Biocatalysis and Agricultural Biotechnology 25 (2020) 101619
Contents lists available at ScienceDirect
Biocatalysis and Agricultural Biotechnology
journal homepage: http://www.elsevier.com/locate/bab
Comparative study on modeling by neural networks and response surface
methodology for better prediction and optimization of fermentation
parameters: Application on thermo-alkaline lipase production by
Nocardiopsis sp. strain NRC/WN5
Mohamed M. Abdel Aziz a, Eman W. Elgammal a, *, Roba G. Ghitas b
a
Chemistry of Natural and Microbial Products Department, Pharmaceutical Industries Research Division, National Research Centre, El Buhouth St., Dokki, 12311, Giza,
Egypt
b
Online NeuroSolver Co., El-Sheikh Zayed City, Giza, Egypt
A R T I C L E I N F O
A B S T R A C T
Keywords:
Neural networks
Response surface methodology
Plackett-Burman
Box-Behnken
Lipase
Nocardiopsis sp
Artificial neural network is a mathematical approach that has been utilized with great success in system design,
modeling, prediction and optimization. Statistical design has been used for long years in improving microbial
fermentations. The current work intended to compare both of the two strategies in optimizing production of
thermo-alkaline lipase by Nocardiopsis sp. strain NRC/WN5. The preliminary investigation showed intensive
effects of fermentation medium type, time course and their interaction on lipase production. In the best medium,
significant effects of carbon source, inorganic and organic nitrogen sources were realized (p ¼ 0.0287, p ¼
0.00076 and p ¼ 0.000015, respectively). Plackett-Burman design showed significant effects of ten variables on
lipase production. The most important factors were sodium chloride, time course and medium pH which
possessed more than 78% of total effect exerted on lipase production. Basing on these three factors, very sig­
nificant model (p ¼ 0.0005) was conducted through Box-Behnken design. To get the best model of neural
network, a comparative study on five different models of neural networks was accomplished. The highly pre­
dictive model was that based on LBFGS optimization function, sigmoid activation function, 150 iterations and
100 neurons in hidden layer. Coupled with genetic algorithm, the predictive model was used to optimize and
predict the variables of optimum fermentation which were experimentally validated in comparison with that
obtained from two-step statistical designs. Practical validation showed superiority of lipase production in
fermentation developed by neural network coupled with genetic algorithm (62.21 � 1.7 U/ml) over that
developed by two-step statistical design (50.11 � 2.6 U/ml).
1. Introduction
To meet the increasing industrial demand for enzymes, new strate­
gies in optimization of enzyme production should be targeted to get the
advantages of increasing the yield as well as getting the privilege of
reducing the production cost. Lipases (EC 3.1.1.3) are hydrolases that
catalyze hydrolysis, esterification and transesterification reactions of
lipids. They have great biotechnological applications in many fields
(Bhosale et al., 2016) such as pharmaceutical industries, medical di­
agnostics, fine chemical synthesis, cosmetics, detergent formulation,
biofuel production, leather processing, processing of food and feed,
acylation reactions, synthesis of esters and separation of racemic mix­
tures (Schmid and Verger, 1998; Sharma et al., 2001; Masomian et al.,
2013; Guerrand, 2017). As such, lipases come in the third order after
proteases and amylases in the total sales volume (Hasan et al., 2006) and
it was predicted that lipase market will reach 590.5 million dollars by
2020 (Bhosale et al., 2016).
Microbial lipases generally are preferred than animal and plant li­
pases due to ease of production, their high activity and stability at broad
range of pH and temperature (Hasan et al., 2006), their broad substrate
specificity and stability in organic solvents (Lescic et al., 2004). More­
over, lipases produced from organisms isolated from extreme habitats,
* Corresponding author. Chemistry of Natural and Microbial Products Department, Pharmaceutical Industries Research Division, National Research Centre, El
Buhouth St., Dokki, 12311, Giza, Egypt.
E-mail address: emanelgammal50@yahoo.com (E.W. Elgammal).
https://doi.org/10.1016/j.bcab.2020.101619
Received 28 February 2020; Received in revised form 16 April 2020; Accepted 19 April 2020
Available online 24 April 2020
1878-8181/© 2020 Elsevier Ltd. All rights reserved.
M.M. Abdel Aziz et al.
Biocatalysis and Agricultural Biotechnology 25 (2020) 101619
like hot springs (Lee et al., 1999) and high salty or sugary environments
(Ghanem et al., 2000), were found to have new unique properties due to
their special stability. The most common microbes in production of li­
pases of diverse industrial applications are Aspergillus sp., Candida sp.,
Rhizopus sp., Yarrowia lipolytica., Humicola sp., and Pseudomonas sp.
