INTRODUCTION
Intestinal microbiota affects human health and
vitality. Microbial community is a powerful and
multifunctional metabolic system that modulates
immunity, suppresses pathogens, and produces various
vitamins [1, 2]. A disturbed qualitative and quantitative
microbial composition leads to various alimentary
and chronic diseases. For instance, low counts
of Bacteroides and Firmicutes, if accompanied by
excessive proteobacteria, fusobacteria, and the mucindecomposing
Ruminococcus gnavus, can trigger Crohn’s
disease, ulcerative colitis, obesity, and diabetes [3].
However, some intestinal microbes inhibit
pathogens and food contaminants by producing
such antimicrobial substances as organic acids
and bacteriocins or competing for nutrients and
adhesion sites [4–7]. If it were not for them, unwanted
microorganisms would cause constant harm to
human health by producing various toxins or
enzymes. For instance, Bacillus cereus is a common
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food contaminant that produces two types of toxins
and causes vomiting and diarrhea intoxication [8].
B. cereus spores are resistant to heat treatment and
chemical preservation [9].
Non-typhoid Salmonella is another wide-spread
cause of foodborne diseases [10]. Salmonella enterica
s. Typhimurium is often resistant to antibiotics and can
develop biofilms, thus causing gastroenteritis, vomiting,
and diarrhea [11]. Antibiotic-resistant bacteria are the
most dangerous causes of intestinal infections [12].
Therefore, novel non-antibiotic ways to suppress
these pathogens and food contaminants for therapy
and prevention are one of the most urgent tasks of the
modern medicine. Synbiotic compositions offer a
potential solution to this problem because they are
extremely effective in inhibiting the growth, activity,
and pathogenesis of specific undesirable microorganisms.
Probiotics, prebiotics, and synbiotics are parts
of functional foods that inhibit unwanted members
of intestinal microbiota [13]. These food additives
are known to increase α-diversity, combat obesity,
improve immunity, and counteract pathogens [13–16].
Synbiotics are the most effective type because they
possess synergistically enhanced beneficial properties of
probiotics and prebiotics [17].
For synbiotics, the most important criteria are their
inhibiting properties, adhesion to intestinal epithelial
cells, and pathogen toxicity. Antagonistic research
of synbiotic combinations is a promising strategy
for developing new synbiotics. Ruiz et al. studied
the combined antimicrobial activity of a synbiotic
based on Bifidobacterium longum subsp. infantis and
galactooligosaccharides against such enteric pathogens
as Escherichia coli, Cronobacter sakazakii, Listeria
monocytogenes, and Clostridium difficile. C. sakazakii
and C. difficile proved to be the most effective
pathogen inhibitors [18]. Co-cultivation of B. longum
or Bifidobacterium breve with C. difficile in a medium
with commercial fructooligosaccharides reduced
the pathogen growth, as well as the toxicity of its
metabolites [19].
Śliżewska and Chlebicz-Wójcik focused on the effect
of various prebiotics co-cultivated with lactobacilli
on pathogenic S. enterica of various serovars and
L. monocytogenes. Inulin demonstrated the greatest
antagonistic activity, although the effect depended on
the test strain [20]. Obviously, the effectiveness of one
and the same composition depends on the pathogen.
The inhibitory effect can be measured by the inhibitory
metabolites produced by probiotics. This effect can
be expressed in terms of inhibition constants (Ki) or
minimal inhibitory concentrations. The synbiotic
factor is another quantitative criterion for evaluating
the effectiveness of synbiotic compositions. It shows
how many times the specific growth rate of a pathogen
or microbial contaminant decreases under the action
of acids produced by a probiotic when they are cocultivated
in the same medium with this prebiotic [21].
Plant extracts are common sources of prebiotic
substances. In addition to polysaccharides of various
molecular weights, they may contain non-carbohydrate
substances with a potential beneficial
effect, e.g., polyphenols [22, 23]. Precipitation with
different concentrations of ethanol can separate plant
carbohydrates into fractions with different degrees of
polymerization. Polysaccharides with a higher degree of
polymerization require a lower concentration of ethanol.
As the alcohol concentration increases, the average
degree of polymerization of the precipitated fraction
decreases [24, 25]. Polysaccharides with a high degree
of polymerization are not metabolized by pathogens
without extracellular hydrolases. However, they can
be metabolized by many types of probiotics, e.g.,
bifidobacteria and some lactobacilli, which determines
their significant prebiotic potential [26]. In our previous
research, we evaluated the effectiveness of a synbiotic
composition in vitro by the degree of its antagonism
against staphylococci. It depended on the fractional
composition of Arctium lappa fructans, as well as on the
ratio of the initial probiotic and pathogen counts [27].
