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Bacteriocin production optimization applying RSM and hybrid (ANN-GA) method for the indigenous culture of Pediococcus pentosaceus Sanna 14

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The present study optimized the submerged fermentation conditions of Pediococcus pentosaceus Sanna 14 culture to improve bacteriocin yield by applying response surface methodology (RSM) and hybrid artificial neural networkgenetic algorithm (ANN-GA). A full factorial central composite design (CCD) of RSM was applied to assess the effect of four principle variables, i.e., pH (4.0–8.0), agitation (120–220 rpm), sucrose (20–40 g/l), and peptone (5–20 g/l), on the yield of bacteriocin. The RSM optimized the experimental results of pH (7.0), agitation (200), sucrose (40 g/l), and peptone (20 g/l), and supported a higher yield (2.4 g/l) of bacteriocin and was validated applying ANN-GA methodology. The RSM bacteriocin yield (2.4 mg/l) was found to match with the ANN-predicted yield (2.4 mg/l). GA results confirmed the genetic fitness of the culture of P. pentosaceus Sanna 14 during fermentation. The present study registered a sixfold increase in bacteriocin yield (2.4 mg/l) compared to the yield (0.4 mg/l) of the unoptimized process conditions.
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Journal of Applied Pharmaceutical Science Vol. 11(10), pp 050-060, October, 2021
Available online at http://www.japsonline.com
DOI: 10.7324/JAPS.2021.1101008
ISSN 2231-3354
Bacteriocin production optimization applying RSM and hybrid
(ANN-GA) method for the indigenous culture of Pediococcus
pentosaceus Sanna 14
Raje Siddiraju Upendra1* , Pratima Khandelwal2, Mohammed Riyaz Ahmed1
1Department of Bioelectronics Engineering, School of Multidisciplinary Studies, REVA University, Bangalore, India.
2Pratima Khandelwal, Founder, ‘FlyHigh’ Educational and Excellence Services, Bangalore, India.
ARTICLE INFO
Received on: 29/04/2021
Accepted on: 19/07/2021
Available Online: 03/10/2021
Key words:
Pediococcus pentosaceus,
bacteriocin, response surface
methodology design, artificial
neural network, genetic
algorithms.
ABSTRACT
The present study optimized the submerged fermentation conditions of Pediococcus pentosaceus Sanna 14 culture
to improve bacteriocin yield by applying response surface methodology (RSM) and hybrid articial neural network-
genetic algorithm (ANN-GA). A full factorial central composite design (CCD) of RSM was applied to assess the
effect of four principle variables, i.e., pH (4.0–8.0), agitation (120–220 rpm), sucrose (20–40 g/l), and peptone (5–20
g/l), on the yield of bacteriocin. The RSM optimized the experimental results of pH (7.0), agitation (200), sucrose (40
g/l), and peptone (20 g/l), and supported a higher yield (2.4 g/l) of bacteriocin and was validated applying ANN-GA
methodology. The RSM bacteriocin yield (2.4 mg/l) was found to match with the ANN-predicted yield (2.4 mg/l).
GA results conrmed the genetic tness of the culture of P. pentosaceus Sanna 14 during fermentation. The present
study registered a sixfold increase in bacteriocin yield (2.4 mg/l) compared to the yield (0.4 mg/l) of the unoptimized
process conditions.
INTRODUCTION
Lactic acid-producing bacteria (LAB) are Gram-positive,
non-spore forming, non-motile, non-respiring bacteria (Montet and
Ray, 2016; Ray, 2020). The various antimicrobial and industrially
important compounds produced by these LAB comprise lactic
acid (Mayo et al., 2008), acetic acid (Ramsey et al., 2014), ethanol
(Ray and Joshi, 2014), formic acid, fatty acids, hydrogen peroxide,
and bacteriocin (Vanderbergh, 1993). Bacteriocins are ribosomal
synthesized small antimicrobial proteins produced mainly by
members of LAB and possess antimicrobial activity toward
other bacteria, while synthesizing organisms are resistant to their
own bacteriocins (Caulier et al., 2019; Chen and Hoover 2003;
Perez et al., 2014). Bacteriocins are reputed as bio-preservatives
due to their generally recognized as safe status (Singh, 2018).
Bacteriocins are classied into different classes and turned out
to be inactive as soon as they were treated with gastrointestinal
enzyme in the stomach and were found to be harmless for human
consumption (Khandelwal and Upendra, 2019; Khandelwal et al.,
2015). Class I bacteriocins named Lantibiotics are bound to the
type II lipid of the bacterial membrane which serves as a transporter
of N-acetylmuramic acid, N-acetylglucosamine subunits of
peptidoglycan layer from bacterial cytoplasm to its cell wall. This
action prevents the synthesis of the bacterial cell wall and promotes
cell death. In addition, bacteriocins apportioned in the class II type
possess amphiphilic helical structures and insert themselves into
to the bacterial membrane and promote depolarization, which in
turn leads to the death of the bacterial cell. Class III bacteriocins
catalyze the breakdown of the cell wall of Gram-positive bacteria,
cause the lysis of bacteria, and promote its death (Tulini, 2014).