(Guerrand, 2017). Although the genus Nocardiopsis is well known
resource of extracellular enzymes (Bennur et al., 2014) and many
valuable metabolites (Bennur et al., 2015), its ability to produce lipases
was recorded in very few reports (Gandhimathi et al., 2009; Chakra­
borty et al., 2015) without any interest in optimization of lipase
production.
Many techniques and strategies were employed in recent decades for
improving microbial fermentation (Kennedy and Krouse, 1999; Singh
et al., 2017). Each of such techniques and strategies has its own ad­
vantages and disadvantages and so employing different optimization
techniques in combination helps greatly to attain the ultimate optimi­
zation (Singh et al., 2017).
Statistical optimization was employed in considerable large number
of literatures (Kennedy and Krouse, 1999; Singh et al., 2017). In most
cases, statistical optimization was achieved through preparatory
screening step using Plackett-Burman design to assess the effect of
different variables with minimum number of runs, and such step should
be followed by subsequent step to get a fine depict about the action and
interactions between the most affecting factors (Kennedy and Krouse,
1999).
Artificial neural network (ANN) is a mathematical approach that has
been utilized with great success for system design, modeling, prediction,
optimization and control due to its ability to learn, filter noisy signals
and generalize information through training procedures (Rajendran and
Thangavelu, 2012). The principle behind ANNs is to mimic the func­
tioning and learning process of a human brain using an artificial neuron
(Sewsynker-Sukai et al., 2017). Its applications in optimization of mi­
crobial fermentation are in continuous enormous growth (Rajendran
and Thangavelu, 2012; Sewsynker-Sukai et al., 2017; Singh et al., 2017).
In addition, many studies employed ANNs, in comparison with statisti­
cal methods, for optimizing production of some enzymes such as
L-asparaginase (Baskar et al., 2011), protease (Dutta et al., 2004) and
lipase (Ebrahimpour et al., 2008). Nevertheless, such studies used single
statistical optimization step based on response surface methodology to
be compared with ANNs. Genetic algorithm (GA) is an algorithm that
mimics the process of mutation and is based upon the principle “survival
of the fittest” (Singh et al., 2017). It is used to solve a variety of opti­
mization problems and so to predict the conditions at which the targeted
variable (response) get its highest value.
Targeting the optimization of lipase production, the current inves­
tigation intended to employ two optimization techniques (response
surface methodology and neural network coupled with genetic algo­
rithm) in a comparative study between their effectiveness in optimiza­
tion of fermentation parameters.
containing 50 ml of fermentation media. Flasks were inoculated with 0.5
ml/flask of spore suspension (containing 106-107 spore/ml) prepared
from five-day old slant of Nocardiopsis sp. strain NRC/WN5. Immediately
after inoculation, 1 ml of sterile olive oil (autoclaved separately) was
added to each flask under sterile condition. Then flasks were incubated
in shaking incubator at 30 � C�2 and 150 rpm. Fermentations was done
in duplicate flasks for each treatment and after separate analysis of each
flask, results were expressed as arithmetic mean � standard error of the
mean.
2.2.1. Fermentation media
Four nutritional media were initially tested for its ability to support
the highest yield of lipase. Composition of these media (in g/l) was as
follow:
Medium I, the basal medium used by Aly et al. (2012), contained:
glucose, 10 and peptone, 30. Medium II, modified from Veerapagu et al.
(2013), contained: peptone, 5; NH4H2PO4, 1; NaCl, 2.5; MgSO4⋅7H2O,
0.4; CaCl2, 0.4; tween 80, 20 drops. Medium III, modified from
Rajeshkumar et al. (2013), contained: peptone, 5; NH4H2PO4, 1; NaCl,
1.25; MgSO4⋅7H2O, 0.2 and CaCl2, 0.2. Medium IV, modified from Sel­
vam and Vishnupriya (2013), contained: starch, 20; peptone, 20; NH4Cl,
3.8; MgSO4, 1.0 and K2HPO4, 5.0. For all media, NaCl in a concentration
of 5% was added to other components and pH was adjusted at 8 before
sterilization. Also, addition of sterile olive oil to each flask (1 ml per
volume inside the flask) was done to act as inducer for lipase production.
2.2.2. Investigation of different carbon and nitrogen sources
Different carbon sources such as glucose, fructose, mannose, galac­
tose, malt extract, and dextrin were added separately as replacements of
starch in a concentration of 20 g/l in medium showed the highest lipase
yield (medium IV). The medium free of starch was used to test the ability
of olive oil (which would be inoculated in concentration of 1 ml per
volume inside the flask) to act as nutritional carbon source beside its role
as an inducer for lipase production.
