succinic acid is a member of the four - carbon dicarboxylic acid family in tricarboxylic acid cycle ( tca ) and has a wide range of applications in the fields of surfactants , green solvents , and pharmaceutical intermediates as an important four - carbon intermediate compound . succinic acid is classified as the most promising chemical among 12 biobased chemicals by the u.s . succinic acid has been conventionally produced from fossil raw materials for more than a century , which can lead to high cost , feedstock exhaustion , and environment pollution [ 3 , 4 ] . recent studies suggest that the production of succinic acid by microbe fermentation has the potential to solve the aforementioned problems . as the endproduct of energy metabolism , succinic acid can be produced by many anaerobic microbes , such as actinobacillus succinogenes , anaerobiospirillum succiniciproducens , mannheimia succiniciproducens , and escherichia coli especially a. succinogenes atcc 55618 , which is a facultative anaerobe isolated from the bovine rumen . it could produce higher concentration succinic acid as a major endproduct than other strains without gene reconstruction and glucose feeding . however , to obtain a high production of succinic acid ( i.e. , more than 45 g  l ) , the fermentation medium compositions were often complicated with the addition of high concentration of glucose and ten kinds of slight vitamins [ 810 ] . according to the aforementioned reports , about twenty kinds of components were added into the fermentation medium for the succinic acid production , which led to high cost and complex operation . it must be paid attention that the complicated medium is not the assurance of high production of succinic acid [ 1113 ] . so the optimization of fermentation medium of a. succinogenes atcc 55618 is essential . since nine components existed in the fermentation medium ( i.e. , glucose , yeast extract ( ye ) , corn steep liquor ( csl ) , kh2po4 , k2hpo4 , nacl , mgcl2 , cacl2 , and mgco3 ) , a great number of experiments should be simultaneously run , and the possible interactions between these components were also needed to be studied . for fermentation medium optimization , plackett - burman design ( pbd ) was a good choice in rapid screening multifactors to find the most significant independent factors . moreover , to optimize the concentration of the important factors , statistical methods have been done by using response surface methodology ( rsm ) , in which several factors were simultaneously varied . compared with the one - factor - at - a - time method , statistical experimental design has the advantages of reducing experiment numbers and improving statistical interaction analysis [ 1618 ] . for the reasons above , we proposed the optimization method including the following steps : ( 1 ) using a two - level pbd to identify the key medium components and their experimental levels for further optimization , ( 2 ) applying the path of steepest ascent ( sa ) to move rapidly towards the neighborhood of the optimum response , and ( 3 ) employing a box - behnken design ( bbd ) to develop mathematical models to estimate the relationships between the response ( i.e. , the succinic acid production ) and the key components . the strain of a. succinogenes atcc 55618 , purchased from american type culture collection ( atcc ) , was maintained in 20% glycerol at 70c . the plate was inoculated with the strain maintained at 70c and incubated at 37c for 2 days . preculture medium consisted of the following components ( g  l ) : tryptone 17 ; soya peptone 3 ; glucose 2.5 ; nacl 5 ; k2hpo4 2.5 . for the first preculture , 50  ml of medium was prepared in a 250-ml anaerobic bottle , and a colony from a plate culture was inoculated , and then incubated for 12 hours at 37c on a rotary shaker at 120  rpm . for the second preculture , 47.5  ml of medium was prepared in a 250-ml anaerobic bottle , inoculated with 2.5  ml of the first preculture broth , and incubated for 12 hours at 37c on a rotary shaker at 120  rpm . g  l ) : glucose 107 ; yeast extract ( ye ) 16 ; corn steep liquor ( csl ) 12 ; kh2po4 3 ; k2hpo4 1.5 ; nacl 1 ; mgcl2 0.3 ; cacl2 0.3 ; mgco3 40 . the fermentation medium was inoculated with 2.5  ml of the second preculture broth in 47.5  ml of medium in a 250-ml anaerobic bottle with co2 as the gas phase . . the dynamic profile of the succinic acid biosynthesis of a. succinogenes was monitored during the submerged fermentation process . each data point was expressed by an average with an error bar ( i.e. , standard error from three independent samples ) . the purpose of plackett - burman design ( pbd ) was to identify the key medium components for the succinic acid production . two dummy variables , whose levels did not change in the design , were introduced to estimate the population standard error . the first order was used to fit data from pbd as follows : 
 ( 1)y=0+ixi , 
 where y is the predicted response , 0 and i are constant coefficients , and xi is the coded independent factors . first , the effect of all variables , including dummies , was calculated as follows : 
