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Optimization of media conditions for the production of

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Optimization of media conditions for the production of
ethanol from sweet sorghum juice by immobilized
Saccharomyces cerevisiae

Jianliang Yu, Xu Zhang, Tianwei Tan*
Beijing Key Lab of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China



article info                                 abstract

Article history:                             In order to obtain high ethanol yield and fermentation rate, response surface methodology
Received 18 June 2007                        (RSM) was employed to study the effect of culture medium on the ethanol productivity from
Received in revised form                     stalk juice of sweet sorghum by immobilized yeast. A 23 central composite design (CCD) was
20 May 2008                                  chosen to explain the combined effects of the medium constituents, viz. nitrogen (adjusted
Accepted 27 August 2008                      by adding (NH4)2SO4), phosphorus (adjusted by adding KH2PO4), and pH. A mathematical
Published online -                           correlation about the influence of the nitrogen, phosphorus, and pH on the ethanol
                                             productivity was established. It predicted that the maximum ethanol production rate
Keywords:                                    (119.12 g/l h) was observed for a medium consisting of 0.77 g/l phosphorus, 2.15 g/l nitrogen,
Ethanol                                      and pH ¼ 6.39. Under this condition, the ethanol fermentation rate was 122.85 g/l h.
Response surface methodology                                                                      ª 2008 Elsevier Ltd. All rights reserved.
Sweet sorghum juice
Medium optimization




1.        Introduction                                                 mineral elements, sweet sorghum juice is a good and
                                                                       economical substrate for ethanol production. Cao et al. [5]
The production of fuel ethanol by fermentation requires the            have reported that KH2PO4, (NH4)2SO4, and Mg2þ would influ-
ability to produce high ethanol concentrations rapidly while           ence the growth and metabolism of the yeast cells, meanwhile
maintaining good yields. Rapid fermentation and high alcohol           pH is an another important factor. There are few reports
levels are desirable to minimize capital costs and energy              analyzing the mathematical correlation about the influence of
required for distillation, while good yields are necessary for         the added mineral elements on the ethanol yield and
process economics. The substrate is the main cost component            productivity.
for industrial ethanol production and it is essential that                The traditional ‘one-factor at a time’ technique used for
ethanol production should be carried out with cheap                    optimizing a multivariable system is not only time consuming
substrates such as sweet sorghum or sago starch [1,2]. Sweet           but also often easily misses the alternative effects between
sorghum is presently considered as a potential alternative             the components. Recently many statistical experimental
crop for energy and industry in the EU [3], mainly because it          design methods have been employed in bioprocess optimi-
can yield biomass and fermentable sugars. The composition              zation. Response surface methodology (RSM) is the one
of the juice was presented in Table 1 [4]. Because of the large        suitable for identifying the effect of individual variables and
amount of fermentable sugars accompanied by profuse                    for seeking the optimum conditions for a multivariable




 * Corresponding author.
   E-mail address: twtan@mail.buct.edu.cn (T. Tan).
0961-9534/$ – see front matter ª 2008 Elsevier Ltd. All rights reserved.
doi:10.1016/j.biombioe.2008.08.020

 Please cite this article in press as: Yu J et al., Optimization of media conditions for the production of ethanol from sweet
 sorghum juice by immobilized Saccharomyces cerevisiae, Biomass and Bioenergy (2008), doi:10.1016/j.biombioe.2008.08.020
                                                   ARTICLE IN PRESS
2                                               biomass and bioenergy xxx (2008) 1–6




    Table 1 – The components of sweet sorghum juice.
    Variety                                          The contents of sweet sorghum juice (g/kg)

               Glucose      Fructose       Sucrose       Protein       P       Kþ          Naþ          Caþ     Mg2þ        Fe3þ        Mn2þ