Ammonium chloride in medium IV was replaced by different inor­
ganic nitrogen sources which were added in a concentration of 3.8 while
organic nitrogen sources were tested as replacements of peptone in a
concentration of 20 g/l. The inorganic nitrogen sources were ammonium
sulphate, ammonium hydrogen phosphate, ammonium molybdate and
potassium nitrate while the organic nitrogen sources included yeast
extract, casein hydrolysate, soybean meal and corn steep liquor.
2.3. Lipase activity assay
The lipolytic activity was determined by spectrophotometric method
using p-nitrophenyl palmitate (p-NPP) as substrate as described by
Chakraborty and Raj (2008) with some modifications. Solution of p-NPP
(10 mM) in isopropanol was prepared and a volume of 20 μl of this
solution was transferred to a test tube containing 300 μl of buffer solu­
tion (50 mm of disodium hydrogen phosphate/sodium hydroxide buffer,
pH 11). To this buffered substrate suspension, sample of crude enzyme
(30 μl) was added and the test tube was then incubated in a water bath at
55 � 2 � C for 30 min under continuous shaking (150 rpm). Excess so­
dium carbonate solution (3 ml, 0.1 M) was added, to ensure sufficient
alkaline medium, and then the contents of each tube were filtered using
nylon syringe filter (0.45 μ) before measuring the absorbance at 400 nm
by Jasco UV-VIS spectrophotometer. For each sample, blank was pre­
pared typically like the sample without adding p-NPP substrate. Read­
ings of standard p-NP concentrations in Na2CO3 solution (0.1 M) was
used to get product concentrations in samples. The coefficient of
extinction (ε) of p-NP under the conditions described was determined as
21 mM 1 cm 1. One unit of enzyme activity was defined as the amount
of enzyme needed to liberate 1 μM min 1 of p-NP under the standard
assay conditions (Mander et al., 2012).
2. Materials and methods
2.1. Microorganism
The microorganism used was previously isolated from the high
salinity and alkalinity environment and characterized, basing on 16S
rRNA gene sequence, as Nocardiopsis sp. strain NRC/WN5 (accession
number MG970558). This strain was kindly provided from the culture
collection at Natural and Microbial Products Chemistry Department,
National Research Centre (NRC), Dokki, Giza, Egypt. Culture was
maintained on starch-casein agar slants (Atlas, 1997) supplemented
with 5% NaCl and pH was adjusted at 8 before autoclaving.
2.2. Fermentation
Fermentation was carried out in Erlenmeyer flasks (250 ml)
2
M.M. Abdel Aziz et al.
Biocatalysis and Agricultural Biotechnology 25 (2020) 101619
incubation. In order to realize the significance of the effects of medium
type and time course, analysis of variance (ANOVA) for the obtained
data was conducted and presented in Table 1. As shown in Table 1, type
of medium and incubation time course were of highly significant role
(p-value 32 � 10 9 and 28.4 � 10 8 respectively), and there was a
significant interaction between them (p-value 27.6 � 10 6) on lipase
production. Increased lipase yield in medium I and IV justified a sug­
gestion that the presence of carbohydrates as glucose in medium I and
starch in medium IV supported the enzyme production and they were
co-metabolized in synergism with lipase production (Arora et al., 2015).
Also, the results in Fig. 1 showed that after 5 days of incubation, the
production rate decreased which may be attributed to the consumption
of the nutrients, change in medium pH or production of proteases that
hydrolyze lipase (Nouroozi et al., 2015).
2.4. Statistical design and modeling
Statistical designs (Plackett-Burman and Box-Behnken), analysis of
variance (ANOVA) of data, regression analysis to get polynomial co­
efficients and equations, three-dimensional response surface plots and
predictions of the optimum levels of variables were achieved using the
“Design Expert‟ software (Version 7.0.0, Stat-Ease Inc., Minneapolis,
USA) statistical package.
2.5. Neural network modeling
Artificial neural networks was constructed using MATLAB software
version 7.1.
2.6. Statistical calculations
3.2. Effect of different carbon sources on productivity of lipase
Calculations of standard deviation, ANOVA single factor and two
factors at confidence level of 95% were conducted through Microsoft
Office Excel 2007. The standard error is an indicator of how close the
sample mean is to the population mean whereas the standard deviation
is a measure of how widely values are dispersed from the average value.