The response surface methodology was developed
by Box and Wilson [28]. It is a powerful tool
for establishing quantitative relationships between
various factors and the response function, also by
taking into account the mutual effect of factors in
multiparameter equations. Shuhaimi et al. used this
method to optimize the composition of a synbiotic that
consisted of Bifidobacterium pseudocatenulatum and
several prebiotics, while Pandey and Mishra tested this
method on a soy drink with lactic acid bacteria and
organophosphates [29, 30].
Few researchers venture beyond simple optimization
to look for the patterns between various factors
and the response function. This approach proved
quite effective in studying the change patterns in
microbial communities under various environmental
factors [31, 32]. Antagonism is a type of relationships in
microbial communities. Our research objective was to
use the response surface method to evaluate the effect
of fructan fractional composition, the initial counts of
probiotics and the pathogen test strain on the antagonism
of the synbiotic against B. cereus and S. enterica.
STUDY OBJECTS AND METHODS
Plant raw materials and obtaining fructan
fractions. To isolate fructans, we used burdock
root powder (Arctium lappa L.) in accordance with
pharmacopeial monograph 2.5.0025.15 of the Russian
Pharmacopoeia. The powder was diluted with distilled
water in a ratio of 1:12 (g dry solids per 1 mL extractant)
and extracted twice at 75°C and pH 6.5 for 30 min with
constant stirring. The pulp was separated by vacuum
filtration. To separate high-molecular impurities, the
extract was ultrafiltered at 45°C through a hollow fiber
module (AR-0.5-20PS, NPO Biotest, Kirishi, Russia)
with a retention threshold of 20 kDa. The permeate
was stirred with active clarifying carbon at a rate of
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15 g/L for 30 min until the extract became colorless.
The activated charcoal was separated by vacuum
filtration [33].
The extract was evaporated using a rotary film
evaporator (model 561-01110-00 with glass set G1,
Heidolph, Germany) at 45°C until the carbohydrate
concentration reached 170–200 g/L. To separate the
carbohydrates into fractions with different degrees
of polymerization, the extract was precipitated with
varying ethanol concentrations (20.0, 32.2, 50.0, 67.8,
and 80.0%) at 4°C for 4 days [24].
The precipitates were separated by centrifugation at
5000 rpm for 15 min and dried in a ScanVac Coolsafe
100-9 freeze-dryer under the following temperature
and time conditions: 0°С – 8 h, 5°С – 8 h, 10°С – 6 h,
15°С – 6 h, and 20°С – 6 h. The samples were diluted
1:1 with a 10% solution of trichloroacetic acid and
hydrolized for 40 min in a boiling water bath. After that,
the content of fructans was determined by the modified
Bertrand method.
Microbial objects and cultivation conditions. All
the bacterial cultures were obtained from the National
Bioresource Center of the All-Russian Collection of
Industrial Microorganisms in the National Research
Center of Kurchatov Institute (VKPM). Bifidobacterium
bifidum (AS-1666, ATCC 29521T) served as a probiotic
culture. Bacillus cereus (B-8076, ATCC 9634) was
used as a model food contaminant. Salmonella enterica
(B-5300) was a model intestinal pathogen. The medium
described in [34] was modified to obtain inoculums and
co-cultivate the probiotic and test strains.
The composition of the carbohydrate-free
medium was as follows (g/L): casein trypton (Difco
Laboratories) – 10; yeast extract (Springer) – 7.6; meat
extract (Panreac) – 5; ascorbic acid (AppliChem) – 1;
sodium acetate – 1; (NH4)2SO4, – 5; urea – 2;
MgSO4·7H2O – 0.2; FeSO4·7H2O – 0.01; MnSO4·7H2O –
0.007; NaCl – 0.01; Tween-80 – 1, and L-cysteine –
0.5 (pH 7.0). All the components were dissolved
in 80% of the required amount of distilled water and
autoclaved at 115°C for 30 min. The fructan precipitates
were dissolved in distilled water (20% of the required
medium volume) and sterilized separately under the
same conditions. Prior to inoculation, carbohydrates
were added to the medium aseptically until their
concentration was 8 g/L.
Inoculums were cultivated at 37°C and stirred at
180 rpm under anaerobic conditions (2% CO2, 98% N2)
in a CB-210 CO2 incubator (Binder, Germany) for 12 h
without maintaining a constant pH. After that, the
inoculums were centrifuged at 6000 rpm and 4°C for
2 min and washed twice in sterile saline (9 g/L NaCl).
Then the precipitate was resuspended in a carbohydratefree
medium to obtain suspensions with an optical
density depending on the bacterial count. To achieve the
selected initial count of the probiotic and the test strain,
0.5 mL of the obtained solution was added to the media
with pre-added fructans. To determine the synbiotic
factor, co-cultivation lasted during 10 h under the same
conditions. Sampling took place at the beginning and
end of fermentation.
