogurt. In a test
tube, 5 g of a yogurt sample was placed and centrifuged at
1.200×g for 0, 3, 6, 9, 12, and 15 min at room temperature.
To estimate the initial syneresis rate, the volume of the
serum separated from the samples was measured at
each time interval, which was expressed as milliliters of
serum released per gram of sample per unit of time. To
evaluate the syneresis rate for that day, the average of 5 tests
(except 0) was calculated [11].
The cups containing 100 mL of a yogurt sample at
10°C were provided for sensory analysis. Each sample
was assessed in three repetitions for flavor, texture,
appearance, color, and overall acceptability on a nine-point
hedonic scale, where 1 = the least/lowest; 9 = the most/
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highest. The panelists were trained about the sensory
attributes before the sensory analysis.
Design Expert software (version 13.0) was used to
optimize the development of a plant-based yogurt. The
response surface methodology (RSM) explored the
relationship between explanatory variables and one or
more response variables. The mixture simplex-lattice
design was used to find the optimum combination of
constituents in the range between 5 and 10. The values
of sugars and stabilizers were taken as constant. Time
and temperature of incubation were also taken as
constant for improved product quality. The mixture
consisted of peanut milk, coconut milk, and oats milk in
14 combinations (Table 1).
Statistical and data analysis. To represent the fitted
response value, the linear, special cubic, and special
quartic models (Equations (1) – (3)) were used. To make
predictions about the response for given levels of each
factor, the equations could be used in terms of coded
factors. The statistical significance of each equation was
determined by variance analysis (ANOVA).
Y = b1X1+b2X2+b3X3 (1)
Y = b1X1 + b2X2 + b3X3 + b1b2X1X2 + b1b3X1X3 +
+ b2b3X2X3 + b1b2b3X1X2X3 (2)
Y = b0 + b1X1 + b2X2 + b3X3 + b12X1X2 + b13X1X3 +
+ b23X2X3 + b1123X1^2X2X3 + b1223X1X2^2X3+ (3)
+ b1233X1X2X3^2+e
where Y is the predictive dependent variable (sensory
analysis, viscosity, flow behavior, syneresis rate); b is
the equation coefficient; X is the proportion of pseudocomponents
[12].
For Simple Quartic, X1, X2 and X3 are the proportions
of each number grade; b0 is the constant, b1, b2 and b3 are
the coefficients of linear terms; b12, b13 and b23 are the
coefficients of two-term interactions; b1123, b1223 and b1233
are the coefficients of special three-term interactions.
RESULTS AND DISCUSSION
Fitting for the best model. Table 2 shows the
results of mixture design studies. The independent and
dependent variables were fitted to linear, cubic, and
special quartic models and the residuals plots were
formulated to check the goodness of model fit. Low
standard deviation, low predicted sum of squares, and
high predicted R-squared were the parameters for the
best model [13]. The linear model was found to be best
fitted for sensory analysis and viscosity. The special
cubic model was best fitted for the response flow
behavior. The special quartic model was found to be best
fitted for syneresis.
The linear and the quadratic models were used
to relate the response to the operating factors of the
experiment design. The fit of the polynomial models was
analyzed using the coefficient of determination R² and
the adjusted R², with statistical significance tested by
the F-test. A large value specified that variations in the
response could be revealed by the regression equation.
To point out the statistical significance, the desired
larger F-value was tested by the P-value. The model
that showed a confidence interval greater than 95%
(prob > [t] < 0.05) by the probability test was regarded
as statistically significant. The Prob > F-value for the
linear model was less than 0.0032 R² and the adjusted
R² was found to have a maximum of 0.6474 and 0.5833,
respectively. Although the cubic model was found to
be aliased, the linear model was selected for further
analysis of viscosity.