The human gastrointestinal (GI) tract consists of layers such as
mucosa, submucosa, epithelial cell lining, mucus layer, and
serosa. Probiotic microorganisms are colonized in the gut of the
*Corresponding Author
R. S. Upendra, Head Research and Innovation, Department of
Bioelectronics Engineering, School of Multidisciplinary Studies, Reva
University, Bangalore, India. E-mail: upendra.rs @ reva.edu.in
© 2021 R. S. Upendra et al. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License
(https://creativecommons.org/licenses/by/4.0/).
Upendra et al. / Journal of Applied Pharmaceutical Science 11 (10); 2021: 050-060 051
human GI tract and produce bacteriocins to compete with the
sensitive bacteria, hence reducing the load of bacteriocin-sensitive
bacteria present at the GI tract. Due to the natural harsh conditions
of the human gut, the colonized probiotic bacteria may produce
bacteriocins lesser than the minimal inhibitory concentration
levels, hence it inhibits the bacterial growth and are not harmful to
humans (Dicks et al., 2018).
Bacteriocins, as a probiotic ingredient, exhibit different
food applications, such as extend shelf life of food, preservation
(Balciunas et al., 2013), control microbial spoilage of beer, wine,
alcohol fermentation (Gabrielsen et al., 2014; Kjos et al., 2011),
and are also used in antimicrobial packaging lm to prevent
microbial growth (Malhotra et al., 2015). A bacteriocin named
Nisin was approved by the US-Food and Drug Administration
as a food preservative and is widely used in canned foods, dairy
products, meat products, and alcoholic beverages in more than 50
countries around the world (Barbour et al., 2020; Zhang and Jin,
2015).
Due to the wide use of conventional antibiotics in
dealing with human diseases, multidrug resistance (MDR)
strains appeared and are a major threat to mankind. To control
MDR strains in food and feed products, bacteriocins can be used
as antimicrobial substances instead of antibiotics. Bacteriocins
are a viable alternative to traditional antibiotics in controlling
infections caused by Gram-negative bacteria, i.e., Escherichia coli
and Salmonella typhimurium, and Gram-positive bacteria, such as
Listeria monocytogenes (Cotter et al., 2012; Helander et al., 1997;
Khan et al., 2015). Bacteriocins are used for therapeutic purposes,
i.e., atopic dermatitis, abdominal ulcers, and immune deciency
conditions (Perez et al., 2014). Nisin is used in the development
of various healthcare products, such as toothpaste and skin care
products, and in the treatment of cancer therapy (Mishra et al.,
2020; Yang et al., 2014).
Several groups of LAB, i.e., Enterococcus, Oenococcus,
Leuconostoc, Lactobacillus, Pediococcus, Lactococcus, and
Streptococcus, were reported with bacteriocin-producing abilities
(Lorca and de Valdez, 2009). Among these genera, nearly 415
species were reported to be LAB species (Euzéby, 1997; Parte,
2014). Bacteriocins such as pediocin AcH or pediocin PA-1
(Motlagh et al., 1992) isolated from the strains of Pediococcm
acidilactici were used in meat and vegetable fermentations
(Bhunia et al., 1998). In several food systems, bacteriocins
(pediocins) were used successfully to inhibit foodborne pathogens
such as L. monocytogenes (Pucci et al., 1988; Yousef et al.,
1991). Pediococcus pentosaceus was isolated for the rst time
in the year 1953 from cucumber fermentation (Costilow et al.,
1956). Several investigators proved the bacteriocin-producing
abilities of the strain P. pentosaceus (Gutiérrez-Cortés et al.,
2018; Svetoslav and Dicks, 2009; Wu et al., 2004; Zommiti et
al., 2018).
The biggest challenge in the bioprocess was providing
optimal fermentation conditions for the economically feasible
bioprocesses (Upendra et al., 2013). Response surface
methodology (RSM) is an effective and convenient method for
designing experiments, building models, and screening key factors
of process conditions (Kar et al., 2009; Upendra and Khandelwal,
2021; Upendra et al., 2015b). RSM employed with the hybrid
articial neural network-genetic algorithm (ANN-GA) will be able
to address the nonlinear relationship between the actual and coded
factors (Upendra et al., 2014a). The hybrid ANN-GA provides
validated results and assesses the genetic tness of organisms
during the process.
In our earlier studies, bacteriocin-synthesizing
LAB species, identied from unexplored food sources, were
characterized as P. pentosaceus through 16S RNA typing. 16S RNA
forward strand sequence was deposited in a nucleotide data bank,
i.e., GenBank, of NCBI with issued accession number MF183113
(Upendra et al., 2016a). Scanty research is documented on the
optimization of the submerged fermentation (SmF) process for
higher bacteriocin yield applying RSM and hybrid ANN-GA. No
study was found on the optimization of P. pentosaceus SmF culture
for higher bacteriocin yield applying RSM and hybrid ANN-GA.