Microbial count. Microbial count was conducted
in triplicate by seeding tenfold dilutions in Petri dishes
with the media. Colonies of B. cereus and S. enterica
were counted after 24 h of aerobic growth at 37°C in
MRS medium [35]. B. bifidum colonies were counted
after 48 h of growth in BFM medium with the
following composition (g/L): peptone – 10, NaCl – 5.0,
lactulose – 5.0, L-cysteine – 0.5, riboflavin – 0.01,
yeast extract – 7, meat extract – 5, starch – 2, thiamine
chloride – 0.01, and lithium citrate – 3.3 [36]. The pH
was adjusted to 5.5 by adding propionic acid (5 mL/L).
The dishes were incubated under anaerobic conditions at
37°C using a BD GasPak™ Anaerobic Container System.
Determining the content of organic acids. The
concentration of organic lactic and acetic acids was
determined by high-performance liquid chromatography
(HPLC) according to a slightly modified standard
procedure by the refractometric signal [37]. The
experiment involved an Agilent 1220 Infinity chromatograph
(Santa Clara, CA, USA) with an Agilent Hi-
Plex H column (250×4.6 mm). The supernatant was
centrifuged at 12 000 rpm for 15 min, then filtered
through 0.45-μm cellulose acetate membranes (HAWP,
MF-Millipore, St. Louis, MO, USA). Other parameters
included: sample volume – 3 μL, temperature – 50°C,
mobile phase flow rate (0.002 M H2SO4) – 0.3 mL/min.
To prepare calibration solutions, the concentrated
organic acids were diluted in their mobile phase to
concentrations of 1, 5, and 10 g/L.
Determining the structure of fructans. The
structure of the isolated fructans was analyzed using
carbon-13 nuclear magnetic resonance (13C-NMR)
spectrometry following the procedure described by
Mariano et al. [38]. One-dimensional spectra were
obtained at 298 K on a BRUKER CXP-200 NMR
spectrometer (50.3 MHz) (Bruker, Germany) in an
aqueous solution of D2O. Inulin (Orafti ® HSI, BENEOORAFTI,
Belgium) and oligofructose (Orafti ® P95,
BENEO-ORAFTI, Belgium) served as standard.
Calculating the synbiotic factor. The synbiotic
factor was calculated in accordance with the
previously approach proposed by Karetkin et al. and
Evdokimiova et al. [21, 27]. The microbial count, pH,
and the concentration of organic acids were determined
at the initial and final stages of co-cultivation. Based on
the data obtained, the synbiotic factor was calculated as
follows:
(1)
where SF is the synbiotic factor; pHopt is pH optimal
for test strain growth; pHmin is pH the minimal for test
strain growth; [LA] is the concentration of undissociated
lactic acid, (mg/mL); [AA] is the concentration of
undissociated acetic acid, mg/mL; MICLA is the minimal
inhibiting concentration of lactic acid, mg/mL; MICAA
is the minimal inhibiting concentration of acetic acid,
( ) 2 2 2
1 2 3 0 1 1 2 2 3 3 12 1 2 23 2 3 13 1 3 123 1 2 3 11 1 22 2 33 3 , , kYx x x b bx = + + b x + b x + b x x + b x x + b x x + b x x x + b x + b x + b x
[ ] [ ] 1 1
min
opt min LA AA
pH pH LA AA SF
pH pH MIC MIC
−   α    β 
= ×  −    ×  −    −        
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mg/mL; α and β are constants for B. cereus or
S. enterica, which we defined in [39] (Table 1).
Design of experiment and statistical analysis.
The central composition rotatable design was applied
to study the effect of the following parameters on the
co-cultivation: the precipitant concentration x1, the
fractional composition of A. lappa fructans, the initial
count (decimal logarithm) of bifidobacteria (x2), and
test strain cells (x3). Synbiotic factor (Y1) and final test
strain count (Y2) were chosen as response functions. The
variation levels were determined based on data obtained
from [21, 27] (Tables 3 and 4). The response function
was presented as follows:
The significance test of the coefficients for Eq. (2)
was based on the t-test. The adequacy of the equation
was assessed by the Fisher criterion at P = 0.05.
Response surfaces were calculated and constructed
using the MathLab software. The scanning method
with a variable step as in [40] was applied to determine
the extreme values of the factors. The method consists
in a sequential search for points in the parametric
space using the GeoGebra Classic software 6.0.694.0
(University of Salzburg, Salzburg, Salzburg state,
Austria).
