Bioresource Technology 102 (2011) 7548–7554

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Bioresource Technology

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Optimization of the production of poly-c-glutamic acid by Bacillusamyloliquefaciens C1 in solid-state fermentation using dairy manure compostand monosodium glutamate production residues as basic substrates

Xiaoyu Yong, Waseem Raza, Guanghui Yu, Wei Ran, Qirong Shen, Xingming Yang ⇑Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization, Nanjing Agricultural University, Nanjing 210095, China

a r t i c l e i n f o

Article history:Received 7 January 2011Received in revised form 5 May 2011Accepted 17 May 2011Available online 27 May 2011

Keywords:OptimizationPoly-c-glutamic acidResponse surface methodologySolid-state fermentation

0960-8524/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.biortech.2011.05.057

⇑ Corresponding author. Tel./fax: +86 025 8439629E-mail addresses: 2009203043@njau.edu.cn, xingm

a b s t r a c t

Poly-c-glutamic acid (c-PGA) is a polymer with uses in foods, cosmetics, medicine and agriculture. Themedium for the production of c-PGA by Bacillus amyloliquefaciens C1 was optimized by response surfacemethodology using agro-industrial wastes in solid-state fermentation (SSF). The optimal SSF medium(20 g substrates with 50% initial moisture) for producing c-PGA was determined to contain 5.51 g dairymanure compost, 1.91 g soybean cake, 0.57 g corn flour, 2.15 g monosodium glutamate production resi-dues, 1.5 g wheat bran, 0.5 g rapeseed cake, 0.1 g citric acid, 0.05 g MgSO4�7H2O and 0.03 g MnSO4�H2O. Inthis medium the strain produced up to 0.0437 g c-PGA per gram of substrates when cultured for 48 h at37 �C. SDS–PAGE showed that the molecular weight of the c-PGA was more than 130 kDa. Due to thehigh-yields observed and the low-cost nature of the optimal medium, this study indicates a possibilityto establish economical large-scale production of c-PGA.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

The biopolymer poly-c-glutamic acid (c-PGA), the main compo-nent in the capsules produced by certain Bacillus species (Jianget al., 2006), consists of D- and/or L-glutamate connected by amidebonds between c-carboxyl and a-amino groups. Its molecularweight ranges from 10 to 2000 kDa (Ashiuchi and Misono, 2002).The anionic c-PGA is water-soluble, biodegradable, edible and non-toxic to humans and the environment, making it useful for variousapplications in foods, cosmetics, medicine and agriculture. It can beused as an animal feed additive, in biodegradable diapers, for drugdelivery, water retaining material, slow-release systems for fertil-izers and wastewater treatment (Ashiuchi, 2010; Shih and Van,2001). Due to the increasing number in applications of c-PGA,the demand for methods to significantly enhance c-PGA produc-tion while decreasing cost has increased. c-PGA can be producedwith submerged fermentation (SMF) (Shi et al., 2006; Yoon et al.,2000); however, problems with oxygen supply due to viscosityincreases in the medium are observed (Xu et al., 2005b). As analternative to SMF, c-PGA can be produced by solid-state fermen-tation (SSF) (Chen, 2005; Wang et al., 2008; Xu et al., 2005b). Theuse of low-cost raw materials such as dairy manure composts(DMC) and monosodium glutamate production residues (MGPR)

ll rights reserved.

1.ing@njau.edu.cn (X. Yang).

could potentially make SSF a viable option for the economic pro-duction of c-PGA.

Therefore, response surface methodology (RSM) was used tooptimize the composition of a solid medium composed of agricul-tural and industrial waste products for the production of c-PGA byBacillus amyloliquefaciens C1.

2. Methods

2.1. Organism and culture medium

Strain C1, isolated from farmland in Nanjing, Jiangsu Province,China and identified as B. amyloliquefaciens by both physiologicaland biochemical characteristics and phylogenetic analysis of 16SrDNA sequences, was maintained on a 5% agar slant composed of10 g/L peptone, 3 g/L beef extract and 5 g/L NaCl with a pH of7.0–7.2, which was also used as seed medium without agar. Thestock culture was sub-cultured at regular intervals of one monthand stored at 4 �C. A loopful of cells from the slant was inoculatedinto a 150-mL Erlenmeyer flask containing 30-mL seed mediumand cultivated using a rotary shaker at 170 rpm at 30 �C for 14 h(mid-log phase of C1) until the cell concentration (OD600) reached2.0. The liquid culture of C1 was then sub-inoculated into 100 mLof seed medium in a 500-mL flask at a 5% level (v/w) and incubatedunder the same conditions.

