**2.4. Experimental design by Box-Behnken Design**

A three-level factor was employed to generate 17 experimental runs by considering the effect of glucose concentration (g/L), inoculum size (% v/v), and corn steep liquor (% w/v). The range and the levels of the independent variables investigated using the Box-Behnken experimental design (**Table 1**) were chosen based on variables previously reported to influence acetoin [11, 26]. The minimum, center point, and maximum levels of each variable were coded as −1, 0, and +1, respectively.

A second-order mathematical equation, including all interaction terms, was used to calculate the predicted response:

$$Y = b\_0 + \sum\_{l=1}^{k} b\_l X\_l + \sum\_{l=1}^{k} b\_{l\bar{l}} X\_l^2 + \sum\_{l \neq l}^{k} b\_{l\bar{l}} X\_l X\_{\bar{l}} + e \tag{1}$$

was investigated graphically. Statistical evaluation of the model was carried out using analy-

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

Reducing sugar concentration was analyzed using the dinitrosalicylic acid (DNS) method [27] and the results were expressed as glucose equivalent. To 1 mL of the supernatant, 3 mL of the DNS solution was added in the test tube and boiled for 15 min, cooled, and diluted appropriately after which the absorbance was measured at a wavelength of 540 nm using a

Dry cell weight (DCW) was obtained by centrifuging an aliquot of the sample followed by drying the cell pellet to a constant weight using an electric oven (Scientific, series 2000), in a

tor. The biomass concentration was calculated on the basis of the volume of the fresh sample as the difference between the weight of the empty tube and the final weight of the tube plus

Acetoin was determined by the modified Voges-Proskauer (VP) reaction of Westerfield [30].

ture to volume and shaking vigorously, the solution was kept at 30°C. The color intensity of the complex was determined by measuring the absorbance after 40 min at 530 nm using a

The study in **Figure 2** shows that CSL supports rapid utilization of the reducing sugar (from 150 to 84 g/L) within the first 60 h of fermentation [32], the resultant acetoin and biomass growth being maximum during this period. The maximum biomass growth is found to be fairly constant at ~ 8 g/L until the 144 h when it starts to decline. Likewise, the maximum acetoin concentration is found to be ~ 7 g/L after 60 h of fermentation and then declines. The decline in the acetoin concentration could be attributed to the complete metabolism of glucose in the fermentation medium. Although the biomass concentration remained constant after the 60 h of fermentation, it can be assumed that the energy derived from reducing sugar metabolism was channeled toward cell maintenance since biomass growth remained constant and acetoin was still produced till the fermentation lapse. It has been shown previously that CSL is a rich source of proteins, amino acids, minerals, vitamins, and trace elements and can

**3.1. Preliminary evaluation of complex nitrogen sources on AC production**

C for approximately 24 h. It was later cooled in a desicca-

of creatine solution were added. After adjusting the mix-

calibrated flask. A total of 2.5 cm3

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

89

sis of variance (ANOVA).

**2.6. Analytical methods**

*2.6.1. Reducing sugar analysis*

UV–Visible Spectrometer (GBC Cintra 2020).

the dried biomass after drying and cooling [28, 29].

An aliquot of the sample solution was pipetted into a 25 cm3

UV–Visible Spectrometer of 2020 GBC Cintra model [31].

*2.6.2. Biomass concentration determination*

pre-weighed centrifuge tube, at 105°

*2.6.3. Acetoin concentration determination*

of 1-naphthol solution and 1.0 cm3

**3. Results and discussion**

where Y is response variable (acetoin concentration), b0 is the intercept value, b<sup>i</sup> (i = 1, 2…k) is the first-order model coefficient, bij is the interaction effect, and bii represents the quadratic coefficients of X<sup>i</sup> . X<sup>i</sup> and Xj are the input variables that influence the response variable and *e* represents the random error.

### **2.5. Statistical analysis**

The observed data were subjected to multiple regression analysis using Design-Expert versions 10.0 (Stat Ease Inc., Minneapolis, USA) to obtain the coefficients of the quadratic equation. The *F*-value and the probability *p*-value were used to appraise the significance of the model. The coefficient of determination (*R*<sup>2</sup> ) and adjusted *R*<sup>2</sup> was calculated to evaluate the performance of the regression equation. The behavior of the model in the experimental area


**Table 1.** Factors and their levels for Box-Behnken design.

was investigated graphically. Statistical evaluation of the model was carried out using analysis of variance (ANOVA).
