**4. Conclusion**

**Factor Sum of squares df Mean square** *F***-value** *p***-value**

(Glucose) 14.99 1 14.99 16.51 0.0048

<sup>2</sup> 6.52 1 6.52 7.18 0.0315

(CSL) 57.85 1 57.85 63.73 0.0001

<sup>2</sup> 1.06 1 1.06 1.17 0.3160

(Inoculum size) 2.98 1 2.98 3.28 0.1131

<sup>2</sup> 4.98 1 4.98 5.48 0.0517

X<sup>2</sup> 0.12 1 0.12 0.13 0.7317

X3 1.29 1 1.29 1.42 0.2724

X3 4.14 1 4.14 4.56 0.0701

Model 90.63 9 10.07 11.09 0.0022

of 0.90 proved that the model was significant. It has been suggested

Error 3.000E-004 3 1.000E-004

should be less or equal to 80% for the good fit of a model [39].

The optimum values of the three factors selected for the fermentation process were obtained by solving Eq. (2) using the Design-Expert software package (version 10.0). The optimal condition was statistically predicted as glucose concentration of 78.40 g/L, CSL of 15.00% w/v, and inoculum size of 2.70% v/v. Under this condition, the AC concentration predicted was 10.73 g/L. In order to validate the model, the optimal condition values were applied to three independent experimental replicates and the average value of AC produced was 10.70 ± 0.1 g/L. The correlations between predicted and experimental values after optimization infer the validity of the response model and the existence of an optimum point [40]. The bar chart (**Figure 6**) is a graphical view for each optimal solution showing the desirability of every dependent and independent factor with combined value. Independent factors are shown with red bars, while the dependent response and combined values are displayed in blue. The desirability result is accurate as it falls within the acceptable value ranging between 0.8 and 1 [41]. In **Table 4**, variance inflation factor (VIF) obtained showed that the center points are orthogonal to all other factors in the model. The 95% confidence interval (CI) bounds showing high and low help to hypothesize that there is 95% probability of including the right predicted responses by the model, and there is only 5% chance that the observed value lies either below or above the level of confidence limits. The coefficient estimate shows the confidence interval

Total sum of squares 96.99 16

**Table 3.** Test of significance for every regression coefficient and ANOVA.

X1

94 Renewable Resources and Biorefineries

X1

X2

X2

X3

X3

X1

X1

X2

ANOVA

that *R*<sup>2</sup>

*R*<sup>2</sup> = 0.93, Adjusted *R*<sup>2</sup> = 0.90

[28, 38]. The adjusted *R*<sup>2</sup>

**3.4. Model validation**

The feasibility of corn steep liquor to replace yeast extract and beef extract, which is an expensive nutrient source in acetoin fermentation, was investigated. Corn steep liquor—a low-cost nitrogen source—competes with other complex nutrients (yeast extract and beef extract) for acetoin production and statistical optimization was carried out in batch fermentation. The model that best described the AC fermentation process was a quadratic model with *R*<sup>2</sup> of 0.930. The most significant positive factor for the process was glucose concentration and corn steep liquor, while inoculum size was an insignificant factor in the AC fermentation. Optimal condition predicted for the three independent factors were a glucose concentration of 78.40 g/L, CSL of 15% w/v, and inoculum size of 2.70% v/v, which were validated experimentally with AC concentration of 10.70 ± 0.1 g/L. Based on study results, it can be concluded that the optimization methodologies developed were effective in ascertaining the amount of CSL required for commercial acetoin production, and it reduced the cost, time, and effort associated with experimental techniques.

[2] Zhang L, Chen S, Xie H, Tian Y, Hu K. Efficient acetoin production by optimization of medium components and oxygen supply control using a newly isolated *Paenibacillus polymyxa* CS107. Journal of Chemical Technology and Biotechnology. 2012;**87**:1551-1557.

Statistical Optimization of Acetoin Production Using Corn Steep Liquor as a Low-Cost Nitrogen…

http://dx.doi.org/10.5772/intechopen.79353

97

[3] Li L, Wei X, Yu W, Wen Z, Chen S. Enhancement of acetoin production from *Bacillus licheniformis* by 2, 3-butanediol conversion strategy: Metabolic engineering and fermentation control. Process Biochemistry. 2017;**57**:35-42. DOI: 10.1016/j.procbio.2017.03.027 [4] Luo Q, Wu J, Wu M. Enhanced acetoin production by *Bacillus amyloliquefaciens* through improved acetoin tolerance. Process Biochemistry. 2014;**49**:1223-1230. DOI: 10.1016/j.

[5] Zhang X, T-w Y, Lin Q, M-j X, H-f X, Xu Z-h, et al. Isolation and identification of an acetoin high production bacterium that can reverse transform 2, 3-butanediol to acetoin at the decline phase of fermentation. World Journal of Microbiology and Biotechnology.

[6] Xiao Z, Wang X, Huang Y, Huo F, Zhu X, Xi L, et al. Thermophilic fermentation of acetoin and 2, 3-butanediol by a novel Geobacillus strain. Biotechnology for Biofuels. 2012;**5**:88.

[7] Nielsen DR, Yoon SH, Yuan CJ, Prather KL. Metabolic engineering of acetoin and meso-2, 3-butanediol biosynthesis in *E. coli*. Biotechnology Journal. 2010;**5**:274-284. DOI:

[8] Xu Q, Xie L, Li Y, Lin H, Sun S, Guan X, et al. Metabolic engineering of *Escherichia coli* for efficient production of (3R)-acetoin. Journal of Chemical Technology and Biotechnology.

[9] Sun J, Zhang L, Rao B, Han Y, Chu J, Zhu J, et al. Enhanced acetoin production by *Serratia marcescens* H32 using statistical optimization and a two-stage agitation speed control strategy. Biotechnology and Bioprocess Engineering. 2012;**17**:598-605. DOI: 10.1007/

[10] Teixeira RM, Cavalheiro D, Ninow J, Furigo A Jr. Optimization of acetoin production by *Hanseniaspora guilliermondii* using experimental design. Brazilian Journal of Chemical

[11] Tian Y, Fan Y, Zhao X, Zhang J, Yang L, Liu J. Optimization of fermentation medium for acetoin production by *Bacillus subtilis* SF4-3 using statistical methods. Preparative Biochemistry and Biotechnology. 2014;**44**:529-543. DOI: 10.1080/10826068.2013.835731

[12] Liu D, Chen Y, Ding F, Guo T, Xie J, Zhuang W, et al. Simultaneous production of butanol and acetoin by metabolically engineered *Clostridium acetobutylicum*. Metabolic

[13] Roncal T, Caballero S, de Guereñu MMD, Rincón I, Prieto-Fernández S, Ochoa-Gómez JR. Efficient production of acetoin by fermentation using the newly isolated mutant

Engineering. 2002;**19**:181-186. DOI: 10.1590/S0104-66322002000200014

Engineering. 2015;**27**:107-114. DOI: 10.1016/j.ymben.2014.11.002

DOI: 10.1002/jctb.3791

procbio.2014.05.005

DOI: 10.1186/1754-6834-5-88

2015;**90**:93-100. DOI: 10.1002/jctb.4293

10.1002/biot.200900279

s12257-011-0587-4

2011;**27**:2785-2790. DOI: 10.1007/s11274-011-0754-y