DMC and MGPR were used as the basic substrates in SSF. TheDMC was produced by composting fresh dairy manures by Lianye

X. Yong et al. / Bioresource Technology 102 (2011) 7548–7554 7549

Bio-technology Co., Ltd. (Yixing, Jiangsu Province, China). The com-post contained 30% moisture, 33.24% carbon, 1.97% total nitrogen,0.83% phosphorus and 0.69% potassium. The MGPR was obtainedfrom Anhui BBCA Biochemical Co., Ltd. (Bengbu, Anhui Province,China), which contained 28.8% moisture (w/w), 9.4% glutamic acid(w/w), 29.7% crude protein (w/w), 2.16% reducing sugar (w/w).

2.2. Solid-state fermentation (SSF)

Twenty grams of substrates in a 250-mL Erlenmeyer flask wasused to produce c-PGA with SSF. DMC was used as the basic sub-strates and the other medium compositions were based on the sin-gle-factor experiments and RSM experimental design. The initialpH and moisture content of the substrates were adjusted by adding0.1-M HCl or NaOH and 0.1-M Na-phosphate buffer, respectively,which were well mixed and autoclaved at 121 �C for 20 min. Theflasks were then cooled to room temperature, and each was inocu-lated with B. amyloliquefaciens C1, thoroughly mixed with a sterileglass rod and sealed with eight-layer gauze. They were then incu-bated in a stationary mode with a relative humidity of 90% for 48 h.

2.3. Selection for supplemental nutrients and parameters of solid-statefermentation

Eight SSF experiments were conducted to detect the effects ofdifferent carbon sources on c-PGA production, which consisted ofDMC supplemented with either 5.0% (w/w) glucose, lactose, su-crose, starch, glycerol, L-glutamic acid, citric acid or MGPR (initialmoisture content was 60%). The nitrogen sources, soybean cake, ra-peseed cake, corn flour, wheat bran, peptone or yeast extract weretested at a level of 10%, and the media were supplemented with5.0% MGPR and 1.0% citric acid. To determine the impact of mineralsalt additions, MgSO4�7H2O, MnSO4�H2O, FeCl3�6H2O or CaCl2�2H2Owere added to final concentrations of 0.5%, 0.3%, 0.3% and 0.5%,respectively. The influence of the inoculum levels was evaluatedby varying inoculum levels of 2.5%, 5%, 7.5%, 10% and 15% (v/w).To determine the influence of the initial moisture content, the med-ium was adjusted to moisture levels of 40–70% by adding 0.1-M Na-phosphate buffer (pH 7.0). Fermentation temperatures of 28, 30, 33,37, 40 and 45 �C were also tested, and initial pH values of 5.5–8were achieved by adjustment with 0.1-M HCl or NaOH.

2.4. Plackket–Burman design (PBD)

Eight variables were chosen based on the previous single factorexperiments: wheat bran, soybean cake, rapeseed cake, corn flour,MGPR, citric acid, MgSO4�7H2O and MnSO4�H2O, which were

Table 1Plackket–Burman experimental design and its response (c-PGA production).

Runs Real levels (coded levels)

Wheatbran (g)

Soybeancake (g)

Rapeseedcake (g)

Cornflour (g)

MGPR(g)

