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Food Sci. Biotechnol. 23(6): 1911-1919 (2014)
DOI 10.1007/s10068-014-0261-7
Optimization of Succinic Acid Production from Cane Molasses by
Actinobacillus succinogenes GXAS137 Using Response Surface
Methodology (RSM)
Naikun Shen, Qingyan Wang, Yan Qin, Jin Zhu, Qixi Zhu, Huizhi Mi, Yutuo Wei, and Ribo Huang
Received November 4, 2013; revised May 27, 2014; accepted May 27, 2014; published online December 31, 2014
© KoSFoST and Springer 2014
Abstract A method combining a Plackett-Burman design
(PBD), the steepest ascent method (SA), and a Box-
Behnken design (BBD) was developed to optimize succinic
acid production from cane molasses by Actinobacillus
succinogenes GXAS137. The important parameters were
(g/L): total sugars of cane molasses (85 g/L), yeast extract
(8.84 g/L), and MgCO3 (63.1 g/L). Verification experiments
indicated that the maximal succinic acid production
reached 57.43±0.86 g/L, which agreed with the predicted
value (57.12 g/L). In addition, batch and fed-batch
fermentations were carried out in a 1.3 L stirred bioreactor.
Compared with a batch fermentation that produced 57.96
g/L of succinic acid at 60 h, a fed-batch fermentation,
performed to minimize the inhibition effect of the substrate,
produced 64.34 g/L of succinic acid at 60 h. The combined
method is powerful for selection of optimized conditions
for succinic acid production from cane molasses.
Keywords: succinic acid, cane molasses, Plackett-Burman
design, response surface methodology, fermentation
Introduction
Succinic acid is a member of the 4-carbon dicarboxylic
acid family and has a wide range of applications in the
fields of surfactants, green solvents, and pharmaceutical
intermediates as an important 4-carbon intermediate
compound (1). Succinic acid has been conventionally
produced from fossil raw materials for more than a century
(2). Due to depletion of fossil resources and a strong
demand for environmentally friendly energy sources,
biological production of succinic acid has attracted great
interest (3).
Succinic acid can be produced via mircrobial fermentation
using Actinobacillus succinogenes (4,5), Mannheimaia
succiniciproducens (6), Anaerobiospirillum succiniciproducens
(7), and recombinant Escherichia coli (8). A. succinogenes,
a member of the Pasteurellaceae family, is one of the most
promising succinic acid producers due to an ability to
produce high concentrations of succinic acid naturally from
a broad range of carbon sources (4,9). The fermentation
cost of bio-based succinic acid is, however, a key factor
when competing with petroleum-based succinic acid
production. To obtain a high production of succinic acid,
synthetic fermentation media compositions are often
complicated by addition of a high concentration of glucose
and 10 kinds of vitamins (10,11), leading to increased
expense and a lack of facility for economic bulk succinic
acid production. Therefore, use of cheap carbon sources
rather than glucose is important for economical production
of succinic acid. Use of the natural substrates of starch (12)
and cellulose (13) is economically unfavorable because of
a requirement for pretreatment in order to release fermentable
sugars. Furthermore, A. succinogenes is a fastidious organism
(14) and high concentrations of complex growth supplements,
mainly yeast extract, are required for use of renewable
Naikun Shen, Qingyan Wang, Yan Qin, Yutuo Wei, Ribo Huang (�)Guangxi Key Laboratory of Subtropical Bio-resource Conservation andUtilization, College of Life Science and Technology, Guangxi University,Nanning, Guangxi 530005, ChinaTel: +86-0771-2503902; Fax: +86-0771-2503908E-mail: [email protected]
Naikun Shen, Qingyan Wang, Yan Qin, Jin Zhu, Qixi Zhu, Huizhi Mi,Ribo HuangGuangxi Academy of Sciences, State Key Laboratory of Non-foodBiomass and Enzyme Technology, National Engineering Research Centerfor Non-food Biorefinery, Guangxi Biomass Industrialization EngineeringInstitute, Guangxi Key Laboratory of Biorefinery, Nanning, Guangxi530007, China
RESEARCH ARTICLE
1912 Shen et al.
resources.
