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Department of Botany, Utkal University, Vani Vihar, Bhubaneswar, Odisha, India

- *Corresponding Author:
- Kunja Bihari Satapathy

Department of Botany, Utkal University

Vani Vihar, Bhubaneswar-751 004, Odisha, India

**Tel:**+916742567940

**E-mail:**[email protected]

**Received date:** December 26, 2016; **Accepted date:** January 09, 2017; **Published date:** January 16, 2017

**Citation: **Ganguly S, Satapathy KB (2017) Statistical Optimization of Culture Conditions for L-Methionine Production by Corynebacterium glutamicum X300. J Theor Comput Sci 4:150. doi:10.4172/2376-130X.1000150

**Copyright:** © 2017 Ganguly S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

**Visit for more related articles at** Journal of Theoretical & Computational Science

Statistical optimization was done for L-methionine production by Corynebacterium glutamicum X300 using Response Surface Methodology emphasizing Central Composite Design with different variables. Maximum production of L-methionine (52.1 mg/ml) was obtained with 72 h of incubation.

Statistical; Optimization; L-methionine; Response surface methodology; Central composite design

Statistical methods are widely used for fermentation optimization, because they reduce the total number of experiments required and provide a good understanding of the interactions among different factors on the outcome of the fermentation [1]. Response Surface Methodology (RSM) is a group of mathematical and statistical techniques for the optimization of multiple variables and levels in a minimum acceptable number of experimental trials [2]. Taguchi’s method has gained worldwide acceptance in the optimization of fermentation processes [1].

Adinarayana and Ellaiah used RSM for the optimization of the
medium components for the production of alkaline protease by a *Bacillus *sp [3]. Balusu et al. used RSM for the optimization of medium
components for ethanol production from cellulosic biomass by *Clostridium thermocellum* SS19 [4]. Shih and Shen applied RSM to
optimize the production of poly €-lysine by *Streptomyces albulus* IFO
14147 [5]. Nelofer et al. optimized the process variables for L-lysine
production by *Corynebacterium glutamicum* K [6]. Pandey and
Banik optimized different physical parameters for alkaline phosphate
production by *Bacillus licheniformis* using RSM [7].

RSM is advantageous over conventional methods as it requires less number of experiments and its suitability for multiple variable experiments and search for common relationship between different variables towards finding the most suitable conditions for the production of the desired metabolites [8-12]. This methodology could employ to optimize different physic-chemical conditions for L-methionine production.

Thus, the aim of this present investigation was to optimize
different production conditions of L-methionine by the mutant *Corynebacterium glutamicum *X300 controlling different physical and
nutritional parameters using RSM.

**Microorganism**

*Corynebacterium glutamicum *X300 developed by induced mutantion and protoplast fusion was used throughout the study [13].

**Composition of growth medium**

Glucose, 20 g; (NH_{4})_{2}SO_{4}; 1.6 g; NaCl, 2.5 g; MgSO_{4}.7H_{2}O, 0.25 g;
MnSO_{4}.4H_{2}O, 0.1 g; K_{2}HPO_{4}, 1 g; KH_{2}PO_{4}, 1 g; H_{2}O, 1 L and agar, 2%
as a solidifying agent [14].

**Growth conditions**

The fermentation was carried out using medium volume, 30 ml;
initial pH 7; shaker speed, 200 rpm; the age of inoculum, 48 h; cell
density, 3 × 10^{8} cells/ml and temperature, 30°C [14].

**Composition of basal salt medium for L-methionine
production**

L methionine production was initially carried out (before
optimization) using the following basal salt medium: glucose, 60 g;
(NH_{4})_{2}SO_{4}, 1.5 g; K_{2}HPO_{4}, 1.4 g; MgSO_{4}·7H_{2}O, 0.9 g; FeSO_{4}·7H_{2}O, 0.01
g; biotin, 60 μg and H_{2}O, 1 L [14].