The standard error of the mean was calculated from standard deviation
according to Lee et al. (2015) using the following equation:
The most critical factor which affects the microbial enzyme pro­
duction is the carbon source. So, starch in medium number IV was
replaced by different carbohydrates such as glucose, fructose, mannose,
galactose, malt extract and dextrin. A medium devoid of carbohydrates
was applied to test the ability of olive oil (2%) to act as a carbon source
for microbial growth beside its role as inducer for lipase. The results
depicted in Fig. 2 revealed that lipase production is dependent on the
type of sugar as recorded previously in elsewhere (Arora et al., 2015).
ANOVA analysis (inside the frame in Fig. 2) helped to ensure that
variation in lipase concentration between different carbon sources is
significant comparing with variation between replicates. At p-value of
0.0287 which is smaller than 0.05 and with calculated F-value of 4.308
which is greater than F-critical value (F crit ¼ 3.5), there was a signifi­
cant change in lipase concentration with the type of carbon source.
Dextrin was the best carbon source (afforded 36.6 � 3.6 U/ml) between
other tested sources. However, it could not potentiate any increase in
lipase concentration than that in case of starch in control (36.8 � 3.1
U/ml). Other easily utilized sugars and olive oil (as a carbon source)
were found to have an inhibitory effect on the production (http://www.
ejbiotechnology.info/index.php/ejbiotechnology/article/view/v14n4
-8/1334 Lee et al., 1999). In this context, Bapiraju et al. (2005) obtained
similar results with Rhizopus sp. BTNT-2 using potato starch (33.9
U/ml). Also, Espinosa et al. (1990) reported that dextrin and starch were
the best carbohydrate for maximum lipase production. However, lower
yields were observed by Candida rugosa using starch (Dalmau et al.,
2000). On the other hand, Osman et al. (2012) found that the synergistic
effect of glucose, sucrose and fructose with oil substrate inhibited the
production of lipase.
Standard error of the mean ¼ SD / √n
Where SD is the standard deviation and n is the sample size.
3. Results and discussion
3.1. Lipolytic activity of Nocardiopsis sp. strain NRC/WN5 in different
fermentation media
Generally, lipase production in most cases is dependent on the
presence of an inducer in the fermentation medium (Lotti et al., 1998).
From the previous reports, it was observed that variations in culture
media have different effects on the enzyme productivity and that effects
is dependent on the type of microorganism (http://www.ejbiotechnolo
gy.info/index.php/ejbiotechnology/article/view/v14n4-8/1334
He
and Tan, 2006) and the incubation time (Soleymani et al., 2017). To test
lipase production, four different media containing olive oil as inducer
were used. Fig. 1 showed that the maximum yield (35.9 � 4.0 U/mL)
was obtained by applying medium IV and the next favorable one was
medium I giving enzyme activity reached 28.3 � 2.1U/ml after 5 days of
3.3. Effect of different inorganic and organic nitrogen sources on lipase
production by Nocardiopsis strain NRC/WN5
Although starch and dextrin afforded nearly the same amount of
lipase, superiority of starch over dextrin on economic base, availability
and ease of extraction from many natural sources clearly justified the
choose of starch as the carbon source in subsequent studies aiming at
investigating the effect of nitrogen source. In many cases, the combi­
nation of organic and inorganic nitrogenous forms is preferred for some
microorganisms (Dong et al., 1999). To investigate the role of nitrogen
Table 1
Two-factor ANOVA analysis for lipase production by Nocardiopsis strain NRC/
WN5 in different media at different time courses.
ANOVA analysis at 0.05 significance level
Fig. 1. Production of lipase by Nocardiopsis strain NRC/WN5 in different
fermentation media. Bars in the figure represent the standard error of the mean
in replicates.
3
Source of Variation
F
p-value
F crit
Media
Time course
Interaction
47.92509
35.15399
10.88468
0.000000032
0.000000284
0.000027657
3.238872
3.238872
2.537667
M.M. Abdel Aziz et al.
Biocatalysis and Agricultural Biotechnology 25 (2020) 101619
Table 2
Codes and levels of the factors studied by Plackett-Burman design.
Factor
code
Name (Units)
Low
level
( 1)
Mean
level (0)
High
level
(þ1)
Std.
Dev.
A
B
C
Starch conc. (g/l)
Peptone conc. (g/l)
Amm. molybdateconc.
(g/l)
Magnesium sulphate
conc. (g/l)
Dipotassium hydrogen
phosphate conc. (g/l)
Sodium chloride conc.