    Rio        10           8              69            4.87         0.050    2.2         0.015        0.093   0.0084      0.084       0.091




system efficiently. This method has been successfully applied           2.3.    Pre-cultivation
to optimize alcoholic fermentation [6–8], optimize vegetable
oil bioconversion [9] and biomass production [10]. Enzyme              Two loops from yeast slants were used to inoculate 100 ml of
production was also promoted by optimizing the medium                  the pre-culture medium in 250 ml Erlenmeyer flasks and
composition using RSM [11]. A detailed account of this tech-           cultivated on a rotary shaker (180 rpm) at 37  C for 20 h.
nique has been outlined [12]. Basically, this optimization
process involves three major steps: performing the statisti-           2.4.    Immobilization procedure
cally designed experiments, estimating the coefficients in
a mathematical model, and predicting the response and                  Cells were easily immobilized according to the method
checking the adequacy of the model.                                    described previously [13]. Immobilized yeast particles were
    Hence, the main objective of this paper was to evaluate the        added into the fermentation culture medium in 250 ml flasks.
effects of initial concentration of nitrogen, initial concentra-       The flasks were incubated at 37  C and 140 rpm. The total
tion of phosphorus, and initial pH on ethanol productivity             biomass was determined at the end of each set of experi-
using the response surface methodology. The conditions that            ments. The yeast cells were separated from the support by
yielded the maximum ethanol productivity for the selected              washing with distilled water and centrifuging the collected
region of interest and the response surface provided by the            liquid. Cell number was determined using a haemocytometer.
predicting model are described in this study. According to this        The total biomass removed from the support was then dried at
model, the proper amounts of nitrogen ((NH4)2SO4) and                  80  C for 24 h and weighed [14].
phosphorus (KH2PO4) are easily gained to add into different
varieties of sweet sorghum juice, and maximum ethanol                  2.5.    Fermentation
productivity will always be reached.
                                                                       (NH4)2SO4, KH2PO4 and NaOH were supplemented into the
                                                                       sterilized stalk juice before the fermentations were carried out
                                                                       according to Table 3. Immobilized yeast particles were added
2.        Materials and methods                                        into the fermentation culture medium at the ratio of 1:1 (W/V) in
                                                                       250 ml flasks. The flasks were incubated at 37  C and 140 rpm.
2.1.      Sorghum
                                                                       2.6.    Analytical methods
Sweet sorghum, bred by Chinese Academy of Agricultural
Science, was harvested in Beijing, October 2006. Leaves and            Stalk juice of sweet sorghum contained sugar primarily in the
husks were stripped from the fresh stalks by hand and stored           form of sucrose, glucose and fructose. Glucose was deter-
in the freezer at À20  C. The stalks were squeezed by a three-        mined enzymatically with a glucose oxidase-–chromogen
roller mill to obtain fresh juice. The fresh juice was sterilized      reagent (Shandong University) [13]. Sorghum sucrose was
in an autoclave sterilizer at 0.15 MPa and 121  C for 15 min and      hydrolyzed in 1.2 N HCl for 7 min at 60  C and neutralized with
stored at 4  C in a refrigerator before it was used. The total        1 N NaOH prior to its determination by the method of glucose
nitrogen and phosphorus contents were 1.479 and 0.131 g/l,             and translated into sucrose. Residual sugars were determined
respectively. Sugar contents of the juice were (in g lÀ1):             using the 3,5-dinitrosalicylic acid (DNS) method [15,16]. The
glucose, 15.5; fructose, 24.6; sucrose, 140.6.                         concentrations of nitrogen and phosphorus were determined
                                                                       according to the standard methods of the People’s Republic of
                                                                       China [4]. The ethanol content was measured by using
2.2.      Microorganism and media

The laboratory mutant strain of baker yeast was maintained
in MY medium whose composition (in g lÀ1) was glucose, 20;              Table 2 – Experimental range and levels of the
yeast extract, 3; polypeptone, 5; malt extract, 3; agar, 20. In all     independent variables.
cases, cultures were maintained at 37  C for 24 h and then             Variables                  Symbol                Coded levels
stored at 4  C. Subculturing was done every 2 months. The                                                       À1           0           1
composition of the pre-culture medium for yeast (in g lÀ1) was:
                                                                        pH                         X1           3           5           7
glucose, 10; sucrose, 10; yeast extract, 3; polypeptone, 5; malt
                                                                        Phosphorus (g/l)           X2           0.131       0.701       1.271
extract, 3. All the media were adjusted to pH 6.5 and auto-
                                                                        Nitrogen (g/l)             X3           1.479       2.009       2.539
claved at 116  C for 20 min before use.