**Analysis of L-methionine**

Descending paper chromatography was employed for detecting L-methionine in the broth and was run for 18h on Whatman No.1 chromatography paper. Solvent system contained: n-butanol: acetic acid: water (2:1:1). The spot was visualized by spraying a solution of 0.2% ninhydrin in acetone and quantitative estimation of L-methionine in the suspension was done using a colorimetric method [14].

**Confirmatory test for L-methionine**

Quantitative determination of L-methionine in the fermentation medium without purification was done following the method as described by Greenstein and Wintz. 1 ml of 5(N) NaOH, and 0.1 ml of 10% sodium nitroprosside solution, was added to 5 ml supernatant after centrifugation at 5000 rpm for 15 min. The tube was thoroughly shaken and the mixture was allowed to stand for 10 min. 25 ml of 3% aqueous solution of glycerine was added to the reaction mixture with frequent shaking over a period of 10 min. After additional 10 min interval, 2 ml of concentrated orthophosphoric acid was added drop wise to the mixture and the test tube was properly shaken. Colour development was allowed to produce for 5 min and colour intensity was measured at 540 nm in spectrophotometer (Perkin Elmer Lambda 68 UV VIS). The L-methionine yield was extrapolated from a standard L-methionine curve [15].

**Recovery of L-methionine from fermented broth**

An inexpensive down-stream recovery process that is capable of achieving the requisite recovery yield and purity is essential for producing any metabolite. Various levels of down-stream processing are required for the existing amino acid fermentation. The general approach to designing an efficient recovery scheme for bio products has been elucidated by Chisti and Moo-Young. The production scheme must accommodate the various regulatory requirements and consider the end use application of the product. Purification of L-amino acids relies on their physico-chemical properties, particularly solubility and isoelectric point. As the first step of the down-stream recovery process, the cells are separated from the fermentation broth by either centrifugation or filtration. The cell-free broth is then passed through activated charcoal columns for decolorization. L-methionine (isoelectric pH 5.74) can be recovered from the clarified broth by adjusting the pH to 5 with sulfuric acid to convert the amino acid to its cationic form and passing the broth though a bed of Amberlite IR-120 (H+) ion exchange resin at a controlled flow rate. The process is repeated until all the L-methionine is adsorbed. Afterwards, the column is washed with deionized water and eluted with 1(M) NH4OH to recover the L-methionine. Crystalline L-methionine can be obtained by concentrating under vacuum, treating with absolute alcohol, and drying overnight at 80°C [16-18].

**Estimation of Dry Cell Weight (DCW)**

The cell paste was obtained from the fermentation broth by centrifugation and dried at 1000C until constant cell weight was obtained [17-19].

**Estimation of residual sugar:** Residual sugar was determined by
the DNS method as proposed by Miller [14].

**Statistical analysis: **All data were expressed as mean±SEM, where
n=6, where ‘n’ denotes the number of experimental set up. Data were
analyzed by one way ANOVA using a software Prism 4.0, considering
p<0.05 as significant and p<0.01 as highly significant.

**Response surface methodology: **It consists of a group of
experimental technologies, used for evaluation of relationship between
different variables and measured responses. Plackett-Burmann design
was used to assess the pick variables that influence L-methionine
fermentation by the mutant significantly and insignificant factors were
eliminated in order to obtain a smaller manageable set of variables.
RSM was applied in two stages, first to trace out the significant variables
for the production using Plackett-Burmann design criterion and later
significant variables related from Plackett-Burmann design were
optimized by a central composite design. The experimental design and
statistical analysis of the data were done by using a software, prism 4.0.

**Plackett-Burmann Design (PBD): **Each variable was examined
at two levels, namely a high level (+1) and low level (-1). Initial pH,
volume of medium, age of inoculum, shaker’s speed, temperature,
cell density, period of incubation, carbon source, nitrogen source,
K_{2}HPO_{4}, KH_{2}PO_{4}, CaCO_{3}, MgSO_{4}.7H_{2}O, NaCl, KCl, ZnSO_{4}.7H_{2}O,
Na_{2}MoO_{4}.2H_{2}O, MnSO_{4}.4H_{2}O, FeSO_{4}.7H_{2}O, biotin and thiamine-HCl
were screened by conducting six experiments using Plackett-Burmann
design. All experiments were conducted in six sets and mean values of
L-methionine production was used for statistical analysis. The variables,
which were significant at 1% level (p<0.01) from one way ANOVA
were considered to have high impact on L-methionine production and
were further optimized using Central Composite Design.