Table 1 Yogurt formulations based on oats milk, peanut milk,
and coconut milk in a three-component mixture constrained
simplex-lattice design
Run Ingredients, mL
X1 (oats milk) X2 (peanut milk) X3 (coconut milk)
1 10.000 5.000 10.000
2 7.500 7.500 10.000
3 5.000 10.000 10.000
4 10.000 10.000 5.000
5 7.500 10.000 7.500
6 9.167 6.667 9.167
7 9.167 9.167 6.667
8 7.500 7.500 10.000
9 10.000 10.000 5.000
10 5.000 10.000 10.000
11 6.667 9.167 9.167
12 8.333 8.333 8.333
13 10.000 5.000 10.000
14 10.000 7.500 7.500
Table 2 Experimental design for viscosity, sensory analysis,
flow behavior, and syneresis rate for each plant-based yogurt
formulation
Run Response 1
Viscosity, P
Response 2
Sensory
analysis
Response 3
Flow behavior
(n-value)
Response 4
Syneresis
rate,
mL/min
1 49.050 8.0 0.139 0.0324
2 53.810 7.0 0.058 0.0210
3 49.467 6.5 0.134 0.0314
4 56.960 6.0 0.034 0.0125
5 55.685 6.0 0.020 0.0128
6 49.051 8.0 0.140 0.0335
7 55.680 6.5 0.020 0.0170
8 53.760 7.0 0.068 0.0220
9 56.961 6.0 0.034 0.0160
10 49.460 6.0 0.133 0.0356
11 49.000 6.5 0.134 0.0354
12 53.960 6.6 0.078 0.0240
13 49.010 8.0 0.130 0.0312
14 55.980 7.0 0.060 0.0110
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Table 3 ANOVA for the linear model of plant-based yogurt
viscosity
Source Sum
of squares
df Mean
square
F-value P-value
Model 93.03 2 46.51 10.10 0.0032
Linear
mixture
93.03 2 46.51 10.10 0.0032
Residual 50.66 11 4.61
Lack of fit 50.66 7 7.24 13950.78 < 0.0001
Pure error 0.0021 4 0.0005
Cor total 143.69 13
Figure 1 3D surface graph (a) and diagnostic plots (b) of the effect of independent variables on viscosity of the plant-based yogurt
(A: Oats milk, B: Peanut milk, C: Coconut milk)
The ANOVA results for the model fitted for viscosity
are shown in Table 3. As we can see, the linear effects
of coconut (X3), oats (X1), and peanut (X2) milk on
the yogurt viscosity were found to be significant.
Considering the significant factors, equation (4)
represents the model developed for viscosity.
Viscosity = 65.25X1 + 65.64X2 + 28.72X3 (4)
The interaction effect of the process parameters
was studied using response surface plots, which helped
predict the optimal levels of each parameter to achieve
maximum viscosity. Figure 1a shows the influence of
three parameters on viscosity. According to Table 2,
runs 5, 9, and 14 show greater viscosity. This means that
viscosity increased with an increase in peanut and oats
milk, but decreased with an increase in coconut milk.
The optimum region was determined by setting
the maximum viscosity as the goal. In a study by
Ye et al., the increase in viscosity was due to a higher
protein content in peanut and oats milk [14]. Brückner-
Gühmann et al. suggested that due to a high content
of protein, oats could be used as a plant-based gelling
agent even at temperatures below the temperature of
denaturation [15]. The addition of pectin and xanthan
gum also influenced the viscosity range. Figure 1b
shows that viscosity ranged from 48.000 to 58.000 P.
Our results showed that peanut and oats milk, as well
as stabilizers, had a greater effect on the viscosity of
the plant-based yogurt than other components, such as
coconut milk or sucrose.
The cubic model was used to relate the response to
the operating factors of the experiment design. The
fit of the polynomial models was analyzed using the
coefficient of determination R², the adjusted R², with
statistical significance tested by the F-test. A large value
specified that response variations could be revealed
by the regression equation. To point out the statistical
significance, the desired larger F-value was tested by the
P-value. The model that showed a confidence interval
greater than 95% (prob > [t] < 0.05) by the probability
test was regarded as statistically significant. The
Prob > F-value for the special cubic model was less
than 0.0135 R² and the adjusted R² was found to have a
maximum of 0.8467 and 0.7154, respectively. The cubic
model was selected for further analysis of flow behavior.
The ANOVA results for the model fitted for the
flow behavior are shown in Table 4. As can be seen, the
cubic effects of coconut (X3), oats (X1), and peanut (X2)
milk on the flow behavior were found to be significant.
Considering the significant factors, equation (5)
represents the model developed for the flow behavior.
Flow behavior = – 19.43355X1 – 20.93901X2 –
– 20.43434X3 + 85.28998X4 + 85.42010X5 + (5)
+ 89.18653X6 – 229.73535X7
where X4 = oats milk + peanut milk, X5 = oats milk +
+ coconut milk, X6 = peanut milk + coconut milk,
X7 = oats milk + peanut milk + coconut milk.