1 1.50 (+1) 1.00 (�1) 0.50 (+1) 0.00 (�1) 1.00 (�1)2 1.50 (+1) 2.00 (+1) 0.00 (�1) 0.50 (+1) 1.00 (�1)3 1.00 (�1) 2.00 (+1) 0.50 (+1) 0.00 (�1) 2.00 (+1)4 1.50 (+1) 1.00 (�1) 0.50 (+1) 0.50 (+1) 1.00 (�1)5 1.50 (+1) 2.00 (+1) 0.00 (�1) 0.50 (+1) 2.00 (+1)6 1.50 (+1) 2.00 (+1) 0.50 (+1) 0.00 (�1) 2.00 (+1)7 1.00 (�1) 2.00 (+1) 0.50 (+1) 0.50 (+1) 1.00 (�1)8 1.00 (�1) 1.00 (�1) 0.50 (+1) 0.50 (+1) 2.00 (+1)9 1.00 (�1) 1.00 (�1) 0.00 (�1) 0.50 (+1) 2.00 (+1)10 1.50 (+1) 1.00 (�1) 0.00 (�1) 0.00 (�1) 2.00 (+1)11 1.00 (�1) 2.00 (+1) 0.00 (�1) 0.00 (�1) 1.00 (�1)12 1.00 (�1) 1.00 (�1) 0.00 (�1) 0.00 (�1) 1.00 (�1)

a The c-PGA production (%, w/w), the ratio of the weight of c-PGA to the total SSF m

designated as X1, X2, X3, X4, X5, X6, X7 and X8, respectively. Each vari-able was evaluated at a high level (+) and a low level (�). The exper-imental design is shown in Table 1. The variables, which weresignificant at 95% of the confidence level (P < 0.05) from the regres-sion analysis, were considered to have a significant effect (Guo et al.,2009) on c-PGA production and were further optimized using thepath of steepest ascent experiment. The experimental design andstatistical analysis of the data were performed by the Minitab (R)15.1.0.0 (Minitab Inc., USA) statistical software package.

2.5. Path of steepest ascent experiment

The variables selected by the PBD were further investigatedusing the path of steepest ascent, which is considered to be a sim-ple and economically efficient procedure for moving rapidly to-wards the region of the optimal response (Wang and Wan, 2009).The contents of soybean cake (X2), corn flour (X4) and MGPR (X5)were increased along the path of steepest ascent until no increasein response occurred.

2.6. Optimization of the concentration of significant variables bycentral composite design (CCD)

Once the region of optimal response was identified by the pathof steepest ascent, CCD, developed by Box and Wilson (Niladeviet al., 2009), was adopted to detect the optimal content of the threevariables selected by PBD. The coded levels and real values of thevariables are shown in Table 2. The role of each variable, theirinteraction and statistical analysis to obtain the predicted yieldwere explained by applying the following quadratic equation:

Y ¼ b0 þXn

i¼1

biXi þXn

i¼1

biiX2i þ

Xn�1

i¼1

Xn

j¼iþ1

bijXiXj ð1Þ

where Y is the response; Xi and Xj are the independent variables; b0

is a constant; and bi, bii and bij are the linear, squared and interac-tion coefficients, respectively.

The coefficient of determination (R2) and adjusted R2 (Adj R2)were used for the verification of the significance of the second-order polynomial model (Raza et al., 2010a). Based on the analysisof variance (ANOVA), parameters with a significance level (P)greater than 5% were eliminated to obtain the final reduced model.This final model can be displayed as three-dimensional (3D)response surface plots by varying two factor levels while keepingthe other factor at a constant level. Design-Expert version 7.0.0(Stat-Ease, Inc., Minneapolis, MN, USA) was used for the regressionanalysis and the graphical presentation.

c-PGA production(%, w/wa)

Citricacid (g)

MgSO4�7H2O(g)

MnSO4�H2O(g)

Actual Predicted

0.10 (�1) 0.05 (+1) 0.03 (+1) 1.36 ± 0.211 1.550.10 (�1) 0.00 (�1) 0.03 (+1) 3.14 ± 0.076 2.940.10 (�1) 0.00 (�1) 0.00 (�1) 2.66 ± 0.197 2.700.30 (+1) 0.00 (�1) 0.00 (�1) 2.35 ± 0.092 2.410.10 (�1) 0.05 (+1) 0.00 (�1) 3.38 ± 0.069 3.580.30 (+1) 0.00 (�1) 0.03 (+1) 2.84 ± 0.044 2.800.30 (+1) 0.05 (+1) 0.00 (�1) 2.70 ± 0.066 2.640.10 (�1) 0.05 (+1) 0.03 (+1) 3.04 ± 0.115 2.850.30 (+1) 0.00 (�1) 0.03 (+1) 2.28 ± 0.036 2.470.30 (+1) 0.05 (+1) 0.00 (�1) 2.01 ± 0.095 1.810.30 (+1) 0.05 (+1) 0.03 (+1) 1.34 ± 0.070 1.400.10 (�1) 0.00 (�1) 0.00 (�1) 1.09 ± 0.087 1.05

edium.