Molasses is a byproduct of the sugar industry that is
readily available at a relatively low cost. Molasses contains
water, sugars, nitrogen compounds, organic acids, amino
acids, and heavy metals (15). According to previous reports,
the highest concentration of succinic acid was only 46.4 g/
L when cane molasses was used as a carbon source (16).
Cane molasses has been used for production of lactic acid
(17), ethanol (18), and citric acid (19). Therefore, it is
desirable to develop an effective method for succinic acid
production using cane molasses.
There are no reports on optimization of succinic acid
production from cane molasses using Actinobacillus
succinogenes. Response surface methodology (RSM) has
been successfully applied for optimization of bioprocess
(20). Compared with single variable methods, RSM is a
powerful mathematical model based on statistical techniques
in which interactions between multiple process variables
can be identified using fewer experimental trials. In the
present work, an optimization method is proposed including
steps of (a) use of a one-factor-at-a-time approach to
determine the critical components of a medium, (b) use of
a 2-level Plackeet-Burman design (PBD) to identify important
parameters and experimental levels for further optimization,
(c) application of the path of steepest ascent (SA) approach
to the biggest region of succinic acid production, and (d)
use of a Box-Behnken design (BBD) to develop mathematical
models for estimation of relationships between responses
obtained using the optimum values of parameters.
Materials and Methods
Chemicals and materials All chemicals used were
obtained from either Sinochem (Shanghai, China) or OXOID
(Hampshire, UK), except where otherwise specified.
Microorganism and growth conditions The GXAS137
strain of Actinobacillus succinogenes isolated from the
bovine rumen was obtained from the China Center for
Type Culture Collection (No. CCTCC M 2011399) and
was maintained in 20% glycerol at −80oC. Cells were
grown in 100 mL sealed anaerobic bottles containing 50
mL of medium. The medium for inoculum cultures was
composed of glucose (20 g/L), yeast extract (5.0 g/L), corn
steep liquid (dry power) (2.5 g/L), NaH2PO4·H2O (9.6 g/
L), K2HPO4 (15.5 g/L), and NaHCO3 (10 g/L). All liquid
cultures were incubated in a rotary shaker (New Brunswick
Scientific Co., Edison, NJ, USA) at 37oC and 100 rpm.
Treatment of cane molasses The molasses used in this
study was obtained from a local beet sugar refinery in
Guangxi, China. Molasses contained sucrose (370 g/L),
water (250 g/L), converted sugars (glucose and fructose)
(100 g/L), other carbohydrates (26 g/L), crude proteins
(41 g/L), crude fats (0.8 g/L), ash (90 g/L), salt (49 g/L),
and metal ions (83 g/L). Crude molasses was diluted using
distilled water to obtain a total sugar concentration of 250
g/L. For sulfuric acid treatment, the molasses solution was
adjusted to pH 3.5 using 5 M H2SO4, and heated at 60oC
for 2 h. After centrifugation at 8,000×g in an Avanti J-26
XP centrifuge (Beckman Coulter Inc., Fullerton, CA, USA)
for 15 min, the supernatant was adjusted to pH 7.0 using 10
M NaOH (16).
Fermentation in anaerobic bottles For anaerobic bottle
fermentation, exponentially growing cells were inoculated
into 250 mL sealed anaerobic bottles with 100 mL of
medium containing yeast extract (2.5 g/L) and a salt
mixture including K2HPO4 (3.0 g/L), NaH2PO4·H2O
(2.0 g/L), MgCl2·6H2O (0.2 g/L), and NaCl (1.0 g/L). The
pH of the medium was maintained by addition of 40 g of
MgCO3. Separately autoclaved cane molasses was added
aseptically to the medium to make up the desired sugar
concentration (70 g/L). The sterile medium was sparged
with CO2, and Na2S·9H2O (a final concentration of 0.2 g/L)
was added before inoculation to ensure strictly anaerobic
conditions. For fermentation, the medium was inoculated
with a 5% seed culture.