**Central Composite Design (CCD):** It was applied to determine
the optimum levels of seven significant production parameters
determined from PBD. The effects of the parameters (namely: age
of inoculums, shaker’s speed, temperature, cell density, glucose
concentration, nitrogen concentration, K_{2}HPO_{4}, KH_{2}PO_{4}, CaCO_{3},
MgSO_{4}.7H_{2}O, FeSO_{4}.7H_{2}O and biotin) on L-methionine production
by the mutant were examined at levels: -2, -1, 0, +1 and +2 where α+ 2^{n/4},
where ‘α+’ represents the number of levels of significance considered
(i.e., 5). Hence ‘n’ was the number of variables and ‘0’ corresponded
to the central point. The level of each variable was determined by the
following equation [20]:

(1)

The experimental plan and the following independent variable were
obtained from CCD: volume of medium, (15-35 ml), initial pH (6-8),
age of inoculum (24-84), cell density (1 × 10^{8}-6 × 10^{8} cells), temperature
(25-32°C), period of incubation (24-108 h), carbon sources (glucose,
fructose, sucrose, lactose, maltose, ribose, xylose, starch, dextrin,
sodium citraite, sodium acetate and glycerol), different concentrations
of glucose (20-140 g/L), nitrogen sources (urea, ammonium sulphate,
sodium nitrate, ammonium chloride, ammonium nitrate, diammonium
hydrogen phosphate, ammonium dihydrogen phosphate, ammonium
carbonate, ammonium oxalate and ammonium citrate) and different
concentrations of nitrogen (1-10 g/L) in the form of ammonium
sulphate

After identification of significant variables using Plackett-Burmann
design, Box-Wilson 2^{4} factorial CCD was applied to optimize these
variables. Five levels of variables were coded as [6]:

(2)

[Where, Z=Code value; X=Natural value; X’=Natural value in
central domain; ȡx=increment of X corresponding to one unit of Z].
A total number of 31 experiments with 8 axial points (α=2) and six
replications was applied for Box-Wilson 2^{4} factorial CCD. The general
form of the second degree polynomial equation was applied in this
present study. The equation can be presented as follows [7]:

(3)

Where, Y=Response variable; β_{o}=Coefficient of interaction
effect (offset term); β_{i}=Linear coefficient (i^{th} term); β_{ii}=Coefficient
of quadratic effect (ii^{th} term); β_{ij}=Interaction coefficient (ij^{th} term).
Analysis of variance (ANOVA) and regression analysis was done using
software Prism 4.0.

**Screening of variables by Plackett-Burmann design
criterion for L-methionine production by Corynebacterium
glutamicum X300**

The variables which significantly affect the production of
L-methionine by *Corynebacterium glutamicum* X300 were determined
by Plackett-Burmann design. All the twenty one parameters such as
volume of medium, initial pH, age of inoculums, shaker’s speed,
temperature, cell density, period of incubation, glucose concentration,
nitrogen concentration (in term of ammonium sulphate), KH_{2}PO_{4},
KH_{2}PO_{4}, CaCO_{3}, MgSO_{4}.7H_{2}O, NaCl, KCl, FeSO_{4}.7H_{2}O, ZnSO_{4}.7H_{2}O,
Na_{2}MoO_{4}.2H_{2}O, MnSO_{4}.4H_{2}O, biotin and thiamine-HCl were
examined at two widely spaced levels (**Table 1**).