 ( 2)evi=rvi(+)rvi()n/2 , 
 where evi is the effect of variable i ; rvi(+ ) and rvi( ) represent the response parameter ( i.e. , the succinic acid production ) of an assembly in the screening design which contains the high and low levels of variable i , respectively ; n is the number of assemblies ( in this study , n = 12 ) . after determining the effect of each variable , the standard deviation ( sd ) of dummies , which serves as the population standard deviation in the student 's t - test as follows was calculated : 
 ( 3)sd=(ed)2n , 
 where ed is the effect of dummy variables ; n is the number of dummy variables ( in this study , n = 2 ) . finally , a student 's t - test was performed to identify the significant t factors as follows : 
 ( 4)tvi = evisd . the larger the absolute value of the t - value , the variable is more significant . the method of steepest ascent ( sa ) design was used to move rapidly towards the neighborhood of the optimum response . the experiments were adopted to determine a suitable direction by increasing or decreasing the concentrations of variables according to the results of pbd . a 15-run box - behnken design ( bbd ) with three center points was employed in this work , and the maximal values of the productions of succinic acid were shown in table 3 . to describe the relationships between the succinic acid production and the medium components , a mathematical model was developed by the following second - order polynomial equation to fit data in rsm of bbd : 
 ( 5)y=0+ixi+iixi2+i=1nijxixj+  ij ;  i , j=1,2,3  , 
 where y is the predicted response , 0 is the offset term , i is the linear effect , ii is the quadratic effect , ij is the interaction effect , and  is an experimental error . and xi and xj represent the independent variables ( medium components ) in the form of coded values as follows : 
 ( 6)xi = xixxi  i=1,2,3 , 
 where xi and xi are the dimensionless and the actual values of 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 . the model of response was expression in terms of code variables and without the statistically insignificant terms ( different models are available in bbd of statistical software package minitab 15 , minitab inc . , state college , pa 16801 - 3008 , usa ) . standardized pareto charts , representing the estimated effects of parameters and parameter interactions on responses , permitted us to check the statistical significance of the pbd . the student 's t - test permitted to check the statistical significance of the regression coefficients . the analysis of variable ( anova ) was performed on experimental data to evaluate the statistical significance of the model . the models of each response were expressed in terms of code / uncoded variables and without the statistically insignificant terms . a p value less than 5% indicates that model terms are significant , while less than 1% indicates that model terms are highly significant . a p value is a measure of how much evidence one has against the null hypothesis ( the null hypothesis of the test read like this : h0 : 1 = 2 =  = i = 0 ; h1 : at least one  is not zero ) and evidence against null hypothesis is more for smaller p value . a p value of 0.05 or less rejects the null hypothesis at the 5% level , that is , only 5% of the probability the supposed statistical model will fail to predict the response . the model adequacies were checked by the determination coefficients ( r ) , adjusted r , and predicted r. the determination coefficient ( r ) is a measure of how well the regression equation fits the sample data . while r can be made larger simply by adding more predictor variables to the model , an adjusted r has been proposed . adjusted r is a modification of r that adjusts for the number of explanatory terms in a model . a predicted r is used to measure the amount of variation in new data ( i.e. , other levels among the tested maximal value and minimal value of the factor ) explained by the model . it is calculated by systematically removing each observation from the data set , estimating the regression equation , and determining how well the model predicts the removed observation . predicted r is used in regression analysis to indicate how well the model predicts responses for new observations , whereas r indicates how well the model fits the data . predicted r can prevent overfitting the model and can be more useful than adjusted r for comparing models because it is calculated using observations not included in model estimation . predicted r larger values of predicted r squared suggest models of greater predictive ability the  minitab 15 ( minitab inc . , state college , pa 16801 - 3008 , usa ) software was employed for the regression analysis and the graphical optimization , respectively . for sampling , three flasks were taken each time , and cell growth was monitored by measuring the optical density at 660  nm ( od660 ) . at an od660 of 1.0 , a. succinogenes atcc 55618 has a concentration of 0.626  g dry cell weight ( dcw )  l. for succinic acid determination , 1  ml of methanol and 1  ml of acetonitrile were added to 1  ml of fermentation