Fermentation in stirred bioreactors Batch fermentation
was carried out in a 1.3 L fermenter (Eppendorf BioFlo/
CelliGen 115, Hamburg, Germany) with an initial broth
volume of 0.8 L. All fermentations were performed at 37oC
at an agitation speed of 200 rpm and a CO2 flow rate of 0.3
L/min. The fermentation medium was the same as the
medium in the anaerobic bottles and the pH was controlled
using MgCO3 during the fermentation process. Samples
were taken every 4 h during the entire fermentation cycle,
which was terminated after 60 h. Fed-batch fermentation
was carried out under the same conditions and the same
total sugar concentrations as for batch fermentations. When
the concentration of total sugars was lower than 15 g/L, a
concentrated molasses solution containing 300 g/L of total
sugar was fed into the stirred bioreactor using a peristaltic
pump to maintain the sugar concentration within 10-15 g/
L during the course of fermentation.
Experimental design
One-factor-at-a-time design: Before RSM was applied,
the approximate medium composition for cultivation of
A.succinogenes was determined by varying one factor at a
time while keeping all other factors constant. The medium
components (carbon, nitrogen, and metal ions) were selected
for maximum succinic acid production while the temperature,
pH, and fermentation time were examined as conditions for
Succinic Acid Production 1913
the fermentation. The nutritional factors were optimized by
changing one factor at a time and keeping other variables
constant.
Plackett-Burman design (PBD): The methodology of
PBD is a powerful and useful tool for rapid searching for
key factors in a multivariable system. This design does not
consider the interaction effects among the variables. PBD
does not determine exact quantities, but can provide
important information about each factor based on relatively
few experiments (21). The design is based on the first order
model:
Y=β0+βixi (1)
where Y is the predicted response, β0 and βi are constant
coefficients, and xi is a coded independent factor. The
purpose of using PBD was to identify the key medium
components for succinic acid production.
Steepest ascent (SA) design: This method was used to
move rapidly towards the neighborhood of the optimum
response (11). Experiments were designed to determine a
suitable direction by increasing or decreasing the
concentrations of variables based on PBD results. The
optimum point in the optimal range was used as the center
point for optimization using a central composite design.
Box-Behnken design (BBD): BBDs are response surface
designs especially constructed to require only 3 levels,
coded as 1, 0, and +1. The levels are formed by combining
2-level factorial designs with incomplete block designs
(22). The response surface analysis is based on multiple
linear regressions that take into account the main,
quadratic, and interaction effects in accordance with the
following equation:
(2)
; i, j =1, 2, 3...,
where Y is the predicted response, β0 is an offset term, βi
is the liner effect, βii is the quadratic effect, βij is the
interaction effect, and ε is experimental error. The variables
xi and xj represent the independent variables (medium
components) in the form of coded values as follows:
i=1, 2, 3, (3)
where xi and Xi are the dimensionless and the actual values
of the independent variable i, X is the actual value of the
independent variable i at the central point, and ∆Xi is the
step change of Xi corresponding to a unit variation of the
dimensionless value.
Methods of analysis Bacterial growth was determined
using optical density (OD) measurements at 660 nm (DU
800 UV/VIS Spectrophotometer; Beckman, CA, USA).
Cultures were diluted using 0.2 M HCl, then centrifuged at
10,000×g for 10 min in order to ensure that none of the
MgCO3 remained undissolved (23). Cell pellets were
washed 3 times using distilled water to remove all medium
components.