RESULTS AND DISCUSSION
13C-NMR specters of Arctium lappa L. root
fructan fractions. Figure 1 illustrates 13C-NMR
specters of standard inulin and oligofructose, purified
from A. lappa L. fructan fractions and precipitated by
different concentrations of ethanol.
The analysis was based on the difference between
the chemical shifts of the carbon atoms of the monomers
located inside the chain of oligo- and polysaccharides
and the atoms of the terminal monomers [24]. The
chemical shifts of carbon atoms in the standard and test
samples are typical of inulin-type fructans (Table 2).
The obtained spectra of fructan fractions were closer
to those of commercial oligofructose in terms of the
number and location of peaks. In terms of signal
intensity, they were between standard oligofructose
and highly purified inulin. None of the test samples
demonstrated peaks at the terminal C-2 atom of
D-fructofuranose. However, the test samples showed an
increase in the relative areas of the peaks, as well as an
increase in the precipitant concentration for all carbon
Table 1 Minimal inhibitory concentrations, constants, and optimal and minimal pH during the process of Bacillus cereus or
Salmonella enterica inhibition by lactic and acetic acids
Test strain pHopt pHmin MICLA, mg/mL MICAA, mg/mL α β
Bacillus cereus 7.0 4.9 3.48 3.20 0.25 0.40
Salmonella enterica 7.0 5.0 2.25 1.77 1.70 0.90
Figure 1 13C-NMR specters in distilled water with D2O
and (a) HSI inulin, (b) oligofructose and Arctium lappa L.
fructan fractions precipitated by ethanol with concentrations,
(c) 20.0% (Burd-20), (d) 32.2% (Burd-32), (e) 50.0% (Burd-50),
(f) 67.8% (Burd-68), and (g) 80.0% (Burd-80)
e
f
g
a
b
c
d
100
ppm
90 80 70 60
atoms of the D-fructofuranose residues within the chain
(forming a 2→1 bond).
All the peak areas for the corresponding carbon
atoms were smaller than for inulin, and the values
obtained for Burd-50 and Burd-68 were closest to
2 2 2
3 3 12 1 2 23 2 3 13 1 3 123 1 2 3 11 1 22 2 33 3 b x + b x x + b x x + b x x + b x x x + b x + b x + b x
[ ] [ ] 1
LA AA
LA AA MIC MIC
 α    β 
−    ×  −          
( ) 2 2 2
1 2 3 0 1 1 2 2 3 3 12 1 2 23 2 3 13 1 3 123 1 2 3 11 1 22 2 33 3 , , k Yx x x = b + b x + b x + b x + b x x + b x x + b x x + b x x x + b x + b x + b x
[ ] [ ] 1 1
min
opt min LA AA
pH pH LA AA SF
pH pH MIC MIC
−   α    β 
= ×  −    ×  −    −        
(2)
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oligofructose. The differences in the relative proportions
of peak areas for Burd-20 and Burd-32 were small
and manifested as unidentified peaks in the Burd-20.
Probably, carbohydrates of similar molecular weight
were precipitated at these ethanol concentrations.
No correlation was observed between the relative
proportions of the peak areas for the terminal atoms of
glucopyranose and fructofuranose.
Synbiotic antagonism to Bacillus cereus and
response surface analysis. To assess the effect of
various factors on the anti-B. cereus activity of the
synbiotic composition, the experiment was carried out
according to a central composition rotatable design. The
limiting values of ethanol concentration were chosen
as 20 and 80% as in [27]. The average polymerization
degree of the precipitated carbohydrate fraction was at
its highest at 20% of ethanol.
Zeaiter et al. used 33% ethanol to obtain a fraction
of inulin-type artichoke fructans with an average degree
of 32–42 [26]. Table 3 demonstrates the planning matrix,
as well as the experimental and calculated values of
response functions, i.e., the synbiotic factor and the final
test strain cell count.
The coefficients of the response function equation
were determined from the values of the synbiotic factor
and the final bacterial count. The response surface was
constructed according to Eq. (2) (Fig. 2). The adequacy
of the equations was confirmed by Fisher’s criterion
F = 1.681 and 1.66: it was below the tabulated F = 4.704
at P = 0.05.