The interaction effect of the process parameters
was studied using response surface plots, which helped
to predict the optimal levels of each parameter for
achieving maximum flow behavior. Figure 2a shows
the influence of three parameters on the flow behavior.
The flow behavior depends on viscosity and the shear
rate. This was determined by the power law model. The
power law does not consider yield stress since it is a
58
56
54
52
50
48
48 50 52 54 56 58
Predicted
Actual
Predicted vs. Actual
a b Viscosity, P
C (5.000)
B (10.000)
C (10.000)
A (10.000)
B (5.000)
A (5.000)
58
56
54
52
50
48
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non-Newtonian fluid model. The relationship between
viscosity and the shear rate in the power law model is
defined as η = mγn–1, where η is apparent viscosity,
γ is the shear rate, and m and n are the power law
constants [16].
Figure 2b shows that the n-value of the flow behavior
ranged from 0 to 0.14. Yogurt is a thixotropic fluid with
n < 1, where n is the flow behavior index (dimensionless)
indicating the non-Newtonian or Newtonian character.
According to Ghica et al., n < 1 determines a non-
Newtonian pseudo plastic fluid, n > 1 determines a
non-Newtonian dilatant fluid, and n = 1 determines a
Newtonian fluid [10]. Table 2 shows changes in the flow
behavior with respect to viscosity and the composition
of milk. The lesser the viscosity, the greater the flow
behavior. This was due to the influence of stabilizers and
the composition of milk.
The linear model was used to relate the response
to the operating factors of the experiment design. The
fit of the polynomial models was analyzed using the
coefficient of determination R² and the adjusted R²,
with statistical significance tested by the F-test. A large
value specified that variations in the response could be
revealed by the regression equation. To point out the
statistical significance, the desired larger F-value was
tested by the P-value. The model with a confidence
interval greater than 95% (prob > [t] < 0.05) by the
probability test was regarded as statistically significant.
The Prob > F-value for the linear model was less than
0.0001 R² and the adjusted R² was found to have a
maximum of 0.9222 and 0.9080, respectively. Although
the cubic model was found to be aliased, the linear
model was selected for further analysis of sensory
evaluation.
The ANOVA results for the model fitted for sensory
evaluation are shown in Table 5. As we can see, the
linear effects of coconut (X3), oats (X1), and peanut (X2)
milk were found to be significant on sensory attributes.
Considering the significant factors, equation (6)
represents the model developed for sensory evaluation.
Sensory evaluation = 9.64X1 +0.14X2 + 10.50X3 (6)
The interaction effect of the process parameters
was studied using response surface plots, which helped
predict the optimal levels of each parameter to achieve
maximum sensory values. Figure 3a shows the influence
of three parameters on sensory evaluation. We found
a decrease in sensory values with higher contents of
peanut milk. However, higher contents of coconut and
oats milk provided maximum sensory values. This was
due to the nutty flavor of peanut producing off-flavor.
A (5.000) В (10.000)
C (10.000)
B (5.000)
A (10.000)
C (5.000)
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0
Flow behavior (n-value)
A (5.000) В (10.000)
C (10.000)
B (5.000)
A (10.000)
C (5.000)
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0
0 0.02 0.04 0.06 0.08 0.10 0.12 0.14
Predicted
Actual
Predicted vs. Actual
a b
Figure 2 3D surface graph (a) and diagnostic plots (b) of the effect of independent variables on flow behavior
of the plant-based yogurt (A: Oats milk, B: Peanut milk, C: Coconut milk)
Table 4 ANOVA for the special cubic model of yogurt flow behavior
Source Sum of squares df Mean square F-value P-value
Model 0.0257 6 0.0043 6.45 0.0135
Linear mixture 0.0170 2 0.0085 12.81 0.0046
X4 (oats milk + peanut milk) 0.0059 1 0.0059 8.81 0.0208
X5 (oats milk + coconut milk) 0.0035 1 0.0035 5.24 0.0558
X6 (peanut milk + coconut milk) 0.0006 1 0.0006 0.9169 0.3702
X7 (oats milk + peanut milk + coconut milk) 0.0037 1 0.0037 5.50 0.0514
Residual 0.0047 7 0.0007 – –
Lack of fit 0.0046 3 0.0015 66.81 0.0007
Pure error 0.0001 4 0.0000 – –
Cor total 0.0304 13 – – –
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Ye et al. noted that the application of flavoring
agents improved the sensory and overall acceptability
of peanut milk-based yogurt [14]. This confirmed earlier
reports that adding flavoring agents and fruits to yogurt
increased the product range, as well as consumers’
liking of the product [17].