broth to remove proteins , and the sample was kept at 4c overnight . after centrifugation at 11,000  rpm for 30  min , the supernatants were diluted and filtrated through a 0.22-m filter and analyzed by high - performance liquid chromatography ( hplc , waters ) using an agela venusil asb c18 column . the optimized mobile phase was 50  mm kh2po4 water solution , with the ph adjusted to 3.5 by h3po4 . the column oven temperature was maintained at 40c , and the flow rate was 1  ml min . the methodology of plackett - burman design ( pbd ) is a powerful and useful tool in rapidly searching key factors from a multivariable system . pbd does not determine the exact quantity , but it can provide some important information about each factor by relatively few experiments . as shown in table 1 , pbd for 12 trials with two levels of concentrations was undertaken to evaluate the significances of nine medium components . in order to determine the influence of the most important parameters , analysis of the measured response variables enabled to obtain standardized pareto charts and response surface plots . a standardized pareto chart consists of bars with a length proportional to the absolute value of the estimated effects , divided by the standard error . the bars are displayed in order of the size of the effects , with the largest effects on top . the chart includes a vertical line at the critical t - value for an alpha . some investigations find that the confidence levels greater than 80% ( p < 0.2 ) are acceptable . in this study , the chart includes a vertical line at the critical t - value for  of 0.20 . effects for which the bars are smaller than the critical t - value are considered as not significant and not affecting the response variables . effects may be positive or negative .   as shown in figure 1 , for the succinic acid production , the variables glucose , yeast extract , and mgco3 had a confidence level above 80% . hence , these were considered to be significant for the succinic acid biosynthesis . analyzed by minitab , a first - order model , was fitted to the results obtained from the twelve experiments as the equation : 
 ( 7)y(g  l1)=39.09 + 4.18cglucose2.28cye1.84ccsl0.83ck2hpo41.93ckh2po41.24cnacl+1.53cmgcl2 + 1.36ccacl2 + 3.26cmgco3 . 
 where y is the succinic acid production , and cglucose , cye , ccsl , ck2hpo4 , ckh2po4 , cnacl , cmgcl2 , ccacl2 , and cmgco3 are the value of glucose , ye , csl , k2hpo4 , kh2po4 , nacl , mgcl2 , cacl2 , and mgco3 , respectively . the goodness of the regression was checked by the coefficient of determination r whose value ( r = 95.39% ) indicates that only 4.61% of the total variation could not be explained by the model , and it was reasonable to use the regression model to analyze the trend in the response . and then , glucose , yeast extract , and mgco3 were selected for further optimization to obtain a maximum response . according to the coefficients of the glucose ( 4.18 ) , yeast extract ( 2.28 ) and mgco3 ( 3.26 ) , the positive coefficients indicated that the high level of glucose and mgco3 concentration in pbd was benefit for the succinic acid production . on the contrary , the negative coefficient indicated that the low level of yeast extract concentration was helpful for the succinic acid accumulation . a maximum succinic acid production of 51.9  g  l was obtained with 80.0  g  l of glucose , 60.0  g  l of mgco3 , and 10.0  g  l of yeast extract . the corresponding productivity and specific productivity were 1.1  g  l  h and 13.4  g succinic acid g dcw . however , the od660 of 6.2 obtained with this medium was not the highest among the 12 conditions .   according to the metabolic pathway of a. succinogenes , succinic acid was an end - product from the substrate of glucose , which implied that succinic acid formation was decided by the supplement of glucose directly . the yield of succinic acid against glucose was improved by the increase of initial glucose concentration , and it would reach the peak value when the initial glucose concentration was 70.0 the culture ph value is one of the key factors in the production of succinic acid . mgco3 was used as a neutralizing agent and added to the fermentation broth to adjust culture ph . before optimization , the ph value decreased from 7.5 to 5.7 during the fermentation . compared with the ph value of basic medium , the initial ph increased up to 8.0 and was followed by slow decline after the optimization . the final ph value of optimal medium with an initial ph of 7.5 was more close to neutral ( about 6.5 ) during the fermentation , and neutral environment was helpful to accumulate succinic acid . in our previous results , the highest succinic acid production of 48.2 the pattern of succinic acid production at a culture ph of 7.0 was similar with that obtained at 7.5 . when the initial culture ph was adjusted to 6.5 or 8.0 , a significant decrease of succinic acid production was observed . so , the effect of medium optimization mgco3 may be partly from the influence of the ph . on the other hand , sufficient co2 supplement in the fermentation broth could strongly