(%)
Medium pH
Oil conc. (%)
Tween 80 conc. (g/l)
Inoculum size (%)
Time course (day)
10.00
4.00
1.00
15.000
12.000
2.400
20.00
20.00
3.80
4.804
7.686
1.345
0.20
0.600
1.00
0.384
1.00
3.000
5.00
1.922
0.000
2.500
5.00
2.402
8.00
1.00
0.000
1.00
4.00
9.000
2.000
1.000
2.000
5.000
10.00
3.00
2.00
3.00
6.00
0.961
0.961
0.961
0.961
0.961
D
E
F
Fig. 2. Effect of different carbon sources on lipase production by Nocardiopsis
strain NRC/WN5. Bars in the figure represent the standard error of the mean in
replicates. ANOVA analysis of data was indicated inside the frame.
G
H
J
K
L
sources in optimization of lipase production; different inorganic and
organic nitrogen sources were added as replacements of ammonium
chloride and peptone respectively to other components of medium IV.
Fig. 3A and B, with ANOVA analysis data in frames, ascertained an
intrinsic change in lipase concentration by variation in type of nitrogen
source. The results showed that the best lipase activities (42.4 � 3.3
U/mL and 40.9 � 1.8 U/mL) were observed by ammonium molybdate
and peptone as inorganic and organic nitrogen sources respectively.
Many previous studies reported that peptone is considered as an inducer
for lipase production from different bacteria due to its release of NH4þ
ions into the medium which stimulates the microbial growth and
consequently enhances the enzyme production (Song et al., 2001).
Concerning the inorganic nitrogen source of ammonium molybdate,
Bayoumi et al. (2007) reported that it yielded the highest productivity of
lipase by G. stearothermophilus B-78. On the other hand, ammonium
hydrogen phosphate, casein hydrolysate and soy bean meal showed a
negative effect on the enzyme production. Similarly, Bora and Bora
(2012) found the same result with Bacillus sp. using soy bean meal.
3.4. Modulating the fermentation parameters of lipase production via two
successive statistical steps
3.4.1. Plackett-Burman design
Medium IV after being improved with the most suited carbon and
nitrogen nutrition was then directed to further optimization through
statistical design. Plackett-Burman design was conducted to study the
effect of eleven variables on lipase production by Nocardiopsis strain
NRC/WN5. The eleven variables were coded and screened at high (þ1)
and low ( 1) levels and one central point (0) as indicated in Table 2. The
design consisting of 13 runs with corresponding lipase yield was shown
in Table 3. Analysis of variance (ANOVA) of the response (lipase pro­
duction) in relation to the tested variables was shown in Table 3 and it
revealed very significant model (p ¼ 0.0019) having an F-value of
169416.74 which implies that the model is significant. There is only a
0.19% chance that a “Model F-Value” could occur due to noise. Ac­
cording to outputs shown in Table 4, ten variables (coded as A, B, C, D, E,
F, G, H, J, L) are of significant effect (“Prob > F00 was less than 0.05). The
effects of the tested variables were depicted in Fig. 4. The highest effect
was shown by sodium chloride concentration (standardized effect of
19.1) followed by time course (10.0) and the third highest effector was
the medium pH which was of negative effect ( 8.1) inside studied range
of pH from 8 to 10. The significant role of sodium chloride concentration
and medium pH on lipase production by Nocardiopsis strain NRC/WN5
inferred the inherited characteristics of such strain which had been
isolated from haloalkaline environment (Purohit et al., 2014). The
percent contribution of the different tested factors in total effect on
lipase production (Fig. 5) showed that sodium chloride possessed more
than 53% of effect on lipase production, and the three highest factors
(sodium chloride, time course and medium pH) had more than 78% of
total effect exerted on lipase production by Nocardiopsis sp. As such, the
three factors were chosen for second statistical step of Box-Behnken
design to realize their effects and interactions between them.
3.4.2. Box-Behnken design and response surface methodology
Box-Behnken design is three-level fractional factorial design (Ken­
nedy and Krouse, 1999) and was employed here to improve the effect of
most effective variables through modulating the interaction between
them (Venkateswarulu et al., 2017). The design was conducted at three
levels of each of the studied factors NaCl concentration, medium pH and
incubation time (coded by A, B and C respectively) and constituted of 15
runs with three center points. The design with corresponding lipase
concentration for each run, as shown in Table 5, was analyzed by
ANOVA and very significant quadratic model was obtained (Table 6).
Fig. 3. Effect of different inorganic (A) and organic (B) nitrogen sources on
lipase production by Nocardiopsis strain NRC/WN5. Bars in the figure represent
the standard error of the mean in replicates. ANOVA analysis of data was
indicated in frame inside each plot.