Concentrations of organic acids and sugars were
determined using HPLC. The culture broth was centrifuged
at 10,000×g for 10 min, filtered, and 10 µL of each test
sample was subjected to HPLC (Ultimate 3000; Dionex
Co., Sunnyvale, CA USA) equipped with a tunable UV
detector set at 210 nm. An Aminex HPX-87H ion-
exchange column (Rezex ROA-Organic Acid H+, 300×7.8
mm; Phenomene, Torrence, CA, USA) was eluted using
0.005 N H2SO4 as a mobile phase at a flow rate of 0.6 mL/
min. The column temperature was maintained at 45ºC and
a refraction index (RI) detector was used. The temperature
of the RI detector was 55ºC. Amounts of residual total
sugars (glucose, fructose, and sucrose) were determined as
an amount of sucrose based on the DNS method (24) after
acid hydrolysis (adjusted to pH 1.0 using HCl and heated
to 100ºC for 30 min) with sucrose as standard.
Statistical analysis All experiments were repeated 3
times. Data were presented as mean±standard deviation
(SD). All statistical analyses except regression analysis and
surface layers, were performed using one-way analysis of
variance (ANOVA) in IBM-SPSS software (version 19.0;
IBM, Chicago, IL, USA). Treatments were compared
using ANOVA followed by Tukey test. Student’s t-tests
were performed to confirm the comparisons between
groups. All statistical tests were performed at a 5%
signicance level (p<0.05). Design Expert software (version
7.1.3; Stat-Ease Inc., Minneapolis, MN, USA) was used for
regression analysis (R2, standard error, SS, MS, and F
value) and to estimate the coefficients of the regression
equation. Surface layers were also obtained using Design
Expert software (version 7.1.3; Stat-Ease Inc.). The
succinic acid yield was defined as the amount of succinic
acid obtained from 1 g of sugar consumed, expressed as a
percentage.
Results and Discussion
Medium components screened for succinic acid
production with a one-at-a-time method Key parameters
for succinic acid production were identified using a one-at-
a-time method. Key parameters were 40.00 g/L of initial
total sugars for cane molasses, 4.00 g/L of yeast extract,
Y β0
= βixi∑ βiixi2
∑ βijxixji 1=
n
∑ ε+ + + +
I j≠
xiXi Xi–
∆Xi
-------------=
1914 Shen et al.
1.00 g/L of K2HPO4, 1.50 g/L of KH2PO4, 1.00 g/L of NaCl,
0.60 g/L of MnCl2, 0.30 g/L of CaCl2, and 40.00 g/L of
MgCO3 at pH 7.00. The yield of succinic acid reached
30.60 g/L in 60 h at 37oC (Table 1).
Results obtained from a PBD Based on results of the
one-at-a-time analysis, a PBD for 12 trials with 2
concentration levels of was undertaken to evaluate the
significance of 9 medium components (Table 2). Each
variable was represented as 2 levels with a high level
denoted by (+1) and a low level denoted by (−1). All
experiments were performed in triplicate and the average
value of succinic acid concentrations (g/L) after 60 h was
reported as the response (Y). Variables with confidence
levels greater than 95% were considered to significantly
(p<0.05) influence the production of succinic acid.
Based on above results, 9 variables were analyzed using
the PBD, and the maximum and minimum effect of each
variable on the concentration of succinic acid was determined
using Student’s t-test (Table 3). According to the absolute
value of t, the effects of these 9 variables were in the order
of X2>X4>X3>X5>X8>X6>X7>X9>X1. The variables X2, X3,
X4, X5, and X7 had positive effects on succinic acid
production whereas X1, X6, X8, and X9 showed negative
effects on succinic acid production. The variables X2
(initial total sugars of cane molasses), X3 (yeast extract),
and X4 (MgCO3) had significant (p<0.05) effects on
production of succinic acid with confidence levels greater
than 95%. The remaining variables had confidence levels
of <95% and were considered not to be significant
(p>0.05). These insignificant factors were not included in
subsequent optimization experiments, but were used in all
trials at the (−1) level for a negative contribution and a (+1)
level for a positive contribution. A first order model
equation was derived to represent succinic acid production
as a function of the independent variables:
Table 1. Succinic acid fermentation optimized based on a one-at-a-time method1)
Total sugars (g/L) Succinic acid (g/L) Acetic acid (g/L) Formic acid (g/L)Cell concentration
(OD660)Succinic acid yield
(%)
40.00 30.60±0.4 3.41±0.2 2.81±0.5 6.32±0.8 76.50±0.4
1)Each value is a mean of 3 parallel replicates and is reported as mean±SD.