Code | Parameter | High level (+1) | Low level(-1) | ||
---|---|---|---|---|---|

Level | Activity (mg/ml) | Level | Activity(mg/ml) | ||

I | Volume of medium (ml) | 35 | 8.8 ± 0.691 | 15 | 7.8 ± 0.881 |

II | Initial pH | 8 | 7.9 ± 0.591 | 6 | 8.3 ± 0.779 |

III | Shaker’s speed(rpm) | 300 | 8.6 ± 0.881 | 100 | 10.8 ± 0.728 |

IV | Age of inoculum (h) | 84 | 4.8 ± 0.689 | 24 | 9.6 ± 0.913 |

V | Cell density (cells) | 6×108 | 11.1 ± 0.881 | 1×108 | 6.3 ± 0.812 |

VI | Temperature (°C) | 32 | 9.1 ± 0.681 | 25 | 4.8 ± 0.791 |

VII | Period of incubation(h) | 108 | 8.6 ± 0.883 | 24 | 1.8 ± 0.681 |

VIII | Glucose concentration (g/L) | 104 | 17.6 ± 0.992 | 20 | 9.6 ± 0.681 |

IX | Nitrogen content (g/L) | 10 | 22.9 ± 1.181 | 1 | 16.1 ± 0.613 |

X | K_{2}HPO_{4}(g/L) |
2.4 | 26.8 ± 1.631 | 1 | 23.9 ± 1.668 |

XI | KH_{2}PO_{4}(g/L) |
2.1 | 33.8 ± 0.691 | 0 | 25.6 ± 0.916 |

XII | CaCO_{3}(g/L) |
3 | 37.3 ± 0.619 | 0 | 34.3 ± 0.793 |

XIII | MgSO_{4}.7H_{2}O(g/L) |
1.8 | 28.1 ± 1.613 | 0.3 | 25.8 ± 0.874 |

XIV | NaCl (g/L) | 2.4 | 42.8 ± 0.689 | 1 | 42.8 ± 0.613 |

XV | KCl(g/L) | 2.4 | 40.8 ± 0.871 | 1 | 42.8 ± 0.932 |

XVI | FeSO_{4}.7H_{2}O (mg/L) |
35 | 29.9 ± 0.993 | 5 | 26.6 ± 1.913 |

XVII | ZnSO_{4}.7H_{2}O (mg/L) |
1.7 | 43.1 ± 0.793 | 0 | 40.2 ± 0.661 |

XVIII | Na_{2}MoO_{4}.2H_{2}O (mg/L) |
8 | 45.8 ± 0.881 | 0 | 44.2 ± 0.692 |

XIX | MnSO_{4}.4H_{2}O (mg/L) |
6 | 46.7 ± 0.882 | 0 | 46.9 ± 0.692 |

XX | Biotin (μg/ml) | 100 | 50.1 ± 0.832 | 0 | 48.1 ± 0.661 |

XXI | Thiamine-HCl (μg/ml) | 100 | 51.2 ± 0.661 | 0 | 50.8 ± 0.913 |

**Table 1: **Level of variables examined for L-methionine production by the mutant *Corynebacterium glutamicum* X300 using Plackett-Burmann design criterion.

Among the twenty-one variables examined, shaker’s speed (III),
age of inoculum (IV), cell density (V), temperature (VI), glucose
concentration (VIII), nitrogen concentration (IX), KH_{2}PO_{4} (X),
KH_{2}PO_{4} (XI), CaCO_{3} (XII), MgSO_{4}.7H_{2}O(XIII), KCl (XV), FeSO_{4}.7H_{2}O (XVI), MgSO_{4}.7H_{2}O (XIX) and biotin (XX) had significant (p<0.01)
effect on L-methionine fermentation by the mutant, which was
obtained from one way ANOVA. The coefficient of determinant (R^{2})
of the model obtained from regression analysis was 0.861, suggesting
thereby the model could explain up to 86.1% variation of the data. Thus
the production of L-methionine by *Corynebacterium glutamicum* X300
using Placket_Burman design showed wide range of variations which
implied that it required further optimization.