influence the metabolic flux of carbon and the activities of phosphoenolpyruvate ( pep ) carboxykinase , which were the important committed steps for the biosynthesis of succinic acid [ 24 , 26 ] ; therefore , sufficient co2 is another key factor influencing succinic acid accumulation . however , in our previous work , there was no significant effect of co2 partial pressure on the production of succinic acid when gaseous co2 was used as the sole co2 donor . as an important co2 donor in the a. succinogenes fermentation , mgco3 could react with organic acids in fermentation broth and caused an increase in the dissolved concentrations of hco3 , co3 , and co2 . when gaseous co2 was used with mgco3 , higher amount of mgco3 was more effective on promoting the succinic acid synthesis . and the maximum succinic acid production of 61.92 g  l was obtained at 159.22  mm dissolved co2 concentration , which was supplied by 40  g  l mgco3 with 100% co2 gas . this means that the dissolved co2 concentration was another factor affecting succinic acid synthesis . and during the fermentation process , insoluble mgco3 caused turbid broth , which made the cells spread uniformly in the broth . all these properties make mgco3 to be one of the key factors significantly improved the succinic acid production .   at last , yeast extract was also screened to be a key factor because it affected cell growth directly as the nutrient . it contains many trace substances , such as folic acid , pantothenic acid , biotin , and vitamin b1 , b2 , b6 , and b12 . this may be the important reason why many kinds of vitamins could be omitted meanwhile the succinic acid could efficiently be accumulated in this work . to conclude , the variables of glucose , mgco3 , and yeast extract had a confidence level above 80% and hence were considered to significantly influence the succinic acid production . the optimal concentrations of these three key components needed to be further studied as follows . based on the results above , glucose , yeast extract , and mgco3 were the three key factors , and the high level of glucose ( i.e. , 80.0  g  l ) and mgco3 ( i.e. , 60.0  g  l ) , while the low level of yeast extract ( i.e. , 10.0 g  l ) was benefit for the succinic acid production of a. succinogenes atcc 55618 . thus , the path of sa was moved along the path in which glucose and mgco3 increased , while yeast extract decreased ( table 2 ) . the highest succinic acid production of 48.4  1.3  g  l and od660 of 6.1 was observed under the point of levels , namely , 80.0  g  l of glucose , 10.0  g  l of yeast extract , and 60.0  g  l of mgco3 . the corresponding productivity and specific productivity were 1.0  g  l  h and 12.4  g succinic acid g dcw . this result suggested that the point was near the optimal point and was chosen for further optimization . as shown in table 2 , the succinic acid production did not further increase with the increase concentrations of glucose and mgco3 and the decrease concentration of yeast extract . for glucose , although a. succinogenes could tolerate glucose concentration as high as 150.0 g  l , the yield of succinic acid would decrease when glucose concentration exceeds 70.0  g  l [ 9 , 23 ] . g  l , the concentrations of succinic acid could not be further improved . for mgco3 , the culture ph of fermentation broth was increased with the increase of mgco3 concentration ( data not shown ) , which may make the cells more difficult to adapt . , it was observed that the absence of yeast extract led to almost no succinic acid accumulation , which indicated that this component was the basic nutrient for the cell growth . to conclude , the highest succinic acid production was obtained under the condition of 80.0 g  l of mgco3 , which suggested that this point was near the optimal point .   based on the information above , taking the point ( 80.0  g  l of glucose , 10.0  g  l of yeast extract and 60.0  g  l of mgco3 ) as the central level of bbd , then the three - factors , three - level bbd was employed ( table 3 ) . the lack of fit ( lof ) is the variation of the data around the fitted model . lof is a model fit test that is used to check how well a model fits the data and help to remove insignificant terms from the model . a p - value of lof 0.614 indicted that the model fits the response well . the  predicted r  of 0.9696 was in reasonable agreement with the  adjusted r  of 0.9911 , and it might be also said that the model determination coefficient ( r of 0.9968 ) was reasonable agreement with the experimental results , indicating that 99.68% of the variability could be revealed by the model . the polynomial model for the succinic acid production was expressed by ( 8) ( in coded value ) and ( 9 ) ( in uncoded value ) ( table 5 ) consider the following : 