Table 2Central composite experiment design with the three significant variables and itsresponse.

Runs Real levels (coded levels) c-PGA production(%, w/wa)

Soybeancake (g)

Cornflour (g)

MGPR(g)

Actual Predicted

1 1.76 (�1) 0.48 (�1) 1.84 (�1) 3.53 ± 0.092 3.522 2.08 (+1) 0.48 (�1) 1.84 (�1) 3.69 ± 0.087 3.733 1.76 (�1) 0.64 (+1) 1.84 (�1) 3.60 ± 0.182 3.624 2.08 (+1) 0.64 (+1) 1.84 (�1) 3.71 ± 0.089 3.735 1.76 (�1) 0.48 (�1) 2.32 (+1) 3.98 ± 0.089 4.016 2.08 (+1) 0.48 (�1) 2.32 (+1) 3.91 ± 0.123 3.947 1.76 (�1) 0.64 (+1) 2.32 (+1) 4.10 ± 0.053 4.118 2.08 (+1) 0.64 (+1) 2.32 (+1) 3.87 ± 0.141 3.939 1.65

(�1.682)0.56 (0) 2.08 (0) 3.83 ± 0.128 3.83

10 2.17(+1.682)

0.56 (0) 2.08 (0) 3.93 ± 0.137 3.86

11 1.92 (0) 0.43(�1.682)

2.08 (0) 4.12 ± 0.036 4.1

12 1.92 (0) 0.69(+1.682)

2.08 (0) 4.24 ± 0.081 4.19

13 1.92 (0) 0.56 (0) 1.68(�1.682)

3.31 ± 0.060 3.29

14 1.92 (0) 0.56 (0) 2.48(+1.682)

3.92 ± 0.087 3.87

15 1.92 (0) 0.56 (0) 2.08 (0) 4.37 ± 0.140 4.4016 1.92 (0) 0.56 (0) 2.08 (0) 4.41 ± 0.078 4.4017 1.92 (0) 0.56 (0) 2.08 (0) 4.43 ± 0.036 4.4018 1.92 (0) 0.56 (0) 2.08 (0) 4.36 ± 0.036 4.4019 1.92 (0) 0.56 (0) 2.08 (0) 4.42 ± 0.036 4.4020 1.92 (0) 0.56 (0) 2.08 (0) 4.38 ± 0.043 4.40

a The c-PGA production (%, w/w), the ratio of the weight of c-PGA to the total SSFmedium.

7550 X. Yong et al. / Bioresource Technology 102 (2011) 7548–7554

2.7. Extraction of c-PGA

One-hundred milliliter deionized water was added to the fer-mented substrates and the mixture was agitated on a rotary shakerat 150 rpm at room temperature (about 25 �C) for 2 h. Forty milli-liter of the slurry was subjected to centrifugation at 500g for 5 minto remove the SSF substrates. The supernatant was subjected tocentrifugation at 13,000g for 10 min. The resulting supernatantwas poured into four volumes of cold ethanol to precipitate thec-PGA, which was collected by centrifugation at 17,000g for20 min. A 5-mL aliquot of deionized water was added to dissolvethe precipitate and the resulting solution was stored at 4 �C.

2.8. Analysis of c-PGA

A 2-mL aliquot of the crude c-PGA solution and 10 mL 6 M HClwere added to a hydrolysis flask and hydrolysis was carried out at110 �C for 24 h under vacuum. The hydrolyzate was evaporated todryness using a rotary evaporator, followed by adding 1 mL ofdeionized water to dissolve the dry sample. The liquid samplewas filtered through a 0.45-lm membrane filter, and analyzedwith a Biochrom 30 Amino Acid Analyzer (GE, USA). The corre-sponding unhydrolyzed sample was also analyzed, and the differ-ence in the concentration of free glutamic acid between thesetwo solutions was considered to be the c-PGA concentration. c-PGA production was expressed as a percentage (%, w/w), whichindicated the ratio of the weight of c-PGA to the total SSF medium.