With this lacuna, the aim of the present study is to optimize the
conditions of the SmF process for the indigenous cultures of P.
pentosaceus to achieve enhanced yield of bacteriocins by applying
the RSM and hybrid ANN-GA design models. A full factorial
central composite design (CCD) of RSM was used to evaluate
the effect of four SmF process variables, such as pH, agitation,
sucrose, and peptone, on the yield of bacteriocin. Furthermore,
RSM results were validated by applying the hybrid ANN-GA
methodology. The study reported a sixfold increase in bacteriocin
yield (2.4 mg/l), with respect to the unoptimized process yield (0.4
g/l) for the SmF cultures of P. pentosaceus Sanna 14.
MATERIALS AND METHODS
The chemicals and all the reagents used in the preset
study represent analytical grade quality (Merck and Qualigens).
Microorganism
Bacteriocin-producing strains employed in the study,
such as P. pentosaceus Sanna 14 strain (GenBank MF183113), were
isolated by the same research group (Khandelwal and Upendra,
2019; Khandelwal et al., 2017). Pediococcus pentosaceus LAB
culture was grown on Mann Rogassa Sharpe (MRS) agar slants
at 37°C with pH adjusted to 6.2 for 18–24 hours (Panda et al.,
2009) and completely grown slants were preserved at 4°C for
optimization studies (Thirumurugan et al., 2013). The inoculum
was prepared on the MRS broth pH 6.2 by inoculating a loop full
of microorganisms from a culture plate in aseptic conditions and
incubated for 18–24 hours, 37°C at 120 rpm (Zamr et al., 2000)
in the orbital shaker incubator (Remi Pvt. Ltd, Bombay, India).
Response surface methodology (RSM)
Experimental design using CCD of RSM
RSM is a pool of mathematical and modeling tools
applied in building an experimental model design to analyze
the response impact of multivariable process parameters on the
overall process yield (Kar et al., 2009; Upendra et al., 2014b,
2015b). Type of carbon source, type of nitrogen source, pH,
temperatures, and agitation of the fermentation process were the
most important process parameters inuencing the bacteriocin
yield (Gautam and Sharma, 2009; Upendra, 2017). The present
study developed a four-factor experimental design applying a
CCD of RSM with 30 experimental runs using the Design-Expert
software version 9.0.0.7 to evaluate the optimum conditions of the
Upendra et al. / Journal of Applied Pharmaceutical Science 11 (10); 2021: 050-060
052
four principle bacteriocin SmF process parameters selected from
the literature survey (Biswas et al., 1991; Ray, 1995; Senbagam
et al., 2013; Upendra et al., 2016b), i.e., pH (4.0–8.0), agitation
(120–220 rpm), sucrose (20–40 g/l), and peptone (5–20 g/l). All
were taken at a central-coded value considered as zero. It was
observed from the literature review that sucrose was evidently
the best source for the production of bacteriocin for the culture of
P. pentosaceus (Suganthi and Mohanasrinivasan, 2015). The full
experimental design layout is discussed in Table 1. Optimization
experiments were carried out in batch phases considering the CCD
of the RSM design, as shown in Table 1 in the conical ask (250
ml) with 100 ml volume as production media (MRS + optimized
trail), along with MRS media alone conical ask as unoptimized
process standard. 10% v/v (106 colony forming unit/ml) of culture
of P. pentosaceus strain inoculum (Gutiérrez-Cortés et al., 2018)
was transferred aseptically to 250 ml of production media (MRS
+ optimized trail) and unoptimized conical ask (MRS) and
incubated at 37°C for the period of 72 hours.
Analysis of RSM optimization studies
The RSM optimized values of bacteriocin production
were tested through the analysis of variance (ANOVA) study. A
second-order polynomial response equation was applied to give
the yield of bacteriocins (Eq. 1) as follows:
0 + i = 1n
iXi + i =1n
iXi2 + i =1n j= n
ij (1)
where Y is the bacteriocin yield, bo is the intercept,
bi is the linear direct effect coefcient, and bij is the interaction
effect coefcient. The coded equation is useful for predicting the
combined inuence of factors by comparing the factor coefcients
(Myers and Montgomery, 1995; Upendra and Katta, 2021).
Table 1. Comparison of CCD-RSM and ANN results with bacteriocin yield.