The synbiotic factor reduced of the specific
growth rate of the test strain. It showed how many
times the specific growth rate decreased relative
to the optimal value under the effect of inhibitors
produced by the probiotic and the prebiotic. The
maximal inhibition corresponded to the lowest value
of the synbiotic factor [21]. The synbiotic factor of the
composition of Bifidobacterium bifidum and A. lappa
root fructans had a positive linear dependence on
the precipitant concentration (x1). Therefore, the
composition with fructans precipitated by the lowest
alcohol concentration had the greatest inhibitory effect
on B. cereus because it had the highest average degree of
polymerization. This result confirms the data obtained
by us before [27]. The dependence of the synbiotic
factor on the initial probiotic count (x2) was parabolic
and reached its minimum at +1.156, which corresponded
Table 2 13C-NMR chemical shifts of β-D-fructofuranose and α-D-glucopyranose of HSI inulin standard samples, oligofructose, and
experimental samples of Arctium lappa L. root fructan fractions precipitated with various concentrations of ethanol: 20% (Burd-20),
32.2% (Burd-32), 50% (Burd-50), 67.8% (Burd-68), and 80% (Burd-80)
Carbon atom Chemical shift, ppm
Inulin Oligofuctose Burd-20 Burd-32 Burd-50 Burd-68 Burd-80
C-2 f (terminal) – 103.88 – – – – –
C-2 f (2→1 bond) 103.4262 103.2645 104.26 104.29 103.40 103.43 103.43
– 97.98 99.31 99.25 – – –
C-1 g (terminal) – 92.7261 93.41 – – – –
– 88.76 – – – – –
C-5 f (terminal) – – – – – – –
C-5 f (2→1 bond) 81.3253 81.3253 82.40 82.30 81.44 81.50 81.44
– 77.71 – – – – –
C-3 f (2→1 bond) 77.2824 77.1477 78.31 78.06 77.32 77.48 77.54
C-3 f (terminal) – 76.77 77.16 77.00 76.07 – 77.32
– – – – – – –
C-4 f (2→1 bond) 74.5872 74.70 75.85 75.76 74.83 74.96 74.86
C-4 f (terminal) – – – 75.44 – – –
C-3 g (terminal) – 72.8892 73.75 73.75 – 72.79 73.04
C-5 g (terminal) – 72.6736 – – – – –
C-2 g (terminal) – 71.4337 72.53 72.41 – 71.58 71.54
C-4 g (terminal) 69.4 69.3045 71.06 70.94 70.11 70.17 70.46
– 68.39 69.09 68.83 68.13 68.19 68.13
C-6 f (2→1 bond) – 64.1566 65.38 64.49 64.30 64.30 64.43
C-6 f (terminal) – 63.67 64.65 63.47 63.69 63.79 63.69
– – 64.36 – – – –
C-1 f (2→1 bond) 62.3777 62.4858 63.53 62.06 62.61 62.61 62.57
C-1 f (terminal) 61.16 60.84 62.06 61.78 61.04 61.20 61.27
C-6 g (terminal) – 60.49 58.78 58.71 57.88 – 57.85
2
Y1 = 0.0211+ 0.008x1 − 0.0074x2 + 0.0032x2
7 7 7 6 6 2 2 3 2 3 Y = 4.9 ×10 −1.6×10 x −1.2 ×10 x − 7.1×10 x x − 7.4 ×10 x
2
1 1 2 2 Y = 0.0211+ 0.008x − 0.0074x + 0.0032x
7 7 7 6 6 2 2 3 2 3 Y = 4.9 ×10 −1.6×10 x −1.2 ×10 x − 7.1×10 x x − 7.4 ×10 x
2
1 1 2 2 Y = 0.0211+ 0.008x − 0.0074x + 0.0032x
7 7 7 6 6 2
2 2 3 2 3 3 Y = 4.9 ×10 −1.6×10 x −1.2 ×10 x − 7.1×10 x x − 7.4 ×10 x (4)
(3)
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probiotic count of 7.69 lg(CFU/ml) and the A. lappa
fructan fraction precipitated with 20% ethanol.
As the initial bifidobacterial count (x2) increased,
the final bacterial count decreased (Fig. 2b). The
dependence of the final test strain count (Y2) on the
initial one (x3) was parabolic. The maximal value of
the response function was reached when the bifidobacterial
count was minimal, i.e., 6.0 lg(CFU/mL), and
the initial test strain count in the design center was
5.5 lg(CFU/mL). At these values, the inhibition was
least effective. The minimal final test strain count was
around the highest seed doses of both the probiotic and
to 7.69 lg(CFU/mL). As x2 rose (> +1.156), the synbiotic
factor also increased.
All experimental values appeared to be much higher
than those obtained by calculation, both at the minimal
point and at high values of x2. Apparently, the observed
decrease in the antagonistic activity could be ignored.
All the coefficients at x3 proved insignificant, and the
initial test strain count did not affect the synbiotic factor.
Within the range of the variables, the minimal value of
the synbiotic factor (maximal suppression of the test
strain) was 0.0033 and lied at the point with coordinates
–1.682 and 1.156, which corresponded to the initial
Figure 2 Synbiotic factor response surface (a) and final bacterial count (b), CFU/mL
Table 3 Range of variation and encoding of variables: experimental and calculated values of response functions for Bacillus cereus
Test
No.