 Please cite this article in press as: Yu J et al., Optimization of media conditions for the production of ethanol from sweet
 sorghum juice by immobilized Saccharomyces cerevisiae, Biomass and Bioenergy (2008), doi:10.1016/j.biombioe.2008.08.020
                                                  ARTICLE IN PRESS
                                             biomass and bioenergy xxx (2008) 1–6                                                                   3



 Table 3 – Matrix corresponding to 23 factorial designs together with the observed experimental data.
 Run Number                 Uncoded Factor Values                       Ethanol Productivity (g/l $h)                        pH of the medium

                       X1            X2             X3             Experimental                    Predicted               Initial      Eventual

 1                      3           0.131          2.009                  100.70                       101.16                3            2.45
 2                      3           1.271          2.009                  106.91                       107.40                3            2.47
 3                      7           0.131          2.009                  117.76                       116.21                7            3.24
 4                      7           1.271          2.009                  117.04                       116.02                7            3.92
 5                      5           0.131          1.479                  114.19                       112.77                5            3.44
 6                      5           0.131          2.539                  108.14                       109.19                5            3.04
 7                      5           1.271          1.479                  117.04                       115.09                5            3.38
 8                      5           1.271          2.539                  111.43                       112.94                5            3.00
 9                      3           0.701          1.479                  108.57                       109.01                3            2.74
 10                     7           0.701          1.479                  112.00                       113.12                7            4.30
 11                     3           0.701          2.539                  100.43                        98.30                3            2.44
 12                     7           0.701          2.539                  118.57                       118.12                7            3.22
 13                     5           0.701          2.009                  117.14                       117.16                5            3.06
 14                     5           0.701          2.009                  117.14                       117.16                5            3.08
 15                     5           0.701          2.009                  117.14                       117.16                5            3.06




Shimadzu GC-2050 gas chromatography with cbp-20 capillary                The response variable (ethanol productivity) was fitted by
column and a flame ionization detector. The chromatogram                  a second order model in order to correlate the response vari-
was run at 180  C oven temperature and 90  C injection                 able to the independent variables. The general form of the
temperature using N2 as a carrier gas and H2 as a flaming gas.            second degree polynomial equation is
                                                                                     X
                                                                                     4             X
                                                                                                   4              X X
                                                                                                                  4   4

2.7.    Experimental design                                              Yi ¼ b0 þ       bi Xi þ       bii X2 þ
                                                                                                            i       i   bij Xi Xj                  (2)
                                                                                     1             1               1   1


A 23 full factorial design with three coded levels leading to            where Yi is the predicted response; Xi, Xj are input variables
fifteen sets of experiments was used to develop a statistical             which influence the response variable Y; b0 is the offset term;
model for ethanol production [17]. Three assays at the centre            bi is the ith linear coefficient; bii is the quadratic coefficient and
point were carried out to estimate the error involved in the             bij is the ijth interaction coefficient.
experiment and also to know if there were any curvature in                    The second order polynomial coefficients (Xi) were calcu-
the response surface. Initial pH (X1), initial concentration of          lated using the software package Design Expert to estimate
phosphorus (X2) adjusted by adding KH2PO4, initial concen-               the responses of the dependent variable [18–20]. Isoresponse
tration of nitrogen (X3) adjusted by adding (NH4)2SO4, were              contour plots were also obtained using Design Expert. The
chosen as independent factors in the experimental design.                computation was carried out by multiple regression analysis
The ethanol productivity was taken as the dependent variable             making use of the least squares method and MATLAB sub-
or response. For statistical calculations, the variables X1, X2          routine software.
and X3 were coded according to
     À       Á
xi ¼ Xi À Xcp DXi ; i ¼ 1; 2; 3; .; k                        (1)
                                                                         3.        Results and discussion
where xi, dimensionless value of an independent variable; Xi,
real value of an independent variable; Xcp, real value of an             3.1.     The response surface methodology experiment
independent variable at the centre point; and DXi, step change           results of ethanol fermentation
of real value of the variable i corresponding to a variation of
a unit for the dimensionless value of the variable i. The range          The most important factors in the sweet sorghum juice which
and the levels of the variables investigated in this study are           affect the production of ethanol are the initial pH, the initial
given in Table 2.                                                        amount of phosphorus and nitrogen. The suitable levels for
   The central values (zero level) chosen for experimental               these parameters were also determined using statistical CCD.
design were (g/l): phosphorus ¼ 0.701, nitrogen ¼ 2.009, pH ¼ 5.         The experimental design matrix was given in Tables 2 and 3.