Run pH Agitation
(rpm)
Sucrose
(g/l) Peptone(g/l) Bacteriocin
yield (mg/l)
RSM-
predicted
values
ANN-predicted
values Error
16 50 25 12.5 0.02 0.008 −2.50E-05 0.00008
26 150 55 12.5 1.3 1.2583 1.300153961 0.041
35 100 40 20 0.8 0.8291 0.967150121 0.138
46 150 25 12.5 1.5 1.4987 1.499874792 0.001
57 200 40 5 0.9 1.0458 0.868414534 0.177
65 100 10 20 0.1 −0.05375 0.1003797 0.05
75 200 10 20 0.3 0.2958 0.299768672 0.0032
85 200 40 20 1.5 1.5125 1.499874792 0.0125
95 200 40 5 0.5 0.4625 0.500040397 0.0375
10 6 250 25 12.5 1.3 1.275 1.300153961 0.025
11 5 200 10 5 0.1 0 0.1003797 0.1
12 6 150 25 27.5 1.1 1.1416 1.100107243 0.0416
13 6 150 25 12.5 1.4 1.433 1.398932662 0.0341
14 6 150 −5 12.5 0 0.024 −2.50E-05 0
15 7 100 10 5 0.1 0.0958 0.113797 0.01
16 6 150 25 −2.5 0−0.05 −2.50E-05 0
17 5 100 40 5 0.1 0 0.1003797 0.1
18 6 150 25 12.5 1.4 1.378 1.398932662 0.02
19 6 150 25 12.5 1.4 1.3879 1.398932662 0.01
20 7 100 40 5 0.1 0.1125 0.1003797 0.0125
21 6 150 25 12.5 1.5 1.433 1.499874792 0.066
22 7 100 10 20 0.2 0.02458363 0.200208672 0.1755
23 6 150 25 12.5 1.4 1.367 1.398932662 0.0319
24 4 150 25 12.5 0.2 0.2583 0.200208672 0.05
25 8 150 25 12.5 1.2 1.125 1.201374231 0.075
26 7 200 40 20 2.4 2.405 2.400005788 0.005
27 5 100 10 5 0 0.0625 −2.50E-05 0
28 7 200 10 20 1.1 1.0984 1.100107243 0.09
29 7 200 10 5 0.7 0.67916 1.054303835 0.04
30 7 100 40 20 1.1 1.1125 1.100107243 0
Upendra et al. / Journal of Applied Pharmaceutical Science 11 (10); 2021: 050-060 053
Downstream processing of bacteriocin
After 72 hours of incubation, the bacteriocins produced
were harvested from the spent broth by centrifuging at 10,000 g for
21 minutes at 4°C. Supernatant was treated with solid ammonium
sulfate at 50% saturation and stirred at 4°C for 2 hours, centrifuged
at 14,000 g for 1 hour at 4°C. The pellets thus obtained were
suspended using 25 ml of 0.05 M potassium phosphate buffer (pH
7.0) and used in the estimation of bacteriocin with bovine serum
albumin as standard by employing Lowry’s method (de Arauz et
al., 2009; Upendra et al., 2016a).
Confirmation of bacteriocins by ATR FTIR
Qualitative determination of puried bacteriocin was
achieved by employing the FTIR/Diamond ATR method. The
FTIR model used in the present study was FTIR-8400S, Shimadzu
brand. ATR was xed to the FTIR instrument at 45° angle, with a
sampling area of 1 mm diameter and a sampling depth of several
microns. A salt disk was prepared compressing 10 mg sample
and 100 mg of potassium bromide mixture and was placed on the
ATR diamond disk. The sample was scanned at 4,000–400 wave
numbers (cm−1) for absorbance measurements with 1 cm−1 as
resolution (Halami et al., 2011).
Validation by hybrid ANN-GA
Artificial neural network
The CCD of the RSM design-optimized process
parameters supporting a higher yield of bacteriocin was compared
and validated by applying the multilevel feed forward model of
ANN, designed using Neural Network MATLAB (version R 2014a
software, USA) statistical software for simulation. The same
experimental data of the CCD of RSM design were employed in
designing the ANN analysis. The input variables taken were pH (4–
8), agitation (120–220 rpm), sucrose (4.0–7.0), and fermentation
time (8–14 days). The optimum yield of bacteriocin was used as a
target. The data taken for the assessment were divided into three
sets, such as training set with 70%, followed by validation (15%)
and test (15%) datasets (Upendra et al., 2015a). The validation
studies were carried out using the Levenberg–Marquardt algorithm
consisting of trainlm training function. Assessed variables and
response data were kept between 0 and 1 to reduce the network
error. The normalization equation applied was as follows (Eq. 2):
Ya = Yi – YminYmax – Ymin (2)
where Yn, Ya, Ymin, and Ymax are normalized value, actual
value, minimum value, and maximum value, respectively.
Genetic Algorithm (GA)
The genetic algorithm (GA) is a stochastic-based global
optimizing evolutionary algorithm built on the principle of
survival of the ttest theory proposed by Darwin.