2.9. Enumeration of the microbial populations by plate count

One gram fermented substrates was added to 9 mL sterilizedwater and agitated on a rotary shaker at 150 rpm at 4 �C for 1 hto dislodge cells from the solid substrate. Then serial dilutions(10�7 to 10�9) of the fermented materials were prepared using

sterilized water (w/v). A 0.1-mL aliquot of the diluted mixturewas spread onto LB plates in triplicates. After incubation at 37 �Cfor 24-h, the number of colonies on the plates were counted to cal-culate the number of CFU/g (CFU = colony-forming unit).

2.10. Molecular size estimation of c-PGA

Sodium dodecyl sulfate–polyacrylamide gel electrophoresis(SDS–PAGE) of c-PGA was performed according to the conditionsdescribed previously (Yamaguchi, 1996). Alcian blue 8GX was usedto stain c-PGA in acrylamide gel.

3. Results and discussion

3.1. Single-factor experiments

Compared to the control, all carbon sources added to the SSFsubstrates improved c-PGA production. Maximum c-PGA produc-tion (1.17%) was obtained when citric acid was the supplement,followed by glycerol (1.0%) and L-glutamic acid (0.95%). The resultswith citric acid parallel those obtained by Xu et al. (2005a).Although MGPR resulted in the production of a lower amount ofc-PGA (0.66%), it was chosen as supplement for the basic mediumin further studies since it has the lowest cost.

Peptone addition resulted in maximum c-PGA production(1.96%), followed by yeast extract (1.84%), soybean cake (1.79%),corn flour (1.32%), wheat bran (1.28%) and rapeseed cake (1.14%);however, since soybean cake, corn flour, wheat bran and rapeseedcake are the least costly of the nitrogen sources, these materialswere selected for further experiments.

The influence of Mn2+, Mg2+, Fe3+ and Ca2+ on c-PGA biosynthe-sis, stereochemical compositions and molecular weight has alreadybeen established (Ashiuchi et al., 1999; Shih and Van, 2001; Wuet al., 2006), and our date showed a positive effect of the additionof MgSO4�7H2O, MnSO4�H2O and FeCl3�6H2O on c-PGA production(44%, 47% and 23% compared to the control, respectively). How-ever, CaCl2�2H2O supplemented to the medium did not enhancec-PGA production significantly likely because this element was al-ready present in saturating amounts in the other materials.

Increasing the inoculum concentration from 2.5% to 7.5% (v/w),resulted in an increase in c-PGA production from 1.23% to 2.43%.Higher levels of inoculation resulted in decreased yields. The c-PGA production reached 2.62% at a 50% moisture level, but waslower at lower moisture levels. Higher moisture levels then to re-duce yield as oxygen transfer becomes difficult (Rai et al., 2009). Xuet al. (2005b) reported their highest c-PGA production at an initialmoisture level of 60–65% with fresh cattle manure as substrate.The difference might be due to the smaller particle size of theDMC used in the current study. Maximum production (2.64%) ofc-PGA was achieved at 37 �C. This observation is consistent withthe results of Shi et al. (2006) and Chen (2005), who reported thatthe maximum c-PGA production of 58.2 g/L in SMF and 4.5% (todry weight) in SSF, respectively, at 37 �C. Maximum c-PGA produc-tion of 2.62% was obtained at an initial pH of 7.0. Previous studieshave shown pH 6.5 to be optimal (Bajaj et al., 2009). This differencemight be due to the differences in the experimental substrates andbacterial species.

3.2. Optimization by Plackket–Burman design, path of steepest ascentand central composite design experiments

Based on the results of single-factor experiments, the impor-tance of eight variables, namely wheat bran (X1), soybean cake(X2), rapeseed cake (X3), corn flour (X4), MGPR (X5), citric acid(X6), MgSO4�7H2O (X7) and MnSO4�H2O (X8), for c-PGA productionwas investigated by PBD. Table 1 showed that c-PGA production

Table 3Statistical analysis of Plackket–Burman design.