Factors Synbiotic factor Final bacterial count,
Precipitant concentration lg(CFU/mL)*
(EtOH), %
Initial prebiotic count,
lg(CFU/mL)
Initial bacterial count,
lg(CFU/mL)
z1 x1 z2 x2 z3 x3 SFobs SFpred Xbac obs Xbac pred
1 67.8 +1 7.6 +1 6.4 +1 0.0267 0.0249 5.72 6.77
2 67.8 +1 7.6 +1 4.6 –1 0.0310 0.0249 7.48 7.65
3 67.8 +1 6.4 –1 6.4 +1 0.0420 0.0397 7.80 7.72
4 67.8 +1 6.4 –1 4.6 –1 0.0433 0.0397 7.77 7.80
5 32.2 –1 7.6 +1 6.4 +1 0.0136 0.0089 5.43 6.77
6 32.2 –1 7.6 +1 4.6 –1 0.0092 0.0089 7.58 7.65
7 32.2 –1 6.4 –1 6.4 +1 0.0224 0.0236 7.66 7.72
8 32.2 –1 6.4 –1 4.6 –1 0.0244 0.0236 7.79 7.80
9 20.0 –1.682 7.0 0 5.5 0 0.0188 0.0076 7.77 7.69
10 80.0 +1.682 7.0 0 5.5 0 0.0404 0.0346 7.63 7.69
11 50.0 0 6.0 –1.682 5.5 0 0.0471 0.0425 7.82 7.88
12 50.0 0 8.0 +1.682 5.5 0 0.0179 0.0177 7.51 7.34
13 50.0 0 7.0 0 4.0 –1.682 0.0268 0.0211 7.77 7.69
14 50.0 0 7.0 0 7.0 +1.682 0.0301 0.0211 6.63 6.84
15 50.0 0 7.0 0 5.5 0 0.0213 0.0211 7.74 7.69
16 50.0 0 7.0 0 5.5 0 0.0122 0.0211 7.68 7.69
17 50.0 0 7.0 0 5.5 0 0.0239 0.0211 7.76 7.69
18 50.0 0 7.0 0 5.5 0 0.0241 0.0211 7.70 7.69
19 50.0 0 7.0 0 5.5 0 0.0205 0.0211 7.56 7.69
20 50.0 0 7.0 0 5.5 0 0.0237 0.0211 7.66 7.69
* the response function was calculated as CFU/mL; the results are given on a logarithmic scale
a b
% EtOH
X0 Bif
X0 Bac
X0 Bif
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surface was parabolic, and its analytical minimum was
outside the variation range. These surfaces demonstrated
an increase in the initial bifidobacterial count, which
followed the increase in the initial Salmonella count.
The larger bifidobacterial count resulted in
the greatest suppression, which varied from x3 = – 1.682
to x3 = 0. Thus, the response surface method made it
possible to define the critical value of the Salmonella
count (6.5 lg(CFU/mL)). When this value was exceeded,
only the maximal count of viable bifidobacterial cells
could inhibit the pathogen. If the initial pathogen count
exceeded 6.91 lg(CFU/mL), the response surfaces had a
saddle shape.
The global minimum of the response function within
the variation range was determined by the variable
step scanning method. Initially, all variables for each
coordinate had an interval with two equal subintervals.
The values of the function were calculated at the nodes
of the resulting grid to select the optimal point with
the lowest synbiotic factor. Subsequently, the interval
was cut in two. The calculation cycles continued until
the interval along one of the coordinates fell below
0.001. The minimum was determined at the border of
the region in coordinates –1.682, +1.682, and +1.682.
Therefore, the greatest antagonistic effect was expected
at the lowest alcohol concentration of 20% and the
2 2
Y1 = 0.029 + 0.012x1 − 0.007x3 + 0.006x1x2 − 0.006x1x3 + 0.007x1x2x3 + 0.004x1 + 0.003x2
8 7 7 7 2 7 2 7 2
2 2 3 1 2 3 Y = 4.14 ×10 − 4.58×10 x + 3.95×10 x − 3.61×10 x − 2.08×10 x + 2.63×10 x
2 2
3 1 2 1 3 1 2 3 1 2 0.007x + 0.006x x − 0.006x x + 0.007x x x + 0.004x + 0.003x
7 7 2 7 2 7 2
2 3 1 2 3 x + 3.95×10 x − 3.61×10 x − 2.08×10 x + 2.63×10 x
Figure 3 Synbiotic factor response surface as a function of ethanol concentration (x1) and initial probiotic count (x2) at fixed initial
Salmonella count (x3)
x3 = +1.682 x3 = 0.46
x3 = 0 x3 = – 0.86 x3 = – 1.682
the test strain. As the initial probiotic and test strain
concentrations increased, the final bacterial count
plummeted. Probably, bacilli inhibited their own growth
by their own metabolites, i.e., lactic acid.