4
M.M. Abdel Aziz et al.
Biocatalysis and Agricultural Biotechnology 25 (2020) 101619
Table 3
Plackett-Burman experimental design for eleven factors affecting lipase production by Nocardiopsis strain NRC/WN5.
Run
Levels of different factors
A
1
2
3
4
5
6
7
8
9
10
11
12
13
1
1
1
0
1
1
1
B
1
1
1
1
1
1
1
1
1
0
1
1
1
Lipase production (U/ml)
C
1
1
1
1
1
1
1
1
1
1
0
1
1
D
1
1
1
1
1
1
1
1
0
1
1
1
1
E
1
1
1
1
1
1
1
1
1
0
1
1
1
F
1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
1
G
H
1
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
J
1
1
1
1
1
1
1
1
0
1
1
1
1
K
1
1
1
1
1
1
1
1
1
0
1
1
1
L
1
1
1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
1
1
7.45
0.00
14.17
13.76
19.15
47.40
19.59
20.24
24.12
0.56
13.99
1.36
28.28
Table 4
Statistical analysis of Plackett-Burman model.
Source
Sum of Squares
df
Mean Square
F Value
p-value Prob > F
Model
A-Starch conc.
B-Peptone conc.
C-Amm. molybdate conc.
D-MgSO4 conc.
E-K2HPO4 conc.
F–NaCl conc.
G-Medium pH
H-Oil conc.
J-Tween 80 conc.
L-Time course
2019.36
114.77
13.88
55.48
9.59
79.54
1097.71
197.71
59.90
88.08
302.73
10
1
1
1
1
1
1
1
1
1
1
201.94
114.77
13.88
55.48
9.59
79.54
1097.71
197.71
59.90
88.08
302.73
169400
96284.19
11643.92
46544.17
8045.26
66730.55
9.209Eþ005
1.659Eþ005
50251.39
73892.43
2.540Eþ005
0.0019
0.0021
0.0059
0.0030
0.0071
0.0025
0.0007
0.0016
0.0028
0.0023
0.0013
significant
Fig. 4. Effect of different tested factors on lipase production by Nocardiopsis strain NRC/WN5.
The model has p-value of 0.0005 and F-value of 36.95 which indicated
that the model is significant. There is only a 0.05% chance that a model
F-value could occur due to noise. The model showed that the terms A, C,
BC and C2were significant (values of “Prob > F00 were less than 0.05).
Significant interaction between factors B and C was observed (p ¼
0.0063). The lack of fit F-value of 6.63 implies that the lack of fit is not
significant relative to the pure error. Non-significant lack of fit indicates
that the model is valid to navigate the design space. Optimization
through response surface methodology (RSM) was then constructed
basing on the data of Box-Behnken design. RSM uses the data obtained to
develop a mathematical equation describes the model and predicts the
response from studied factors (Panda et al., 2007) in a three-dimensional
(surface) plot of the response with combination of each two of the
studied factors. The final equation describing the effect of the three
factors on lipase production is the following quadratic equation:
Lipase concentration ¼ 474.01754 þ 0.30561A - 1.87736B - 117.28678C þ
0.35272AB - 0.040715AC - 9.39407BC - 0.28157A2 þ 4.25190B2 þ
12.81652C2
The three-dimensional surface plot for the response (lipase produc­
tion) in relative to the combination of two of the studied factors was
depicted in Fig. 6 as shown below.
3.5. Modulating the fermentation parameters of lipase production by
Nocardiopsis strain NRC/WN5 using artificial neural network approach
Artificial neural networks are aids used to relate data of input vari­
ables (data of parameters assumed to affect certain response) to output
5
M.M. Abdel Aziz et al.
Biocatalysis and Agricultural Biotechnology 25 (2020) 101619
Fig. 5. Percent contribution of different tested factors on lipase production by Nocardiopsis strain NRC/WN5.
into sets (A, B, C, D and E) as specified in Table 7. The different sets of
parameters were then used to conduct the different neural network
models. Neural network algorithm was trained on data of the thirteen
different runs used in Plackett-Burman design (Table 3) and consisted of
three layers (input, output and one hidden layer between them).
Fig. 7 showed the relation between actual and predicted values of
lipase concentration for neural networks models constructed by the
different parameter sets. In each plot in Fig. 7, the straight line inter­
secting the origin point represents the line of zero absolute error, so the
distribution of points more close to such line reveals better predictability
of the model. It was clearly observed that the models conducted by sets
A, D and E had good predictability comparing with those of sets B and C.