Variablesa Effect Coefficient SE-coefficient T-value P-value

Constant 2.34917 0.08203 28.64 0X1 0.32833 0.16417 0.08203 2 0.139X2 0.655 0.3275 0.08203 3.99 0.028*

X3 0.285 0.1425 0.08203 1.74 0.181X4 0.93167 0.46583 0.08203 5.68 0.011*

X5 0.705 0.3525 0.08203 4.3 0.023*

X6 �0.19167 �0.09583 0.08203 �1.17 0.327X7 �0.08833 �0.04417 0.08203 �0.54 0.628X8 �0.03167 �0.01583 0.08203 �0.19 0.859R2 96.2%Adj R2 86.0%

a X1, wheat bran; X2, soybean cake; X3, rapeseed cake; X4, corn flour; X5, MGPR;X6, citric acid; X7, MgSO4�7H2O; X8, MnSO4�H2O.

* Significant at P < 0.05.

Table 4Analysis of variance (ANOVA) for CCD.

Terma Coefficient Degree offreedom

Sum ofsquares

F-value

P-value

Model 4.400 9 2.10 100.41 <0.0001**

X1 0.010 1 0.001 0.60 0.4560X2 0.027 1 0.01 4.35 0.0635X3 0.170 1 0.41 174.78 <0.0001**

X1X2 �0.026 1 0.006 2.37 0.1546X1X3 �0.071 1 0.041 17.47 0.0019**

X2X3 �0.001 1 0.00001 0.005 0.9430

X21

�0.200 1 0.55 236.27 <0.0001**

X22

�0.089 1 0.11 49.29 <0.0001**

X23

�0.290 1 1.20 517.43 <0.0001**

Residual 10 0.023Lack-of-

fit5 0.019 4.60 0.0596

Pureerror

5 0.004

Total 19 2.12Std.

Dev.0.048 R2 0.9891

Mean 4.01 Adj R2 0.9792C.V.% 1.2 Pred R2 0.9281

a X1, soybean cake; X2, corn flour; X3, MGPR.** High significant at P < 0.01.

X. Yong et al. / Bioresource Technology 102 (2011) 7548–7554 7551

varied from 1.09% to 3.38% throughout the twelve runs, whichindicated that optimization of the media was important for theproduction of c-PGA.

Based on the statistical analysis (Table 3), the factors having thegreatest impacts on the production of c-PGA were identified as X2

(soybean cake; P = 0.028), X4 (corn flour; P = 0.011) and X5 (MGPR;P = 0.023). The F and P values obtained based on the Fisher’s test forANOVA were 9.42 and 0.046, respectively, suggesting the modelwas statistically significant at a 95.4% level of significance. Thecoefficient of determination (R2), which expressed the quality offit for the polynomial model equation, was 0.962, indicating that96.2% of the variability in the response could be explained by themodel. In addition, the Adj R2 (86.0%) also confirmed a high signif-icance of the model.

Soybean cake (X2), corn flour (X4) and MGPR (X5) showed signif-icantly positive effects on c-PGA production (Table 3). Thus,increasing the concentration of these three variables should resultin a higher production of c-PGA. X2 increased from 1.6 g to 2.4 gwith a step change value of 0.16 g, X4 from 0.4 g to 0.8 g with a stepchange value of 0.08 g, and X5 from 1.6 to 2.8 g with a step changevalue of 0.24 g, respectively. The production of c-PGA (4.28%) wasnearly optimal when the concentration of soybean cake (X2), cornflour (X4) and MGPR (X5) were 1.92, 0.56 and 2.04 g, respectively.This observation suggested that this point might be near the regionof maximum c-PGA production.

The central composite design (CCD) was further employed toinvestigate the interactive effects of soybean cake, corn flour andMGPR (newly designated as X1, X2 and X3, respectively) on the pro-duction of c-PGA. Twenty experiments including six center pointswere conducted with three independent variables at five levels(�1.68, �1, 0, +1 and +1.68). The design matrix of the variables to-gether with the experimental responses is shown in Table 2.