Antagonism of synbiotic compositions against
Salmonella enterica and response surface analysis.
Table 4 shows the design matrix with experimental
and calculated values of the response functions for
S. enterica. The variation range of variables in natural
coordinates did not differ from that of bacilli, except for
the shift in the initial test strain count by +1 lg(CFU/mL).
The response surface analysis for synbiotic factor (Y1)
was represented as the following equation confirmed by
Fisher’s criterion (F = 3.99 < 4.87, P = 0.05):
The coefficients for all factors and their pairwise
interactions turned out to be significant. The response
surfaces were calculated for fixed (Fig. 3). For all the
surfaces obtained, the smallest value of the response
function within the variation range was obtained when
the precipitant concentration was minimal. When
was below 0.46, which corresponded to the initial
Salmonella count (6.91 lg(CFU/mL)), the response
(5)
X0 Bif
% EtOH
X0 Bif
% EtOH
X0 Bif
% EtOH
X0 Bif
% EtOH
X0 Bif
% EtOH
372
Evdokimova S.A. et al. Foods and Raw Materials. 2022;10(2):365–376
highest initial bifidobacterial count of 8.0 lg(CFU/mL).
Unlike the bacilli, the metabolism of the test strain
affected the synbiotic factor and reduced its value.
Probably, the reduction happened because of extra acid
production.
The final Salmonella count equation (F = 2.20 < 4.74,
Р = 0.05) looked as follows:
As for the synbiotic factor, all factors had a
significant impact on target function Y2. The response
surfaces were calculated for fixed x3 values (Fig. 4).
The surface was parabolic in coordinates x1 and x2.
The maximal value of the final test strain count was
8.68 lg(CFU/mL) in coordinates 0, –1.101, and +1.685.
Thus, the synbiotic composition of fructans precipitated
by 50% ethanol and bifidobacteria with the initial count
of 6.34 lg(CFU/mL) had the lowest antagonistic effect
against Salmonella. As the initial Salmonella count
increased, the efficiency weakened.
The effect of the initial test strain count on the
response function was not symmetrical to the design
center because the minimum of the function for this
variable was at the point – 0.749, 5.83 lg(CFU/mL). The
dependence had a quadratic nature. As a result, the
final Salmonella count remained almost the same when
the initial count was below 6.5 lg(CFU/mL). When the
values were large, the value of the response function
rose sharply. Therefore, the initial Salmonella count of
6.5 lg(CFU/mL) was critical from the standpoint of
microbiology.
The response paraboloid was symmetrical to the
design center of variable. Thus, both fructan fractions
precipitated by the highest and the lowest alcohol
concentrations possessed the same inhibition effects.
However, as the initial probiotic count exceeded 6.34
lg(CFU/mL), the inhibition of the pathogen increased.
The lowest values of the final S. enterica count (and the
greatest antagonistic effect) within the variation range
were achieved at the maximal initial bifidobacterial
count of 8.0 lg(CFU/mL) in the medium with A. lappa
root fructan fractions precipitated with 20 or 80%
ethanol.
Previously, we considered Staphylococcus aureus
as the test strain and also found out that the effect of
A. lappa fructans precipitated with 40 and 60% ethanol
was weaker than those precipitated with 20 or 80%
ethanol [27]. Apparently, the highest average degree of
polymerization was effective because the carbohydrate
substrate was less available. The lowest degree of
polymerization was effective because the bifidobacteria
consumed the substrate faster and thus produced more
metabolites. This issue, however, requires further
research.
Table 4 Range of variation and encoding of variables: experimental and calculated values of response functions for Salmonella
enterica
Test
No.