Change in optimization function from LBFGS in set A to Adam in set B
resulted in dramatic decrease in predictability which strongly pointed
out to the sensitivity of the model to optimization function and also
reflected the special superiority of LBFGS function in prediction models
of the current biological system. The mean absolute error between
actual and predicted values of the different models presents a measuring
aid for accuracy of prediction. Comparison between the mean absolute
errors of different models was depicted in Fig. 8. The results in Fig. 8
showed that mean absolute error in set B model (12.23 U/ml) is 233
times greater than that of set A model (0.05 U/ml) which reflected the
ability to attain tremendous improve in prediction by changing the
optimization function from Adam to LBFGS. All neural networks con­
structed by LBFGS optimization function were of obvious reduced mean
absolute errors which have not exceed 0.1 U/ml as in models of sets A, D
and E (mean absolute errors were 0.08, 0.06 and 0.01 U/ml respec­
tively). Although sigmoid activation function is used commonly in back
propagation algorithm (Rajendran and Thangavelu, 2012), tangent hy­
perbolic (Tanh) function showed better prediction in the current
investigation according to the drop in mean absolute error from 0.05 to
Table 5
Box-Behnken design for studying the most important factors affecting lipase
production by Nocardiopsis strain NRC/WN5.
Run
Factor A, Sodium
chloride conc. (%)
Factor B,
Medium pH
Factor C, Time
course (day)
Lipase
production (U/
ml)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
5
5
5
7.5
5
7.5
7.5
10
7.5
7.5
10
10
7.5
7.5
10
7
8
7.5
7.5
7.5
7.5
8
7.5
8
7
8
7
7.5
7
7.5
7
7
8
7
6
7
6
6
8
8
7
7
7
6
8
22.7
18.2
24.3
18.2
39.4
16.3
45.9
32.2
17.3
25.5
10.9
13.7
16.7
35.2
16.7
data (data of response) through a process mimics the brain in training
and learning. While the data of input are arranged in input layer and
data of outputs are represented in the output layer, there are several
(one or more) hidden layers that exist between the input and output
parameters (Nagata and Chu, 2003; Sewsynker-Sukai et al., 2017). The
predictive potential of artificial neural networks is profoundly affected
by the parameters applied like type of optimization function, number of
iterations, type of activation function and number of neurons in hidden
layer. As such, neural network was conducted by several variations in
the affecting parameters. Variants of different parameters were grouped
Table 6
ANOVA for response surface quadratic model of Box-Behnken design.
Source
Sum of squares
df
Mean square
F value
P value Prob > F
Model
A-NaCl conc.
B-Medium pH
C-Time course
AB
AC
BC
A2
B2
C2
Residual
Lack of Fit
Pure Error
1442.04
121.29
2.94
593.95
0.78
0.041
88.25
11.43
4.17
606.51
21.68
19.70
1.98
9
1
1
1
1
1
1
1
1
1
5
3
2
160.23
121.29
2.94
593.95
0.78
0.041
88.25
11.43
4.17
606.51
4.34
6.57
0.99
36.95
27.98
0.68
136.99
0.18
0.009558
20.35
2.64
0.96
139.89
0.0005
0.0032
0.4479
<0.0001
0.6895
0.9259
0.0063
0.1653
0.3717
<0.0001
significant
6.63
0.1338
not significant
6
M.M. Abdel Aziz et al.
Biocatalysis and Agricultural Biotechnology 25 (2020) 101619
Fig. 6. The three-dimensional surface plot for the response (lipase production) in relative to the combination of two of the studied factors.
predictive model conducted by Baskar et al. (2011) which based on 787,
950 iterations. The current prediction model basing on parameters set E,
which had the lowest mean absolute error (0.01 U/ml), was coupled
with genetic algorithm to predict the parameters of optimum fermen­
tation and the maximum lipase concentration attained with such
parameters.
Table 7
Specifications of different sets of parameters used in modeling by neural
network.