Table 2. The Plackett-Burman experimental design and corresponding results
Run X11)
X2 X3 X4 X5 X6 X7 X8 X9 Y2)
1 1 -1 1 -1 1 -1 1 -1 1 030.84±0.433)
2 -1 1 1 1 -1 -1 1 -1 1 31.69±0.35
3 1 1 -1 1 1 1 -1 -1 1 33.12±0.50
4 1 1 -1 -1 -1 -1 -1 1 1 31.64±0.29
5 -1 -1 -1 -1 1 1 1 1 1 34.01±0.34
6 1 -1 1 1 1 -1 -1 1 -1 32.40±0.42
7 1 -1 -1 1 -1 1 1 -1 -1 33.69±0.28
8 -1 1 1 -1 1 1 -1 -1 -1 32.70±0.41
9 -1 -1 -1 -1 -1 -1 -1 -1 -1 30.70±0.29
10 -1 -1 1 1 -1 1 -1 1 1 30.17±0.38
11 -1 1 -1 1 1 -1 1 1 -1 31.91±0.37
12 1 1 1 -1 -1 1 1 1 -1 30.44±0.39
1)Each variable was tested at a low (−1) and at a high level (+1) level. The 2 levels of each variable were: X1 (NaCl), 1.0 and 1.5 g/L; X2 (totalsugars of cane molasses), 40 and 60 g/L; X3 (yeast extract), 4 and 6 g/L; X4 (MgCO3), 40 and 50 g/L; X5 (MnCl2), 0.6 and 0.8 g/L; X6
(KH2PO4), 1.5 and 3.0 g/L; X7 (K2HPO4), 1.0 and 1.5 g/L; X8 (MgCl2), 1.0 and 1.5 g/L; and X9 (CaCl2), 0.3 and 0.6 g/L2)Y is the succinic acid concentration (g/L).3)Each value is a mean of 3 parallel replicates and is represented as mean±SD.
Table 3. Succinic acid production based on results of thePlackett-Burman design (PBD)
VariablesCoefficient
estimateStandard
errort Prob>| t | Order
Intercept 31.94 0.20 9.67 0.0049 0
X1
1) -0.11 0. 20 -1.16 0.3645 9
X2 0.86 0. 20 18.50 0.0026 1*
X3 0.46 0. 20 5.18 0.0525 3*
X4 0.46 0. 20 5.34 0.0497 2*
X5 0.29 0. 20 1.71 0.1379 4
X6 -0.25 0. 20 -1.66 0.1568 6
X7 0.22 0. 20 1.74 0.1564 7
X8 -0.27 0. 20 -1.33 0.2263 5
X9 -0.19 0. 20 -1.98 0.1419 8
1)The symbols are the same as in Table 1; *statistically significant atp<0.05
Succinic Acid Production 1915
Y=31.94−0.11X1+0.86X2+0.46X3+0.46X4+0.29X5
−0.25X6+0.22X7−0.27X8−0.19X9 (4)
Results obtained from SA analysis The coefficients of
X2, X3, and X4 were positive, indicating that the path of
steepest ascent should increase their concentration in order
to enhance succinic acid production. The center point of
the PBD was considered to be the origin of the path. The
experimental design and responses for the steepest ascent
path analysis are shown in Table 4. The succinic acid yield
showed a maximum at run 4. The highest production
response was 52.33 g/L when the concentrations of initial
total sugars of cane molasses (X2), yeast extract (X3), and
MgCO3 (X4) were 75, 9, and 65 g/L, respectively,
indicating that this point was near the region of maximum
production response.