The design follows ve simple steps such as population,
representation, variation, selection, and reproduction (Pasandideh
and Niaki, 2006). GA was developed using MATLAB (version
R 2014a software, USA). The ANN model employed was used
to assess the tness of GA design. At each step, the algorithm
uses the individuals in the current generation to create the next
population and screens the probable occurrence of variation on the
population and accesses the genetic tness of the organisms when
exposed to the optimized conditions of the process (Peng et al.,
2014), using Equation 3 as follows:
YWeight0 21 + e − 1 + hidden layer bias bH (3)
RESULTS AND DISCUSSION
Response surface methodology (RSM)
Experimental design using the CCD of RSM
UV spectrophotometric estimated values of extracted
bacteriocin (mg/l) are discussed in Table 1. The results of the
CCD of RSM experiments studied four independent variables of
bacteriocin production, which are presented in Table 1. Based on
these results, a quadratic polynomial equation was established to
screen the correlation between bacteriocin yield and the studied
process variables (Table 1). The nal equation in terms of coded
factors represents the yield of bacteriocin (Eq. 4) as follows:
Y (mg/l) = + 1.48 + 0.20* A + 0.25* B + 0.17* C + 0.27*
D + 0.19* AB−0.063* AC + 0.13* AD−0.012* BC + 0.17* BD +
0.18* CD−0.21* A^2−0.31* B^2−0.16* C^2−0.23* D^2 (4)
where Y represents the bacteriocin yield (mg/l), A
denotes pH, B represents agitation (rpm), C is the sucrose (g/l),
and D species peptone (g/l). The specied equation is used in
measuring the nal bacteriocin yield. The values of coded factors
were kept between high (+1) and low (−1) levels.
Statistical analysis of RSM optimization studies
The experimental values with respect to predicted values
are compared in Table 1. The high F-value (157.89) denotes that
the employed model was signicant, with only 0.73% chance
for the inuence of noise in the model. The coefcient values
and p-values discussed in Table 1 denote the mutual interaction
between the coefcients. Lesser p-values suggest more impact
of assessed factors on the nal output (Senbagam et al., 2013).
P-values in Table 2 specify that the coefcients of A, B, C, D, (A2),
and (B2), all the quadratic coefcients (A2, B2, C2, D2), and ve of
interaction coefcients, i.e., AB, AD, BC, BD, and CD were found
to be highly signicant. Only AC was reported be non-signicant.
F-value of 2.66 indicates a insignicant impact of lack of t
relative to the pure error of the model (0.013).
The response surface graph studied explains the
interactive effect of independent variables, i.e., pH, agitation,
sucrose, and peptone, on the bacteriocin yield (Fig. 1). Figure 1A
shows the response surface interaction between the variables pH
and agitation (rpm), while keeping the other two variables (sucrose
and peptone) at zero level. The results conrm that the increase
in pH (7.0) and agitation (200 rpm) reportedly increased the
bacteriocin yield to 1.8 mg/l. Figure 1B shows the effect of pH and
peptone on bacteriocin yield, keeping agitation and sucrose at zero
level. The graph shows that the maximum bacteriocin production
(1.8 mg/l) occurred at pH (7.0) and peptone (20 g/l), which agrees
with the model. Figure 1C shows the effect of agitation (rpm)
and sucrose on bacteriocin production, keeping pH and peptone
at zero level. The graph shows that the maximum bacteriocin
production (1.9 mg/l) occurred at agitation (200 rpm) and sucrose
Upendra et al. / Journal of Applied Pharmaceutical Science 11 (10); 2021: 050-060
054
(40 g/l) level. Figure 1D shows the outcome of agitation (rpm) and
peptone on bacteriocin yield, with pH and sucrose at zero level.
The graph explains that maximum bacteriocin yield (1.8 mg/l)
measured at agitation (200 rpm) and peptone (20 g/l). Figure 1E
shows the effect of agitation (rpm) and peptone on bacteriocin
yield, considering pH and agitation at zero level. The graph shows
that the maximum bacteriocin production (1.8 mg/l) occurred at
sucrose (40 g/l) and peptone (20 g/l), which agrees with the model.
The predicted RSM design R2 value (0.9658) was in
close agreement with the measured R2 of 0.9933. This implies
that more than 99.00% of the variation values for bacteriocin
yield were address by the independent variables and the model
does not explain only about less than 1.00% of variations. The
adequate precision value is used to quantify the ratio of signal to
background noise, which is usually greater than 4. The present
ratio of 45.389 indicates that a polynomial-based quadratic model
exhibits adequate signal; hence, the model directs the design space.
The goodness of t values of the RSM design employed indicates
that the experimental output values lie on the 45°, indicating that
the RSM design-predicted values are highly similar and express
close agreement with the experimental data (Fig. 2). Maximum
bacteriocin production was found in the experimental trial 26,
whereas minimum in trial 01. RSM-optimized experimental
results of pH (7.0), agitation (200), sucrose (40 g/l), and peptone
(20 g/l) supported a higher yield (2.4 g/l) of bacteriocin in the SmF
process for the culture of P. pentosaceus Sanna 14 (Table 1).