**Application of Box-Wilson central composite design
for the optimization of L-methionine production by
Corynebacterium glutamicum X300**

The optimum level of the key variables and the effect of their interactions on L-methionine production by the mutant were further examined using Central Composite Design (CCD) of RSM.

**The first CCD**

The fermentation trials for L-methionine by* Corynebacterium
glutamicum* X300 were examined by CCD using the following variables:
shaker’s speed (III), age of inoculum (IV), cell density (V), temperature
(VI), glucose concentration (VIII), nitrogen concentration (IX),
K_{2}HPO_{4} (X), KH_{2}PO_{4} (XI), CaCO_{3} (XII), MgSO_{4}.7H_{2}O (XIII), KCl (XV),
FeSO_{4}.7H_{2}O (XVI), MnSO_{4}.4H_{2}O (XIX) and biotin (XX). The highest
level of L-methionine was obtained up to 52.1 mg/ml with a biomass of
28.5 mg/ml and residual sugar content of 23.8%. **Table 2** depicted the
results of the second order Response Surface Models for L-methionine
production by the mutant, obtained by one way ANOVA.

Source | Degree of freedom (df) | Sum of square | Mean square | F-value | Probe˃F |
---|---|---|---|---|---|

Model | 82.169 | 7 | 9.313 | 18.313 | 0.0016 |

Residual | 7.321 | 5 | 0.861 | 16.328 | 0.0616 |

Lack of fit | 70.613 | 2 | 0.331 | 18.792 | 0.0516 |

Pure error | 0.168 | 3 | 0.062 | 21.613 | 0.0476 |

Total | 94.316 |

R^{2}=0.9982

**Table 2: **One way ANOVA for full quadratic model used for L-methionine production by *Corynebacterium glutamicum*X300.

Chi square test with a very low probability value (α<0.001)
indicated that the model was highly significant. R^{2} (0.982) indicated
that the sample variation for L-methionine production of 98.2% was attributed to the independent variables and only 1.8% of total variation
cannot be justified by the model (**Tables 3**-**5**).

Trial | Factor | L-methionine (mg/ml) [mean ± SEM] |
Predictedvalue (mg/ml) |
Residual values (mg/ml) |
|||
---|---|---|---|---|---|---|---|