Optimization of screening culture conditions using
response surface methodology (BBD) Based on the
information above, the point that represented 75.0 g/L of
glucose, 9.0 g/L of yeast extract, and 65.0 g/L of MgCO3
served as the central BBD level for 3 variables. The coded
and real levels are listed in Table 5, and results for an
analysis of variance (ANOVA) for the BBD are shown in
Table 6. The experimental BBD results were fit to a
second-order polynomial equation as follows:
Y=55.10+6.30X1−1.23X2−2.32X3−2.82X12−3.45X2
2
−4.20X32+0.065X1X2−0.43X1X3+0.11X2X3 (5)
The efficiency of fit of the model was checked using the
determination coefficient (R2). The value of the coefficient
of determination (R2=0.9883) indicated that less than 2%
of the total variation was not explained by the model (Table
5). The values of “Probability>F” (0.03) less than 0.05
indicated that model terms were significant (p<0.05).
There was only a 3% chance could occur due to noise.
Changes in the parameter modeled as the 2 factors moved
Table 4. Steepest ascent experimental design and correspondingresults
RunITSCM1)
(g/L)YE2)
(g/L)MgCO3
(g/L)Succinic acid
(g/L)
1 60.00 6.00 50.00 33.61±0.28
2 65.00 7.00 55.00 39.46±0.35
3 70.00 8.00 60.00 46.66±0.40
4 75.00 9.00 65.00 52.33±0.37
5 80.00 10.00 70.00 50.15±0.43
6 85.00 11.00 75.00 48.24±0.37
1)Initial total sugar of cane molasses.2)Yeast extract
Table 5. The Box-Behnken design and experimental responsesof the dependent variable Y (succinic acid concentration, g/L)
Run
Coded values andreal values
Y (succinic acid g/L)
X11)
X22)
X33) Experimental Predicted
1 70 (-1) 8 (-1) 65 (0) 44.14±0.58 43.82
2 70 (-1) 10 (1) 65 (0) 41.98±0.46 41.23
3 80 (1) 8 (-1) 65 (0) 55.54±0.52 56.29
4 80 (1) 10 (1) 65 (0) 53.64±049 53.96
5 75 (0) 8 (-1) 60 (-1) 51.91±0.68 51.10
6 75 (0) 8 (-1) 70 (1) 45.86±0.57 46.25
7 75 (0) 10 (1) 60 (-1) 48.82±0.41 48.43
8 75 (0) 10 (1) 70 (1) 43.20±0.37 44.01
9 70 (-1) 9 (0) 60 (-1) 42.54±0.46 43.67
10 80 (1) 9 (0) 60 (-1) 57.07±0.50 57.13
11 70 (-1) 9 (0) 70 (1) 39.96±0.82 39.90
12 80 (1) 9 (0) 70 (1) 52.76±0.49 51.63
13 75 (0) 9 (0) 65 (0) 55.15±0.71 55.10
14 75 (0) 9 (0) 65 (0) 55.48±0.53 55.10
15 75 (0) 9 (0) 65 (0) 54.67±0.47 55.10
1)X1 is the initial total sugar of cane molasses (g/L).2)X2 is the yeast extract (g/L).3)X3 is MgCO3 (g/L).