Confirmation of bacteriocins by ATR FTIR
The FTIR chromatogram of bacteriocin denotes peaks
observed at 1,514.04 and 1,649.10 cm−1 conrms the presence
of amide I and II functional groups, respectively; at 3,567.07, it
indicates the occurrence of the free hydroxyl functional group,
conrming the presence of peptides hence bacteriocin (Fig. 3).
Upendra et al. (2016a) carried out the screening of indigenous
strains of LAB species for their ability to produce bacteriocin and
the produced bacteriocin in the fermentation broth was extracted
as crude and was further puried using ammonium sulfate
precipitation method. Puried bacteriocin was analyzed UV
spectrophotometrically. The samples and the standard exhibited
a peak at 225 nm in the UV spectrophotometer scanning spectra
(200–240 nm) and was further conrmed by SDS-PAGE for
the presence of low molecular weight proteins [SDS, molecular
weight approximately less that 14 kDa (Upendra et al., 2016a)].
Validation by hybrid ANN-GA
Artificial neural network
The comparison of RSM- and ANN-predicted values is
discussed in Table 1; the error of 0.005 indicates that the design
applied was signicant. The simulated value of the bacteriocin
yield, predicted by the feed forward model (3.064 mg/g dry
matter) of ANN, was in close agreement with the experimental
values (3.065 mg/g dry matter) and higher than the predicted value
of CCD of RSM (Table 1).
The study used the optimal architecture feed forward
neural networks of ANN model topology (Fig. 4A), which
possesses three layers of ANN, i.e., input layer consisting of the
RSM design suggested optimized trail value; the hidden layer
(tansig) has 11 neurons; and the output layer (purelin) has a
linearized transfer function. 30 data points (n = 30) were taken to
develop the ANN model, in that 70% data were used for training,
15% for testing, and 15% for validation.
In the present model, the training was completed after
six iterations (epochs), and the study calculated the mean square
Table 2. ANOVA table for the response surface quadratic model.
Source Sum of squares df Mean square F-value p-value prob > F
Model 12.40 14 0.89 157.89 <0.0001 Significant
A-pH 1.13 1 1.13 200.79 <0.0001
B-agitation 2.41 1 2.41 428.91 <0.0001
C-sucrose 2.28 1 2.28 406.63 <0.0001
D-peptone 2.16 1 2.16 384.95 <0.0001
AB 0.30 1 0.30 53.91 <0.0001
AC 0.000 1 0.000 0.000 1.0000
AD 0.063 1 0.063 11.14 0.0045
BC 0.12 1 0.12 21.83 0.0003
BD 0.090 1 0.090 16.04 0.0011
CD 0.72 1 0.72 128.76 <0.0001
A^2 0.94 1 0.94 168.06 <0.0001
B^2 1.07 1 1.07 191.48 <0.0001
C^2 1.07 1 1.07 191.48 <0.0001
D^2 1.36 1 1.36 242.91 <0.0001
Residual 0.084 15 5.611E-003
Lack of fit 0.071 10 7.083E-003 2.66 0.1462 Not significant
Pure error 0.013 5 2.667E-003
Cor total 12.49 29
Upendra et al. / Journal of Applied Pharmaceutical Science 11 (10); 2021: 050-060 055
error value (0.000466888) of the design (Fig. 4B). Furthermore,
a regression-based assessment between ANN design outputs and
the experimental received data was carried out and the results
indicate the accurate prediction. The experimental data used in the
prediction show the correlation coefcient (rr) value of 0.99416
for all data (Fig. 4C) and demonstrate that the established ANN
model is signicant and can be utilized to predict the optimal
topology. The quality of input data was assessed through error
histograms. For the present study, the error reported to be between
0.033 and 0.004 indicates that the employed design model is
highly signicant (Fig. 4D).
Genetic algorithm
The hybrid ANN-GA method was employed to optimize
the input values of four variables studied and validated applying
CCD of RSM and ANN models, respectively, with the aim of
enhancing the nal yield of bacteriocin for the SmF cultures of
P. pentosaceus Sanna 14. The GA program was implemented
in MATLAB (version R 2014a software, USA). The following
expression was utilized to analyze the tness assessment of an
individual (solution) in a population:
j = 1-1 J = 1,2...N
In this equation, εj represents the tness score of the
jth solution and yj
pred denes lovastatin yield predicted by design
model employed in response to the given candidate solution.
The optimum solution for the screened process was
achieved by recapitulating the optimized process conditions
for different GA input variable conditions. GA inputs of the
previous literature reported that the solution must be a global
Figure 1. Three-dimensional graphs representing the linear relationship between the two parameters with respect to bacteriocin yield.
Upendra et al. / Journal of Applied Pharmaceutical Science 11 (10); 2021: 050-060
056
Figure 2. Predicted versus actual values for the RSM design. The graph shows a linear relationship between
predicted and actual values. The optimum bacteriocin yield is 2.4 mg/l.