Figure 3b shows that the overall acceptance ranged
from 6 to 8. According to Table 2, runs 1, 6, and 13
showed higher sensory values in the formulations with
a lower quantity of peanut milk compared to oats and
coconut milk. Therefore, the flavor problem in peanut
milk yogurt could be corrected or improved by applying
commercial flavoring agents.
The simple cubic and quadratic models were used
to relate the response to the operating factors of the
experiment design. The fit of the polynomial models
was analyzed using the coefficient of determination R²
and the adjusted R², with statistical significance tested
by the F-test. A large value specified that variations
in the response could be revealed by the regression
equation. The P-value was used to test whether F-value
was large enough to point out statistical significance.
The model with a confidence interval greater than 95%
(prob > [t] < 0.05) by the probability test was regarded
as statistically significant. The Prob > F-value for the
special quartic model was less than 0.0300 R² and the
adjusted R² was found to have a maximum of 0.9083
and 0.7616, respectively. Although the cubic model
was found to be aliased, the special quartic model was
selected for further analysis of syneresis.
The ANOVA results for the model fitted for syneresis
are shown in Table 6. As can be seen, the quartic effects
of coconut (X3), oats (X1), and peanut (X2) milk were
found to be significant on syneresis. Considering the
significant factors, equation (7) represents the model
developed for syneresis.
Syneresis = 0.0338X1 + 0.0321X2 +
+ 0.0146X3 – 0.0433X4 – 0.0403X5 – 0.0442X6 +
+ 0.8024X7 + 0.7247X8 – 0.1370X9 (7)
where X4 = oats milk + peanut milk, X5 = oats milk
+ coconut milk, X6 = peanut milk + coconut milk,
X7 = oats milk2 + peanut milk + coconut milk, X8 = oats
milk + peanut milk2 + coconut milk, X9 = oats milk +
peanut milk + coconut milk2.
The interaction effects of the process parameters
were studied using response surface plots, which
helped predict the optimal levels of each parameter
to achieve minimum syneresis rate values. Figure 4a
shows the influence of three parameters on syneresis.
We found that syneresis was minimum when viscosity
was maximum, i.e., syneresis decreased as viscosity
increased. Figure 4b represents the syneresis values
ranging from 0.01 to 0.04.
According to Table 2, runs 4, 5, 9, and 14 had
minimum syneresis with maximum viscosity values.
This was due to the binding of molecules in higher
viscosity that holds the water during syneresis. In a
study by Dönmez et al., the interaction with casein
micelles in conventional yogurt influenced the strength
of the casein network and the stabilized yogurt structure,
increasing the consistency by reducing the syneresis rate
Table 5 ANOVA for the linear model of yogurt sensory
evaluation
Source Sum of
squares
df Mean
square
F-value P-value
Model 6.74 2 3.37 65.17 < 0.0001
Linear
mixture
6.74 2 3.37 65.17 < 0.0001
Residual 0.5689 11 0.0517 – –
Lack of fit 0.4439 7 0.0634 2.03 0.2577
Pure error 0.1250 4 0.0313 – –
Cor total 7.31 13 – – –
Table 6 ANOVA for the special quartic model of syneresis
Source Sum of squares df Mean square F-value P-value
Model 0.0010 8 0.0001 6.19 0.0300
Linear mixture 0.0006 2 0.0003 15.35 0.0073
X4 (oats milk + peanut milk) 0.0002 1 0.0002 7.88 0.0377
X5 (oats milk + coconut milk) 0.0001 1 0.0001 4.12 0.0982
X6 (peanut milk + coconut milk) 0.0001 1 0.0001 4.97 0.0763
X7 (oats milk2 + peanut milk + coconut
milk)
0.0001 1 0.0001 3.32 0.1281
X8 (oats milk + peanut milk2 +
coconut milk)
0.0001 1 0.0001 2.71 0.1608
X9 (oats milk + peanut milk + coconut
milk2)
1.869E-06 1 1.869E-06 0.0940 0.7715
Residual 0.0001 5 0.0000 – –
Lack of fit 0.0001 1 0.0001 20.59 0.0105
Pure error 0.0001 4 4.041E-06 – –
Cor total 0.0011 13 – – –
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at certain concentrations [18]. The syneresis value also
depends on the composition of the stabilizers used.