Table 6. Analysis of variance (ANOVA) and coefficients estimated for succinic acid production
Term Coefficient SS1) MS F value Pr>F
model 55.1 494.080 54.90 47.13 0.0003*
X1 6.30 317.390 317.390 272.49 0.0001*
X2 -1.23 12.030 12.030 10.33 0.0236*
X3 -2.32 43.060 43.060 36.97 0.0017*
X1*X1 -2.82 29.360 29.360 25.21 0.0040*
X1*X2 0.065 0.017 0.017 0.015 0.9088
X1*X3 -0.43 0.750 0.750 0.640 0.4592
X2*X2 -3.45 44.080 44.080 37.84 0.0017*
X2*X3 0.11 0.046 0.046 0.040 0.8499
X3*X3 -4.20 65.050 65.050 55.85 0.0007*
Lack of fit 5.490 1.830 11.04 0.0842*
1)SS, sum of squares; MS, mean square; *significant at p<0.05, R2=0.9883, adjusted R2= 0.9674
1916 Shen et al.
along those levels, while the other factor were held
constant at the central point, are shown in Fig. 1.
The independent variable (X1) had a significant (p<0.05)
effect with a positive coefficient (Table 6), according to
which an increase in the concentration of X1 led to an
increase in the succinic acid yield. The negative signs of
the independent variables X2 and X3, and the squared
variables X12, X2
2 , and X32 revealed a reduction in succinic
acid production when the concentrations were increased in
the system. The same phenomenon was observed with the
interaction term of X1X3.
A second-order polynomial model was used to calculate
the values of the initial total sugar content of cane
molasses, yeast extract, and MgCO3, in order to determine
the maximum succinic acid concentration that corresponded
to the optimum levels of these variables. The experimental
data were fitted using Equation (5), which indicated that
the concentrations of initial total sugar of cane molasses
(X1=85 g/L), yeast extract (X2=8.84 g/L), and MgCO3
(X3=63.1 g/L) resulted in a maximum succinic acid
concentration (57.12 g/L). Therefore, optimization of succinic
acid production from cane molasses using A. succinogenes
GXAS137 was achieved.
Succinic acid is an end-product of the sugar substrate, so
the initial sugar concentration influenced cell growth and
metabolites production. Urbance et al. (25) reported that
A. succinogenes could tolerate up to 160 g/L of an initial
glucose concentration in batch fermentation. However,
significant decreases in biomass, succinic acid production,
and sugar use were observed when the initial sugar
concentration was over 65 g/L. In this study, a maximum
value of succinic acid was obtained in a culture with an
initial total sugar concentration of 85 g/L, a higher value
than reported (65 g/L) by both Liu et al. (16) and Lin et al.
(26). This might be due to differences in strains used and
experimental conditions.
It has been reported that A. succinogenes is a fastidious
organism that requires complex nutrients, such as amino
acids and vitamins, for cell growth. Therefore, the yeast
extract was determined to be a key factor for production of
succinic acid because it affected cell growth directly as a
nutrient. The yeast extract contained many trace substances,
such as folic acid, pantothenic acid, biotin, and vitamins
B1, B2, B6, and B12. This may be an important reason why
many vitamins could be omitted while maintaining efficient
succinic acid accumulation in this study. The maximum
value of succinic acid was obtained in a culture with a
yeast extract concentration 8.84 g/L, which was lower than
a previously reported yeast extract concentration of glucose
as carbon source (11). Thus, molasses contain some nitrogen
source that meets the demands of cell growth. Therefore,
the cost of providing a nitrogen source for cane molasses
fermentation should be reduced. Replacement of yeast
extract using other protein sources was unsuccessful,
causing low product concentrations (data not shown). An
in-depth study to identify an inexpensive nitrogen source
for succinic acid production from cane molasses is in
progress.