Factors Synbiotic factor Final bacterial count,
Precipitant concentration lg(CFU/mL)*
(EtOH), %
Initial prebiotic count,
lg(CFU/mL)
Initial bacterial count,
lg(CFU/mL)
z1 x1 z2 x2 z3 x3 SFobs SFpred Xsal obs Xsal pred
1 67.8 +1 7.6 +1 7.4 +1 0.0544 0.0477 8.55 8.58
2 67.8 +1 7.6 +1 5.6 –1 0.0636 0.0596 8.53 8.47
3 67.8 +1 6.4 –1 7.4 +1 0.0233 0.0214 8.71 8.67
4 67.8 +1 6.4 –1 5.6 –1 0.0689 0.0616 8.64 8.59
5 32.2 –1 7.6 +1 7.4 +1 0.0114 0.0088 8.58 8.58
6 32.2 –1 7.6 +1 5.6 –1 0.0265 0.0267 8.41 8.47
7 32.2 –1 6.4 –1 7.4 +1 0.0328 0.0351 8.60 8.67
8 32.2 –1 6.4 –1 5.6 –1 0.0278 0.0247 8.60 8.59
9 20.0 –1.682 7.0 0 6.5 0 0.0225 0.0202 8.51 8.49
10 80.0 +1.682 7.0 0 6.5 0 0.0525 0.0602 8.47 8.49
11 50.0 0 6.0 –1.682 6.5 0 0.0468 0.0371 8.61 8.64
12 50.0 0 8.0 +1.682 6.5 0 0.0221 0.0371 8.46 8.44
13 50.0 0 7.0 0 5.0 –1.682 0.0399 0.0413 8.59 8.63
14 50.0 0 7.0 0 8.0 +1.682 0.0179 0.0162 8.77 8.74
15 50.0 0 7.0 0 6.5 0 0.0328 0.0287 8.61 8.62
16 50.0 0 7.0 0 6.5 0 0.0289 0.0287 8.61 8.62
17 50.0 0 7.0 0 6.5 0 0.0334 0.0287 8.65 8.62
18 50.0 0 7.0 0 6.5 0 0.0244 0.0287 8.63 8.62
19 50.0 0 7.0 0 6.5 0 0.0230 0.0287 8.63 8.62
20 50.0 0 7.0 0 6.5 0 0.0308 0.0287 8.57 8.62
* the response function was calculated as CFU/mL; the results are given on a logarithmic scale
(6)
8 7 7 7 2 7 2 7 2
2 2 3 1 2 3 Y = 4.14 ×10 − 4.58×10 x + 3.95×10 x − 3.61×10 x − 2.08×10 x + 2.63×10 x
7 7 7 2 7 2 7 2
2 3 1 2 3 4.58×10 x + 3.95×10 x − 3.61×10 x − 2.08×10 x + 2.63×10 x
373
Evdokimova S.A. et al. Foods and Raw Materials. 2022;10(2):365–376
In this study, we considered lactic and acetic acids
as inhibitors. As proved by Prosekov et al., many
bifidobacteria can produce antimicrobial peptides
(bacteriocins), and some representatives of B. bifidum
are among them [41]. However, their synthesis usually
becomes active at the stationary phase, and by that time
the bifidobacterial count in the co-culture of bacilli
and Salmonella stop growing. Therefore, the synbiotic
factor calculations did not take into account the effect of
bacteriocins. Further research is required to study these
inhibitors under conditions close to real, e.g., intestinal
simulators with a continuous slow medium flow.
The approach proposed in this paper can also be
applied to non-plant prebiotics. Lactulose is one of
the best prebiotics [42]. It is often combined with
other prebiotics, such as fructooligosaccharides, to
make up functional foods. Scientists also turn to
oligosaccharides of goat’s milk, which are a mix of triand
tetrasaccharides that consist of glucose, fructose,
galactose, and their acylated derivatives [43]. Obviously,
the qualitative and quantitative composition affects the
action of the prebiotic both separately and as part of a
synbiotic composition. Our approach can be applied to
similar studies in vitro.
CONCLUSION
In this research, the highest synbiotic efficiency
belonged to the fraction of fructans with a higher degree
of polymerization precipitated by the lowest ethanol
concentration and the highest bifidobacterial count. The
study established a quantitative relationship between the
bifidobacteria and the parameters of fructan production
and the antagonistic activity of their synbiotic
composition. We also determined the effect of the ratio
of probiotic and pathogen counts on antagonism. The
proposed approach can substantiate the composition
of new synbiotics. In the future, we plan to study other
compositions of probiotics and prebiotics in vivo to find
their optimal ratio.
CONTRIBUTION
S. Evdokimova and B. Karetkin developed the
research concept. E. Guseva and I. Shakir were
responsible for data curation and formal analysis.
B. Karetkin acquired the funding. S. Evdokimova and
N. Khabibulina performed the experiments. B. Karetkin
and E. Guseva developed the methodology. B. Karetkin
supervised the project. E. Guseva and M. Zhurikov
worked with the Software. I. Shakir validated the
obtained data. S. Evdokimova and M. Zhurikov developed
the infographics. S. Evdokimova wrote the original
draft. B. Karetkin and V. Panfilov edited the
manuscript. All the authors discussed the results and
contributed to the final manuscript. All the authors
have read and agreed to the published version of the
manuscript.
CONFLICT OF INTEREST
The authors declare that there is no conflict of
interests regarding the publication of this article.
ACKNOWLEDGEMENTS
NMR spectrometry was performed on the equipment
of the Mendeleev Center for Collective Use.
The authors would like to express their gratitude to
Andrey B. Polyakov.

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