X3 | X4 | X5 | X6 | ||||

1 | 150 | 48 | 6×10^{8} |
31 | 13.6 ± 0.613 | 9.3 | 4.3 |

2 | 100 | 24 | 4×10^{8} |
28 | 11.6 ± 0.998 | 10.1 | 1.5 |

3 | 100 | 72 | 4×10^{8} |
25 | 10.8 ± 0.791 | 7.6 | 3.2 |

4 | 150 | 60 | 3×10^{8} |
32 | 11.9 ± 0.991 | 9.1 | 2.8 |

5 | 100 | 36 | 5×10^{8} |
28 | 13.2 ± 0.668 | 8.6 | 4.6 |

6 | 200 | 84 | 3×10^{8} |
28 | 11.6 ± 0.792 | 9.3 | 2.3 |

7 | 150 | 60 | 6×10^{8} |
27 | 13.3 ± 0.837 | 7.3 | 6.0 |

8 | 200 | 48 | 6×10^{8} |
26 | 12.4 ± 0.991 | 8.1 | 4.3 |

9 | 100 | 48 | 3×10^{8} |
30 | 14.8 ± 0.681 | 8.6 | 6.2 |

10 | 100 | 24 | 6×10^{8} |
29 | 15.0 ± 0.927 | 9.4 | 5.6 |

11 | 150 | 72 | 6×10^{8} |
32 | 14.2 ± 0.883 | 8.1 | 6.1 |

12 | 300 | 60 | 5×10^{8} |
31 | 13.1 ± 0.813 | 7.6 | 5.5 |

13 | 150 | 84 | 4×10^{8} |
31 | 13.6 ± 0.698 | 9.4 | 4.2 |

14 | 100 | 72 | 4×10^{8} |
25 | 11.6 ± 0.735 | 7.8 | 3.8 |

15 | 300 | 72 | 1×10^{8} |
29 | 11.3 ± 0.919 | 6.8 | 4.5 |

16 | 300 | 84 | 3×10^{8} |
60 | 12.2 ± 0.882 | 9.6 | 2.6 |

17 | 200 | 84 | 3×10^{8} |
60 | 13.6 ± 0.881 | 10.9 | 2.7 |

18 | 200 | 72 | 4×10^{8} |
48 | 12.1 ± 0.802 | 9.3 | 2.8 |

19 | 300 | 48 | 5×10^{8} |
96 | 10.8 ± 0.682 | 9.3 | 1.5 |

20 | 150 | 60 | 5×10^{8} |
96 | 11.8 ± 0.792 | 7.6 | 4.2 |

21 | 100 | 48 | 1×10^{8} |
26 | 11.3 ± 0.993 | 9.3 | 2.0 |

22 | 300 | 48 | 1×10^{8} |
30 | 13.7 ± 0.813 | 10.6 | 3.1 |

23 | 150 | 60 | 5×10^{8} |
30 | 12.6 ± 0.779 | 10.1 | 2.5 |

24 | 300 | 60 | 6×10^{8} |
28 | 13.1 ± 0.681 | 6.1 | 7.0 |

25 | 200 | 72 | 3×10^{8} |
28 | 14.3 ± 0.983 | 8.3 | 6.0 |

26 | 200 | 84 | 4×10^{8} |
25 | 12.4 ± 0.779 | 9.6 | 2.8 |

27 | 200 | 84 | 4×10^{8} |
26 | 11.7 ± 0.681 | 9.1 | 2.6 |

28 | 300 | 48 | 5×10^{8} |
25 | 13.2 ± 0.832 | 10.4 | 2.8 |

29 | 200 | 48 | 3×10^{8} |
26 | 11.4 ± 0.913 | 9.6 | 1.8 |

30 | 300 | 60 | 5×10^{8} |
26 | 12.6 ± 0.872 | 8.3 | 4.3 |

31 | 200 | 72 | 1×10^{8} |
30 | 11.3 ± 0.881 | 8.3 | 3.0 |

32 | 250 | 48 | 28 | 11.8 ± 0.692 | 7.9 | 3.9 |

**Table 3: **L-methionine production by *Corynebacterium glutamicum*X300 using significant physical parameters based on CCD.

Trial | Factor | Lmethionine (mg/ml) | Predicted value (mg/ml) | Residual Value (mg/ml) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

VIII | IX | X | XI | XII | XIII | XV | XVI | XIX | XX | ||||

1 | 40 | 2.0 | 1.2 | 2.0 | 3.0 | 1.2 | 0.5 | 20 | 0.0 | 00 | 49.6±0.913 | 46.8 | 2.8 |

2 | 20 | 10 | 2.3 | 1.9 | 2.0 | 1.8 | 1.0 | 5.0 | 1.6 | 80 | 41.6±0.772 | 37.6 | 4.0 |

3 | 140 | 7.0 | 1.4 | 0.0 | 1.5 | 0.6 | 1.0 | 20 | 6.0 | 2.0 | 36.3±0.832 | 31.2 | 5.1 |

4 | 40 | 7.0 | 1.0 | 1.2 | 2.5 | 0.3 | 0.0 | 35 | 2.5 | 1.6 | 50.1±0.661 | 42.8 | 7.3 |

5 | 60 | 1.4 | 1.8 | 1.0 | 2.5 | 1.8 | 2.0 | 20 | 2.0 | 4.0 | 52.2±0.992 | 44.6 | 7.4 |

6 | 40 | 1.0 | 2.0 | 2.1 | 3.0 | 0.3 | 2.0 | 35 | 3.0 | 4.6 | 50.3±0.683 | 46.1 | 4.2 |

7 | 60 | 1.4 | 2.2 | 1.8 | 3.0 | 0.6 | 1.0 | 5.0 | 3.0 | 2.6 | 49.6±0.813 | 44.3 | 5.3 |