Figure 3. Attenuated total reflectance-Fourier transform infrared spectroscopy spectra of extracted bacteriocin
(Trail 26).
Upendra et al. / Journal of Applied Pharmaceutical Science 11 (10); 2021: 050-060 057
optimal solution (Verma et al., 2014). The best tness plot
accessed during the analysis after 50 generations explains the
steady progression of the results with respect to the optimal
solution. The sum of mutations declines along with the average
distance measures between individuals, which is nearly 0 for
the nal generation (Fig. 5A). The working model of the GA is
shown in Figure 5B. The GA design assessment stops once the
maximum generation value is attained (50). The maximum time
limit measured in seconds and the results shown in the Figure
5B explain that 100% criteria were met. The selection function
of GA is shown in Figure 5C. Fitness values at each generation
is shown in Figure 5D; the vertical line at individual generations
was smallest to the largest tness value range; tness measures
indicate that the quantity of mutations declines. These plots
represent that the dipping mutation values reduce the diversity
rate of successive generations. GA reported that the optimal set
of factors studied, i.e., pH (7.0), agitation (200 rpm), sucrose (40
g/l), and peptone (20 g/l), were found to inuence the enhanced
yield (2.4 g/l) of bacteriocin. The yield of bacteriocin achieved
during the SmF process conditions was found to exactly match
with the hybrid ANN-GA prediction.
In the present study, the bacteriocin yield was
optimized using biostatistical tools, namely RSM and ANN-GA.
The optimized yield was found to be 2.4 mg/l, which showed
a sixfold increase from the unoptimized bacteriocin yield (0.4
mg/l). The validation was carried out by articial neural network
(MATLAB). The ANN-predicted values and RSM-predicted
values were compared, which showed an error of 0.005, and
the tness criteria of P. pentosaceus Sanna 14 were carried out
using the GA and it was found that the organism is stable for 50
generations. Thirumurugan et al. (2015) optimized Lactobacillus
plantarum using a statistical design, which was reported to be
5.75-fold lesser than the present study. Zhou et al. (2008)
optimized the media composition for Nisin fermentation and
reported a fourfold decrease than the present studies. Zommiti
et al. (2018) extensively investigated the genus Pediococci.
Research group isolated P. pentosaceus MZF16 strain from dried
Ossban a meat products popular in Tunisia and experimented the
growth pattern in different conditions such as pH and bile salts.
Further probiotic inhibition activity of the P. pentosaceus MZF16
on the selected food spoilage and pathogenic bacteria, i.e., L.
monocytogenes, was carried out. Bacteriocin-like compound
which is 100% like coagulin was reported and it was concluded
that the isolated strains of P. pentosaceus MZF16 proved that
pediocins can act as a promising probiotic candidate (Zommiti
et al., 2018).
Figure 4. Bacteriocin optimization process for ANN graphs. (A) Topology of the feed forward design of ANN
with input, hidden, and output layer. The optimum yield obtained using Neuroph was 2.33~2.4. (B) MSE of the
ANN model design measured during the phase of training. (C) ANN regression plots for the SmF bacteriocin
yield and testing network. (D) ANN model Error histogram plots for the bacteriocin yield of the SmF process.
Upendra et al. / Journal of Applied Pharmaceutical Science 11 (10); 2021: 050-060
058
Gutiérrez-Cortés et al. (2018) investigated the
bacteriocins yield abilities in coculture conditions of selected two
LAB species, i.e., P. pentosaceus 147 and L. plantarum LE27.
The study tested the coculture of the selected LAB strains on the
cheese whey-based liquid media. The study reported 51,200 AU/
ml of bacteriocin yield in coculture condition and concluded the
potential of using cocultures of strains of the genera Pediococcus
and Lactobacillus and using alternative substrates such as cheese
whey for the enhanced production of bacteriocins (Gutiérrez-
Cortés et al., 2018).
From the present study, it was concluded that
the optimized condition of the SmF process of the present
investigation, i.e., pH (7.0), agitation (200 rpm), sucrose (40
g/l), and peptone (20 g/l), using P. pentosaceus had shown
maximum yield (2.4 g/l) of bacteriocin. Optimized values of
these parameters were validated by the feed forward model of
ANN and genetic tness of the process was accessed through
GA. The present investigation explored the applications of
biostatical tools in the optimization studies and the optimized
conditions of the present study raised the bacteriocin yield (2.4
g/l) approximately by a 6.0-fold compared with the yield (0.4
mg/l) of unoptimized SmF process conditions.
ACKNOWLEDGMENTS
The authors wish to express their gratitude to the ofcials
and other staff members who rendered their help during the period
of this research work.
CONFLICT OF INTEREST
The authors declare that there are no conicts of interest.
FUNDING
The authors declare that the manuscript was not funded
by any external agency.