By applying the multiple regression analysis on the experimen-tal data, the following second-order polynomial equation was de-rived to explain the c-PGA production:

Y ¼ 4:40þ 0:010X1 þ 0:027X2 þ 0:17X3 � 0:026X1X2

� 0:071X1X3 � 0:00125X2X3 � 0:20X21 � 0:089X2

2

� 0:29X23 ð2Þ

where Y is the predicted c-PGA production, X1 is soybean cake, X2 iscorn flour and X3 is MGPR.

The ANOVA analysis indicated that the model linear term coef-ficients of X1 and X2 and the cross-product coefficients of X1X2 andX2X3 were not significant (P > 0.05) (Table 4). On the contrary, thelinear term coefficient of X3, the quadric term coefficients of X2

1, X22

and X23 and cross-product coefficient of X1X3 had significant effects

on c-PGA production, so the second-order polynomial equationwas modified to be:

Y ¼ 4:40þ 0:17X3 � 0:071X1X3 � 0:20X21 � 0:089X2

2 � 0:29X23

ð3Þ

The ANOVA of the quadratic regression model showed that Eq.(3) reflected a highly significant model, as was also evident fromthe F and P values, which were 100.41 and <0.0001, respectively(Table 4). The goodness of fit of the quadratic regression modelcould be examined by the R2. Joglekar and May suggested thatR2 should be at least 0.80 for a good fit of a response model(Mirhosseini et al., 2009). In our case, the value of R2 (0.9891)indicated that 98.91% of the variability in the response could beexplained by the model, which showed a good correlation betweenobserved and predicted responses. The value of Adj R2 (0.9792) wasalso very high to satisfy the high significance of the model. Besides,the predicted R2 value of 0.9281 for c-PGA production was inagreement with the Adj R2. These results indicated that theresponse equation provided a suitable and reasonable model forthe CCD experiment. The model also showed a statistically non-significant lack of fit (P = 0.0596), so the model was supposed tobe adequate for prediction within the range of variables employed(Raza et al., 2010b). Besides, a relatively low value of the coefficientof variation (CV = 1.20%) (Table 4) indicated a good precision andreliability of the experiments.

The relationships between variables and responses can be betterunderstood by examining the planned series of three-dimensional(3D) response surface plots obtained from the predicted model withDesign-Expert software. These plots were generated for the pair-wise combination of the three variables while keeping the third at‘‘zero’’ levels. Fig. 1A shows the interaction between soybean cakeand corn flour while keeping MGPR at a zero level. The results dem-onstrated that with the increase of soybean cake and corn flour to1.92 g and 0.57 g, respectively, c-PGA production increased to4.40%, and decreased thereafter. The interactive effect of soybeancake and MGPR was demonstrated in Fig. 1B, where corn flour waskept at a zero level. The c-PGA production increased to the optimumlevel (4.42%) with the increase in soybean cake and MGPR. After that,it declined despite the increase in the concentration of soybean cakeand MGPR, which was in agreement with the model. Fig. 1Cexpresses the 3D plot between corn flour and MGPR where thesoybean cake concentration was kept at 1.92 g, which showedc-PGA production could be increased with the increase in the

Fig. 1. Three-dimensional (3D) response surface plot for c-PGA production as a function of corn flour, soybean cake and MGPR.

7552 X. Yong et al. / Bioresource Technology 102 (2011) 7548–7554

concentration of corn flour and MGPR. Corn flour (0.57 g) and MGPR(2.15 g) contributed to the maximum c-PGA production of 4.43%,beyond which a decrease in c-PGA production was observed.

In conclusion, the model predicted a maximum c-PGA produc-tion of 4.43% after a 48-h incubation in the optimum fermentationmedium containing 5.51 g DMC, 1.91 g soybean cake, 0.57 g cornflour, 2.15 g MGPR, 1.5 g wheat bran, 0.5 g rapeseed cake, 0.1 g cit-ric acid, 0.05 g MgSO4�7H2O and 0.03 g MnSO4�H2O, and with aninoculum concentration, initial moisture, incubation temperatureand initial pH of 7.5%, 50%, 37 �C and pH 7.0, respectively.

3.3. Experimental validation of the optimized condition

To confirm the predicted results and the model adequacy, sixadditional validation experiments in flasks containing 20 g of SSFmedium with the optimum compositions were performed. Theactual average c-PGA production was 4.37 ± 0.053% (in 20 g SSFsubstrates), which agreed well with the predicted value, indicatingthat the suggested model was valid for predicting c-PGAproduction.