8 | 120 | 2.0 | 2.1 | 1.6 | 1.5 | 0.3 | 1.0 | 5.0 | 4.5 | 4.1 | 48.2±0.669 | 41.6 | 6.6 |

9 | 80 | 1.4 | 2.1 | 1.6 | 1.0 | 1.5 | 1.6 | 10 | 2.0 | 6.1 | 50.3±0.832 | 46.4 | 3.9 |

10 | 40 | 2.0 | 2.0 | 2.0 | 2.0 | 1.2 | 1.7 | 20 | 4.0 | 4.8 | 46.8± | 41.6 | 5.2 |

11 | 120 | 1.0 | 1.6 | 1.6 | 1.6 | 1.0 | 1.0 | 20 | 0.0 | 3.9 | 31.3±0.661 | 28.3 | 3.0 |

12 | 140 | 4.0 | 1.8 | 1.8 | 0.0 | 1.5 | 1.4 | 10 | 2.0 | 3.3 | 42.8±0.779 | 39.3 | 3.5 |

13 | 40 | 1.0 | 1.8 | 1.6 | 2.0 | 1.8 | 0.0 | 35 | 2.5 | 0.0 | 36.8±0.681 | 32.2 | 4.6 |

14 | 40 | 1.0 | 1.0 | 0.0 | 1.5 | 0.6 | 0.5 | 25 | 2.0 | 0.0 | 41.6±0.832 | 38.9 | 2.7 |

15 | 60 | 4.0 | 2.0 | 0.0 | 2.0 | 0.9 | 0.0 | 25 | 4.0 | 20 | 46.6±0.881 | 41.3 | 5.3 |

16 | 80 | 1.0 | 2.3 | 1.6 | 1.5 | 0.9 | 0.0 | 10 | 6.0 | 5.3 | 48.9±0.732 | 44.2 | 4.7 |

17 | 120 | 1.0 | 2.1 | 1.6 | 1.5 | 0.9 | 0.0 | 10 | 6.0 | 3.9 | 41.3±0.913 | 38.3 | 3.0 |

18 | 60 | 6.0 | 2.3 | 1.8 | 1.5 | 0.3 | 2.0 | 10 | 4.0 | 4.6 | 46.2±0.883 | 42.8 | 3.4 |

19 | 100 | 7.0 | 1.0 | 2.1 | 3.0 | 0.6 | 2.0 | 10 | 4.0 | 4.3 | 46.3±0.961 | 43.1 | 3.2 |

20 | 140 | 1.0 | 1.0 | 2.1 | 3.0 | 1.8 | 1.5 | 5.0 | 2.5 | 4.1 | 44.8±0.832 | 41.6 | 3.2 |

21 | 100 | 1.4 | 2.3 | 0.0 | 3.0 | 1.8 | 1.5 | 5.0 | 4.0 | 4.3 | 50.1±0.599 | 46.2 | 3.9 |

22 | 120 | 9.0 | 1.6 | 0.0 | 2.5 | 1.8 | 1.7 | 5.0 | 2.5 | 3.9 | 40.8±0.599 | 38.3 | 2.5 |

23 | 120 | 1.0 | 1.4 | 0.0 | 0.0 | 1.2 | 1.0 | 20 | 4.0 | 3.1 | 46.2±0.832 | 41.6 | 4.6 |

24 | 140 | 8.0 | 1.0 | 1.0 | 1.0 | 1.8 | 1.5 | 25 | 6.0 | 4.8 | 44.6±0.611 | 41.1 | 4.6 |

25 | 60 | 1.0 | 1.0 | 1.6 | 1.5 | 1.2 | 1.5 | 20 | 6.0 | 4.1 | 50.1±0.432 | 46.2 | 3.9 |

26 | 60 | 1.4 | 1.6 | 1.2 | 2.0 | 1.5 | 1.0 | 20 | 2.5 | 3.6 | 39.7±0.568 | 37.1 | 2.6 |