Parameter
Different sets of parameters
Set A
Set B
Set C
Set D
Set E
Optimization
function
Number of
iterations
Activation
function
LBFGS
Adam
Adam
LBFGS
LBFGS
150
150
300
150
150
Sigmoid
Sigmoid
Sigmoid
Sigmoid
Number of
neurons in
hidden layer
50
50
50
Tangent
hyperbolic
(Tanh)
50
3.6. Prediction and practical validation of optimum fermentation
parameters for thermo-alkaline lipase production by Nocardiopsis strain
NRC/WN5
After two successive shots of statistical designs (Plackett-Burman and
Box-Behnken designs), the optimum fermentation parameters and the
predicted yield of lipase were inferred as listed in Table 8. On the other
side, the single shot of neural network modeling (basing on parameters
set E) coupled with genetic algorithm was employed to find out the
optimum fermentation parameters and the predicted lipase yield which
were listed as shown in Table 8. Each of the two proposed optimum
fermentation parameters were practically verified. It was found that
optimum fermentation developed by neural networks coupled with ge­
netic algorithm afforded the highest lipase concentration (62.21 � 1.7
U/ml) comparing with that developed by statistical designs (50.11 � 2.6
U/ml). The medium developed by neural networks pointed out to the
ability of Nocardiopsis sp. NRC/WN5 to produce lipase in higher yield in
presence of tween-80 as the only inducer for enzyme. In the previous
study of Boekema et al. (2007), it was stated that tween-80 contains a
fatty acyl ester bond and so could induce (or potentiate) lipase pro­
duction when applied in absence (or presence) of oil through the
probable mechanism of inducing lipase gene expression. The ability of
neural networks coupled with genetic algorithm to find out the
100
0.04 U/ml by changing activation function from sigmoid to Tanh in
models of sets A and D. Increase in number of neurons in hidden layer
was also of a significance in getting better predictions and low mean
absolute error; number of neurons of 100 realized 5.2 times lower mean
absolute error than 50 neurons which was inferred from mean absolute
errors of sets A and E models. As such, it was concluded that the most
predictive parameters in neural network modeling, in terms of the cur­
rent studied biological system, were LBFGS optimization function, Tanh
activation function and hidden layer of 100 neurons. Number of itera­
tions was also found to have an effect on prediction; increasing the
number of iterations from 150 to 300 led to drop in mean absolute error
from 12.23 U/ml in set B model to 10.46 U/ml in set C model. Inter­
estingly, the current highly predictive model (set E model) was attained
using very small number of iterations (150) comparing with the
7
M.M. Abdel Aziz et al.
Biocatalysis and Agricultural Biotechnology 25 (2020) 101619
Fig. 7. Models of neural networks conducted by different sets of parameters. Different models were represented by relation between actual and predicted values of
lipase concentration. The straight line intersecting the origin point represents the line of zero absolute error.
fermentation of highest lipase yield from single shot of practical work
consisted from only thirteen runs and covered 11 variables represents
the extra valuable advantage. In terms of the current results, it was
concluded that neural network (run in parameters set E) improved the
microbial fermentation to the highest levels of performance with lowest
practical effort.
4. Conclusion
The artificial neural networks coupled with genetic algorithm were
applied to optimize and predict the optimum variables for highest pro­
ductivity of thermo-alkaline lipase from Nocardiopsis strain NRC/WN5
in comparison with that obtained from two-step statistical designs. The
study conducted different ANN models and specified the most predictive
one which represented a fabulous aid in improving microbial fermen­
tation to the highest levels of performance with lowest practical effort.
Therefore, the current work could help in industrial scale-up of the
enzyme production.
Fig. 8. Mean absolute errors of neural network models constructed by different
sets of parameters.
Table 8
Optimum fermentation parameters, predicted lipase concentration and practical
validations of fermentations conducted according to statistical design and neural
network.
Fermentation parameters
Starch conc. (g/l)
Peptone conc. (g/l)
Ammoniummolybdate conc. (g/l)
MgSO4 conc. (g/l)
K2HPO4 conc. (g/l)
Olive oil conc. (%)
Tween 80 conc. (g/l)
Inoculum size (%)
NaCl conc. (%)
Medium pH
Time course (day)
Predicted lipase concentration (U/ml)
Statistical design
Neural network
20
15.67
1
1
1.06
2.83
2
1
5.08
8
6
15
15
1
1
1
0
4
1
5
8
6
47.15
Practical validation of lipase concentration (U/ml)
50.11 � 2.6
Financial support and sponsorship
Nil.
Declaration of competing interest
The authors declare that there are no conflicts of interest.
CRediT authorship contribution statement
Mohamed M. Abdel Aziz: Conceptualization, Data curation, Formal
analysis, Methodology, Resources, Software, Writing - original draft,
Writing - review & editing. Eman W. Elgammal: Conceptualization,
Formal analysis, Methodology, Resources, Writing - original draft,
Writing - review & editing. Roba G. Ghitas: Conceptualization, Data
curation, Software.
66.92
62.21 � 1.7
8
M.M. Abdel Aziz et al.
Biocatalysis and Agricultural Biotechnology 25 (2020) 101619
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9