Submerged fermentation (SMF) using 20–30 g/L of L-glutamicacid (Bajaj and Singhal, 2011) and citric acid (Xu et al., 2005a) asthe precursor have produced 10–50 g/L c-PGA (Kubota, 1993;Kunioka, 1997; Ogawa et al., 1997; Xu et al., 2005a). Although

these production levels are impressive, the relatively high costsof media constituents in SMF make it an expensive process.

SSF offers an opportunity to reduce energy requirements andcosts of media in the production of c-PGA. B. licheniformis NCIM2324 (Bajaj et al., 2008) yielded a maximum c-PGA of76.18 mg gds�1 (gds, gram of dried fermented matter), but rela-tively expensive media components (glycerol, ammonium sulfate,L-glutamic acid and citric acid) were used. Production cost can bereduced if suitable agro-industrial wastes and by-products can beutilized as raw material for the fermentation process, which wouldalso certainly help in the eco-friendly treatment with value addi-tion especially in developing countries (Rai et al., 2009). In thepresent study, agro-industrial wastes and by-products, such asDMC, MGPR, corn flour and soybean cake were selected as theSSF substrates. Under the optimized condition, the maximum c-PGA production of 4.37% (to total fresh weight) was obtained,which was higher than the results obtained by other investigators(Chen, 2005; Wang et al., 2008; Xu et al., 2005b).

3.4. Time courses of c-PGA production and the number of viable cellsunder the optimal medium composition

Fig. 2 shows the time courses of the number of viable cells andc-PGA production in 5-L flasks containing 500 g of SSF substrate

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 5 10 15 20 25 30 35 40 45 50 55 60Incubation period (h)

γ-P

GA

pro

duct

ion

(%)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Num

ber

of v

iabl

e ce

lls (

log

cfu/

g)

Fig. 2. The time courses of the number of viable cells (j) and c-PGA production (�)under the optimal medium composition.

Fig. 3. SDS–PAGE of the c-PGA produced by B. amyloliquefaciens C1 at differentperiods of solid-state fermentation under the optimal medium composition.

X. Yong et al. / Bioresource Technology 102 (2011) 7548–7554 7553

using the optimal composition of reagents. An exponential growthphase was observed from 10 to 25 h with a rapid increase in bio-mass. The number of viable cells reached a maximum of4.2 � 1010 CFU/g substrates at 25 h, followed by a stationary phase.The increase in c-PGA production closely followed the growth pat-tern of C1, which reached a maximum of 4.40% (in 500 g sub-strates) at 35 h when C1 had established itself well in the deeperlayers of the solid medium. The balanced nutrients available tothe biomass contributed to the maximum production of c-PGA.

However, there was a remarkable decrease in c-PGA productionafter 35 h. This decrease might be caused by c-PGA degradation ofc-PGA depolymerase (Troy, 1973; Yao et al., 2009), which was ex-creted by C1 during the late stationary phase when nutrients in SSFsubstrate sharply decreased with the rapid growth of C1.

3.5. SDS–PAGE of the c-PGA

B. amyloliquefaciens C1 synthesized high molecular mass c-PGA(more than 130 kDa) throughout the whole 70 h by SSF in theoptimal medium (Fig. 3). The molecular weight of c-PGA is criticfor its application, since different purposes request differentmolecular sizes of c-PGA (Ashiuchi et al., 2003).

4. Conclusion

Media optimization by RSM resulted in a maximum c-PGA pro-duction up to 4.37% of the substrate after 48-h of solid state fer-

mentation. This c-PGA production is the highest yield obtainedin solid-state fermentation using agro-industrial wastes as sub-strates. The molecular weight of the produced c-PGA was morethan 130 kDa. Our findings suggest the utility of composted cowmanure and monosodium glutamate production residues as sub-strates for the economical production of a higher-value product.

Acknowledgements

The study was financially supported by Chinese Ministry of Sci-ence and Technology (2007CB109304 and 2011CB100503) and byChinese Ministry of Agriculture (201103004).

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