 Table 4 – ANOVA for full quadratic model.
 Source of Variation        Sum of Squares (SS)     Degrees of Freedom (DF)                Mean Squares (MS)                 F-value    Probe > F

 Model                           509.4305                           9                              56.60339                  6.999544     0.0001
 Residual                         40.43363                          5                               8.086726
 Total                           549.8641                          14


 Please cite this article in press as: Yu J et al., Optimization of media conditions for the production of ethanol from sweet
 sorghum juice by immobilized Saccharomyces cerevisiae, Biomass and Bioenergy (2008), doi:10.1016/j.biombioe.2008.08.020
                                                ARTICLE IN PRESS
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                                                                 Fifteen experiments were performed using different combi-
                                                                 nations of the variables as per the CCD. Using the results of the
                                                                 experiments, the following second order polynomial equation
                                                                 giving the ethanol productivity as a function of initial pH (X1),
                                                                 initial concentration of phosphorus (X2, g/l), initial concen-
                                                                 tration of nitrogen (X3, g/l) were obtained.

                                                                 Y ¼ 68:68585 þ 8:810471X1 þ 16:19834X2 þ 14:98243X3
                                                                   À1:227687X1 Â X1 À 6:320637X2 Â X2 À 9:217871X3 Â X3        (3)
                                                                   À1:409759X1 Â X2 þ 3:706179X1 Â X3 þ 1:182142X2 Â X3

                                                                   The predicted ethanol productivity using the above
                                                                 equation was given in Table 3 along with the experimental
                                                                 value. The results were analyzed using the analysis of
                                                                 variance (ANOVA) as appropriate to the experimental design
                                                                 used (Table 4). The ANOVA of the quadratic regression model
                                                                 demonstrates that the model is highly significant, as it is
                                                                 evident from the Fisher’s F-test (F-model, mean square
                                                                 regression: mean square residual ¼ 6.999) and a very low
                                                                 probability value (P-model > F ¼ 0.0001). The computed value
                                                                 of F exceeding the tabulated value of F at a probability level
                                                                 of 0.0001 so that the null hypothesis (H0) is rejected at the
                                                                 0.0001 level of significance. This indicates the combined
                                                                 effects of all the independent variables significantly
                                                                 contributed to the enhancement of ethanol productivity. The
Fig. 1 – Contour plot of ethanol productivity (EP) (g/l h)       coefficient of determination, R2 is 0.92, implies that the
the effect of nitrogen and phosphorus on EP. pH was              sample variation of 92.35% for ethanol productivity is
fixed at 6.39.                                                    attributed to the independent variables, viz., the initial pH,
                                                                 the initial amount of phosphorus and nitrogen. The R2 value
                                                                 also indicates that only 1% of the variation is not explained
                                                                 by the model.
                                                                    The 3D response surface and the 2D contour plots are
                                                                 generally the graphical representations of the regression




Fig. 2 – Contour plot of ethanol productivity (EP) (g/l h) the   Fig. 3 – Contour plot of ethanol productivity (EP) (g/l h) the
effect of phosphorus and pH on EP. Nitrogen was fixed at          effect of nitrogen and pH on EP. Phosphorus was fixed at
2.15 g/l.                                                        0.77 g/l.