PUBLISHER’S NOTE
This journal remains neutral with regard to jurisdictional
claims in published institutional afliation.
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How to cite this article:
Upendra RS, Khandelwal P, Ahmed MR. Bacteriocin
production optimization applying RSM and hybrid (ANN-GA)
method for the indigenous culture of Pediococcus pentosaceus
Sanna 14. J Appl Pharm Sci, 2021; 11(10):050–060.
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Among these, the strain with the best antifungal activity was L. plantarum FT723, which inhibited the mold Penicillium expansum in modified MRS agar (De Man, Rogosa, Sharpe, without acetate) and fermented milk model, whereas no inhibition was observed toward the yeast Yarrowia lipolytica. Proteolytic activity was detected in 205 isolates by agar assay and out of these, 123 more proteolytic isolates were submitted to confirmatory test of activity by SDS-PAGE. Enterococcus faecalis (strains FT132 and FT522) and Lactobacillus paracasei FT700 were confirmed as proteolytic strains by SDS-PAGE and bands indicating digestion of caseins and whey proteins (β-lactoglobulin and α-lactalbumin) were detected. E. faecalis FT132, along with L. paracasei FT700, were selected for further studies on proteolytic activity using milk proteins as substrates in different conditions and sequential analyses by SDS-PAGE and high-performance liquid chromatography (HPLC). Both E. faecalis FT132 and L. paracasei FT700 showed proteolytic activities at pH 6.5, in the range of 37 to 42 °C, due to production of metalloproteases. Next, to evaluate the possible biological activities of the peptides derived from the action of E. faecalis FT132 and L. paracasei FT700 on milk proteins, the supernatant of fermented milk produced by these strains were cleaned-up with a C8 cartridge and freeze-dried. Fermented milk supernatants produced by these strains were added to cell cultures (monocytes and macrophages) to evaluate cytotoxicity, associated death mechanisms, immunomodulatory properties (differentiation of monocytes into macrophages) and TNF-α (Tumor necrosis factor) production. The fermented milk supernatants were toxic to monocytes after 72 h of exposure at 10 mg/mL by apoptosis mechanism. Below cytotoxic concentrations, both cell-free supernatants of fermented milk produced by E. faecalis FT132 and L. paracasei FT700 stimulated the differentiation of monocytes into macrophages (increased expression of CD71 marker). This immune stimulation was not inflammatory since low production of TNF-α was detected. LAB may also present health benefits by acting as probiotics. However, for the selection of probiotics, absence of virulence traits must be proven. Lactobacilli usually have generally recognized as safe (GRAS) status, contrary to enterococci. In this study, it was detected that E. faecalis FT132 harbored three virulence genes asa1, ace and gelE, and that it was resistant to erythromycin and tetracycline, indicating that it was not advisable to be added to food products. However, L. paracasei FT700 would be a potential probiotic candidate, as well as the bacteriocinogenic strain previously described L. paraplantarum FT259. These two strains were tested for probiotic potential by survival in acidified culture media (pH 2.0, 2.5 and 3.5), in vitro tolerance to bile salts, viability in simulated gastric juice and antibiotic susceptibility. In addition, the antimicrobial peptide produced by L. paraplantarum FT259 was partially purified in column filled with XAD-16 resin, followed by a C18 cartridge solid phase extraction, and analyzed by SDS-PAGE. Polymerase chain reactions with primers for plantaricin NC8, plantaricin S and plantaricin W structural genes followed by DNA sequencing of amplicons were carried out to screen for genetic determinants for bacteriocin production. The results showed L. paraplantarum FT259 tolerated exposure to pH 3.5, and bile salts (0.3%) for up to 180 minutes, but population of cells exposed to pH 2.0 and 2.5 for 90 minutes were dropped to less than 2 log CFU/mL (detection limit of the method). In experiments with simulated gastric juice, viable cells of L. paraplantarum FT259 decreased from 8.6 log CFU/mL to 4.4 log CFU/mL after 180 minutes. Otherwise, L. paracasei FT700 survived well in almost all conditions. After 180 minutes in pH 2.0 and simulated gastric juice, bacterial population decreased 4 and 3 log CFU/mL, respectively. It was also demonstrated that L. paraplantarum FT259 and L. paracasei FT700 were susceptible to the majority the antibiotics tested. SDS-PAGE analysis indicated that the partially purified bacteriocin presented a molecular mass around 3,900 Da and by PCR combined with DNA sequencing it was detected the plantaricin NC8 gene. Overall, these results indicated that both strains have potential probiotic traits. Moreover, the production of bacteriocins (L. paraplantarum FT259) and proteolytic activity (L. paracasei FT700) may be interesting in food technology application. In conclusion, the LAB obtained in this study may be useful in the food industry to the production of new dairy products with prolonged shelf-life and increased digestibility, or even for the production of immunomodulatory peptides commercialized as partially purified formulas.
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