 ( 8)y=49.563 + 1.274a+5.495b+2.346c+1.008ab+0.310ac+2.373bc2.562a24.084b24.972c2(9)y=285.728 + 3.842cglucose0.092cye+5.482cmgco3 + 0.020cglucosecye+0.0031cglucosecmgco3 + 0.047cyecmgco30.026cglucose20.163cye20.050cmgco32 . the model reveals that glucose concentration ( a ) , yeast extract concentration ( b ) , and mgco3 concentration ( c ) had a significant effect ( p < 0.0001 ) on the succinic acid production ( y ) . positive coefficient of a , b , and c indicated a linear effect to increase . the graphs ( figure 2 ) depict the changes in the parameter modeled as the two factors move along those levels , while the other factor held constant at the central point . according to ( 9 ) , the highest succinic acid production of 52.3  g  l was obtained at 14.0  g  l of yeast extract and 64.3  g  l of mgco3 , while glucose concentration was held at 80.0 g  l ; the maximal succinic acid production of 50.0  g  l could be observed at 82.6  g  l of glucose and 62.4  g  l of mgco3 , while holding 10.0  g  l of yeast extract ; under the conditions of 83.9  g  l of glucose , 13.6  g  l of yeast extract and 60.0  g  l of mgco3 ( the factor held constant ) , the succinic acid production could reach its peak value of 51.8  g  l. according to ( 8) and ( 9 ) , it was predicted that a maximum succinic acid production of 53.0  g  l was appeared at 84.6  g  l of glucose , 14.5  g  l of yeast extract , and 64.7  g  l of mgco3 . in a word , the best level combination of the three key factors ( i.e. , glucose , yeast extract , and mgco3 ) was predicted by the polynomial model from bbd . then the reliability of the model and the accuracy of the prediction should be checked by verification experiment . the availability of the regression model of the succinic acid production using the calculated optimal medium compositions , namely , 84.6  g  l glucose , 14.5 g  l yeast extract , and 64.7  g  l mgco3 , was validated with triplicate experiments . the mean maximal value of succinic acid production was 52.7  0.8  g  l , which agreed with the predicted value ( 53.0 the model was considered to be accurate and reliable for predicting the succinic acid production by a. succinogenes . theoretically , 1.71  mol succinate can be produced per mol glucose ( plus co2 ) , based on the available electrons ( i.e. , 24 electrons in glucose divided by 14 electrons in succinate = 1.71 ) . in this work , compared with that of basic medium ( 0.45  mol  mol glucose ) , the yield of succinic acid against glucose was enhanced by 111.1% in optimized medium ( 0.95  mol  mol glucose ) . this may be because there are some factors other than the medium , which affect the yield of succinic acid but are not investigated in this study . other authors have reported that the succinic acid production reached 84  g  l in the fermentation of actinobacillus succinogenes nj113 , in which the medium was with similar components to the basic medium in this work , and the corresponding yield of succinic acid was 1.20  mol  mol glucose . since nj113 is the strain screened by jiang et al , this strain may have greater ability to synthesize succinic acid . to conclude , the best level combination of the three key factors ( i.e. , 84.6  g  l glucose , 14.5  g  l yeast extract , and 64.7  g  l mgco3 ) was obtained by using the statistical experimental design . 0.8  g  l under the optimal medium compositions , which agreed with the predicted value ( 53.0 g  l ) well and was 67.3% higher than that obtained with the basic medium ( i.e. , 31.5  2.3 in this study , a highly efficient optimization method by combining plackett - burman design , steepest ascent , and box - behnken design was developed . this method was demonstrated to be effective in selecting the significant factors and enhancing succinic acid production in a. succinogenes atcc 55618 fermentation . glucose , yeast extract , and mgco3 were screened to be key factors for the succinic acid production . a maximal succinic acid production of 52.7  0.8  g  l was obtained with 84.6  g  l glucose , 14.5  g  l yeast extract , and 64.7  g  l mgco3 . this result agreed with the predicted value well and was 67.3% higher than that obtained with the basic medium .
as a potential intermediary feedstock , succinic acid takes an important place in bulk chemical productions . for the first time , a method combining plackett - burman design ( pbd ) , steepest ascent method ( sa ) , and box - behnken design ( bbd ) was developed to optimize actinobacillus succinogenes atcc 55618 fermentation medium . 
 first , glucose , yeast extract , and mgco3 were identified to be key medium components by pbd . 
 second , preliminary optimization was run by sa method to access the optimal region of the key medium components . 
 finally , the responses , that is , the production of succinic acid , were optimized simultaneously by using bbd , and the optimal concentration was located to be 84.6  g l1 of glucose , 14.5  g l1 of yeast extract , and 64.7  g l1 of mgco3 . 
 verification experiment indicated that the maximal succinic acid production of 52.7  0.8  g l1 was obtained under the identified optimal conditions . 
 the result agreed with the predicted value well . compared with that of the basic medium , the production of succinic acid and yield of succinic acid against glucose 
 were enhanced by 67.3% and 111.1% , respectively . 
 the results obtained in this study may be useful for the industrial commercial production of succinic acid .