 Please cite this article in press as: Yu J et al., Optimization of media conditions for the production of ethanol from sweet
 sorghum juice by immobilized Saccharomyces cerevisiae, Biomass and Bioenergy (2008), doi:10.1016/j.biombioe.2008.08.020
                                                  ARTICLE IN PRESS
                                            biomass and bioenergy xxx (2008) 1–6                                                    5



 Table 5 – Experimental verification of combined effect of optimized medium on the response of ethanol productivity.
 Variables     Levels before optimization (g/l)     Levels after optimization               Ethanol productivity (g/l h)

                                                    Coded       Uncoded (g/l)    Before optimization        After optimization

                                                                                                        Predicted    Experimental

 pH                          5                       0.70           6.39                114.19            119.12           122.85
 Phosphorus                  0.131                   0.12           0.77
 Nitrogen                    1.479                   0.27           2.15




equation, the 2D contour plots are presented in Figs. 1–3 from       using the above optimum concentrations of the variables is
which the values of ethanol productivity for different               119.12 g/l h. The verification of the results using the optimized
concentrations of the variables can be predicted. Each               medium was accomplished by carrying out shake-flask
contour curve represents an infinite number of combinations           experiments. The maximum ethanol productivity obtained
of two test variables with the residual one maintained               experimentally was found to be 122.85 g/l h. This is obviously
constantly. The maximum predicted value is indicated by the          in close agreement with the model prediction. After optimi-
surface confined in the smallest ellipse in the contour               zation, both of the ethanol productivity and ethanol yield
diagram. The contour plots in Fig. 1 show that there is              were enhanced experimentally (Table 5).
a significant mutual interaction between nitrogen and
phosphorus. The contour plots in Figs. 2 and 3 indicated that        3.2.   Optimizing condition for repeated batch
pH is the most important factor which influenced the ethanol          fermentation
productivity obviously. It was concluded that higher ethanol
production rates could be achieved by increasing initial pH          In repeated batch fermentation, sample was withdrawn every
( 7), and the initial and eventual pH of the medium                  4 h. The residual sugar concentration and ethanol concen-
explained this conclusion (Table 3). The pHs of the mediua           tration were determined immediately. After 13–14 h of incu-
were decreased as fermentations went on, for example the             bation, all the media were withdrew, and the carriers were
pH of run 11 dropped from 3 to 2.44 which was out of the             then retrieved and transferred to a fresh batch juice. The
appropriate pH range for yeast cells. Meanwhile, the more            process was repeated. It can be seen in Fig. 4 that the overall
nitrogen was added the lower pH was, because of the NH4þ             ethanol concentration fluctuated around 83 g/l slightly during
hydrolyzing.                                                         the first 12 repeated batches. The ethanol productivity of the
   A numerical method of Java was used to solve the regres-          first 10 batches fluctuated around 120 g/l h, then it decreased
sion equation (Eq. (3)). The optimal values of the test variables    to 90 g/l h. Though the immobilized cell concentrations
in uncoded unit are as follows:                                      increased slowly throughout the whole progress of fermen-
                                                                     tation, the activity of the cells must be decreased which can be
X1 ¼ 6:39; X2 ¼ 0:77; X3 ¼ 2:15
                                                                     explained by the free cell concentrations in the medium. The
with the corresponding Y ¼ 119.12 g/l h. The model predicts          declining cell activity leaded to the low ethanol productivity.
that the maximum ethanol productivity that can be obtained           This means, the immobilized Saccharomyces cerevisiae cells in




Fig. 4 – Repeated batch fermentation of stalk juice by immobilized Saccharomyces cerevisiae under the optimized condition.

 Please cite this article in press as: Yu J et al., Optimization of media conditions for the production of ethanol from sweet
 sorghum juice by immobilized Saccharomyces cerevisiae, Biomass and Bioenergy (2008), doi:10.1016/j.biombioe.2008.08.020
                                                ARTICLE IN PRESS
6                                           biomass and bioenergy xxx (2008) 1–6



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 Please cite this article in press as: Yu J et al., Optimization of media conditions for the production of ethanol from sweet
 sorghum juice by immobilized Saccharomyces cerevisiae, Biomass and Bioenergy (2008), doi:10.1016/j.biombioe.2008.08.020

								
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