Optimization of component proportion. We
solved the equations to yield the average values of each
independent variable in order to obtain the optimal
yogurt. This allowed us to find a desirable combination
of oats, coconut, and peanut milk (Table 7). Then, we
analyzed the optimized yogurt for viscosity, sensory
evaluation, flow behavior, and syneresis rate. The
optimized yogurt consisted of 7.134 mL of peanut milk,
10 mL of oats milk, and 7.866 mL of coconut milk. Its
predicted values of syneresis, viscosity, flow behavior,
and sensory evaluation were 0.0138081, 53.4733,
0.0648189, and 7.20565, respectively, with a desirability
value of 0.717.
Physiochemical analysis of raw milk and
optimized yogurt. The optimized plant-based yogurt
and raw milk were exposed to nutritional analysis
to compare the predicted and actual values (Table 8).
This ensured adequate nutritional values in the
developed yogurt.
CONCLUSION
Our results showed the effectiveness of the mixture
simplex-lattice design approach for optimizing yogurt
based on plant milk. According to our experimental
results and counter plots, an increase in peanut and
oats milk improved the viscosity and reduced the
flow behavior and syneresis rate. The samples with
higher contents of peanut milk received low sensory
values. This indicates that peanut milk has to be used
in minimum amounts with stabilizers, such as corn
starch, pectin, and xanthan gum, to ensure optimum
texture properties. The plant-based yogurt with an
8.5
8.0
7.5
7.0
6.5
6
6.0 6.5 7.0 7.5 8.0 8.5
Predicted
Actual
Predicted vs. Actual
a b
Sensory analysis
B (10.000)
C (10.000) A (5.000)
C (5.000)
A (10.000)
8.5
8.0
7.5
7.0
6.5
6.0
B (5.000)
Figure 3 3D surface graph (a) and diagnostic plots (b) of the effect of independent variables on sensory evaluation of the plantbased
yogurt (A: Oats milk, B: Peanut milk, C: Coconut milk)
Figure 4 3D surface graph (a) and diagnostic plots (b) of the effect of independent variables on syneresis rate
of the plant-based yogurt (A: Oats milk, B: Peanut milk, C: Coconut milk)
0.040
0.035
0.030
0.025
0.020
0.015
0.010 0.015 0.020 0.025 0.030 0.035
Predicted
Actual
Predicted vs. Actual
a b
Syneresis, mL/min
B (10.000)
C (10.000)
A (5.000) C (5.000)
A (10.000)
0.030
0.025
0.020
0.015
0.010
0.040
B (5.000)
0.035
0.010
0.040
Table 7 Optimum ingredient proportions for plant-based
yogurt
Components Percentage
Oats milk 40
Coconut milk 31.5
Peanut milk 28.5
281
Baskar N. et al. Foods and Raw Materials. 2022;10(2):274–282
optimized composition was found to have high sensorial
acceptance. The physiochemical analysis of raw milk
and the optimized yogurt showed adequate amounts of
nutrients.
CONTRIBUTION
The authors were equally involved in the writing
of the manuscript and are equally responsible for any
potential plagiarism.
Table 8 Physiochemical analysis of raw milk and optimized plant-based yogurt
Responses Predicted value for
optimized yogurt
Actual value for
optimized yogurt
Raw materials
Oats milk Peanut milk Coconut milk
Sensory analysis 7.20 7.30 – – –
Viscosity, P 53.47 53.76 43.31 40.69 30.72
Syneresis rate, mL/min 0.013 0.011 – – –
Flow behavior 0.064 0.068 – – –
Moisture, % – 46 45 42 39
pH – 5.500 6.908 6.512 7.412
Titratable acidity – 3.560 0.966 1.066 1.066
Total solids, % – 11.49 22.90 11.46 12.10
Fat, % – 9.45 6.78 4.40 9.40
Protein, % – 17 16 19 12
Ash, % – 0.490 0.344 0.394 1.240
Carbohydrates, % – 27.00 30.80 32.80 34.40
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ACKNOWLEDGEMENTS
We thank the management of Kongu Engineering
College, Perundurai, Erode, India for their support with
our work.

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