MgCO3 was also identified as a key factor because it
was used as a neutralizing agent and as a CO2 donor. In
bio-based succinic acid production, the culture pH is
known to be a key factor for both cell growth and succinic
Fig. 1. Response surface curve for succinic acid production byActinobacillus succinogenes GXAS-137 showing the combinationeffects of (A) total sugars of cane molasses and yeast extract,(B), and (C) yeast extract and MgCO3. Other factors wereconstant at 0 levels. YE, yeast extract; TSCM, total sugars of canemolasses
Succinic Acid Production 1917
acid production. The culture pH can influence cellular
metabolism by changing the chemical environment and by
affecting enzyme activity (27). A near-neutral pH culture
environment is suitable for A. succinogenes. However, the
culture pH quickly decreased during fermentation due to
rapid accumulation of succinic acid and acetic acid. MgCO3
was used to maintain a constant pH value in the fermentation
broth in this study. The final pH of a medium with an
initial pH of 7.5 was close to neutral (approximately 6.5)
during fermentation, and a neutral environment was helpful
for accumulation of succinic acid. Therefore, the effect of
medium optimization using MgCO3 may be partly related
to the influence of the medium pH.
The level of dissolved CO2 and the ionization equilibrium
between HCO3− and CO3
2− are direct factors that influence
production of succinic acid (28). As an important CO2 donor
in A. succinogenes fermentation, MgCO3 reacted with
organic acids in the fermentation broth and caused an
increase in the dissolved concentrations of HCO3, CO32,
and CO2. Recent studies have also reported that magnesium
ions play an important role in maintaining cellular metabolism
because these ions are a cofactor for phosphoenolpyruvate
(PEP) carboxykinase, which is a key enzyme for succinic
acid production (29). These properties make MgCO3 a key
factor for improved succinic acid production.
Validation of the optimization fermentation medium
The combination levels of the 3 key factors (initial total
sugars of cane molasses, yeast extract, and MgCO3) were
predicted based on the BBD polynomial model. The
applicability of the model and the accuracy of the
prediction were checked based on verification experiments
performed in triplicate using the optimized conditions
representing the maximum point of the concentration of
succinic acid to verify the modelling results. The predicted
average concentration of succinic acid was 57.12 g/L, and
the average concentration determined by experiment was
57.43±0.86 g/L. This is an improvement in succinic acid
Fig. 2. Time course of cell growth and production of organic acids in batch (a, b) and fed-batch (c, d) fermentations withpretreated cane molasses. Cells were grown in a 1.3 L stirred bioreactor with a cane molasses total sugar content of 85 g/L, yeast extractof 8.84 g/L, and MgCO3 of 63.1 g/L. In fed-batch fermentation, the final total sugar concentration was the same as the batch fermentation
and the initial total sugar concentration of 35 g/L was maintained at 10-15 g/L during the fermentation process. Symbols are sucrose (■),glucose (▲), fructose (▼), total sugar (△), succinic acid (□), acetic acid (▽), and OD660 (◇).
1918 Shen et al.
concentration by approximately 87% relative to the amount
obtained by optimizing a single variable of the culture
medium, where the concentration of succinic acid was only
30.60 g/L. The good correlation between the predicted and
experimental values after optimization justified the validity
of the response model and the existence of an optimum
point.
Fermentation in stirred bioreactors The fermentation
performance of the fed-batch process was much better than
the batch process (Fig. 2). Substrate inhibition was a clear
limitation to succinic acid production by A. succinogenes
in batch fermentation. When the sugar concentration was
maintained at a low level during the fed-batch fermentation
process, cells grew faster and the steady state phase was
prolonged due to elimination of substrate inhibition. In fed-
batch fermentation, the succinic acid concentration using
cane molasses reached 64.34 g/L, higher than previously
reported values (16,27) and succinic acid productivity and
yield were 1.07 g/L/h and 75.69%, which were higher than
corresponding batch fermentation values of 57.96 g/L, 0.97
g/L/h and 67.87%, respectively. Thus, the fed-batch process
was more efficient for cultivation than the batch process for
succinic acid production by A. succinogenes.
Acknowledgments This work was supported by the
National Natural Science Foundation of China (No.
31160023), the Natural Science Foundation of Guangxi
Province (No. 2013GXNSFBA019102), the Science
foundation of Guangxi Academy of Science (No. 13YJ22SW),
and the BaGui Scholars Program Foundation of Guangxi
Province, China.
Disclosure The authors declare no conflict of interest.
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