27 | 80 | 1.0 | 2.3 | 1.6 | 2.0 | 1.6 | 1.8 | 35 | 2.0 | 20 | 48.3±0.662 | 44.2 | 4.1 |

28 | 60 | 9.0 | 1.8 | 1.4 | 2.5 | 1.4 | 1.5 | 1.6 | 6.0 | 30 | 41.6±0.881 | 39.3 | 2.3 |

29 | 100 | 6.0 | 2.3 | 1.8 | 1.0 | 1.8 | 1.5 | 1.4 | 5.0 | 20 | 43.3±0.793 | 40.1 | 3.2 |

30 | 120 | 2.0 | 2.2 | 1.2 | 0.0 | 1.2 | 1.2 | 1.6 | 6.0 | 10 | 44.6±0.801 | 39.6 | 5.0 |

31 | 140 | 1.4 | 2.3 | 1.6 | 1.5 | 1.6 | 1.8 | 0.0 | 4.5 | 10 | 48.6±0.872 | 44.2 | 4.4 |

32 | 60 | 9.0 | 2.1 | 1.6 | 1.5 | 1.6 | 1.5 | 2.4 | 6.0 | 20 | 40.1±0.661 | 38.1 | 2.0 |

**Table 4:** L-methionine production by *Corynebacterium glutamicum* X300 using significant nutritional parameters based on CCD.

Variable | Coefficient | Standard error of mean | Computed t-value | p-value |
---|---|---|---|---|

Intercept |
52.168 | 0.039 | 233.611 | 0.000 |

III |
0.913 | 0.061 | 1.169 | 0.016 |

IV |
0.682 | 0.083 | 7.311 | 0.024 |

V |
0.366 | 0.072 | 5.316 | 0.136 |

VI |
0.611 | 0.091 | 2.618 | 0.613 |

VIII |
0.732 | 0.059 | 4.813 | 0.024 |

IX |
0.682 | 0.061 | 6.161 | 0.126 |

X |
0.399 | 0.033 | 7.133 | 0.311 |

XI |
0.816 | 0.042 | 4.613 | 0.791 |

XII |
0.913 | 0.061 | 19.613 | 0.066 |

XIII |
0.331 | 0.055 | 11.611 | 0.168 |

XIV |
-0.068 | 0.061 | -5.613 | 0.002 |

XV |
0.611 | 0.066 | 6.918 | 0.069 |

XVI |
0.382 | 0.079 | 9.832 | 0.361 |

XIX |
0.791 | 0.069 | 8.622 | 0.162 |

XX |
0.668 | 0.063 | 6.913 | 0.113 |

**Table 5: **Model coefficient calculated from linear regression for the assessment of the significance of the independent variables.

All total 32 experimental trials for both physical and nutritional variables were examined for the estimation of the principal effects and multi-factors interactions. The production of L-methionine was increased significantly (p<0.01) after optimization of nutritional parameters compared to the optimization of physical parameters.

**The second CCD**

**Table 6** depicted the second order CCD for L-methionine
production in the form of variance (ANOVA) for the quadratic model.

Source of data | Sum of square | Degree of freedom (df) | Mean square | F-value | p-value˃F |
---|---|---|---|---|---|

Model |
0.841 | 5 | 0.313 | 1230.14 | <0.0001 |

III |
0.797 | 6 | 0.331 | 226.64 | 0.0046 |

IV |
0.401 | 3 | 0.210 | 86.46 | <0.0001 |

V |
0.616 | 6 | 0.339 | 117.64 | <0.0001 |

VI |
0.731 | 5 | 0.361 | 237.81 | 0.0024 |

VIII |
0.611 | 3 | 0.305 | 373.61 | <0.0001 |

IX |
0.383 | 1 | 0.116 | 423.64 | 0.0069 |

X |
0.770 | 1 | 0.216 | 874.84 | 0.0011 |

XI |
0.463 | 3 | 0.169 | 321.61 | 0.0022 |

XII |
0.511 | 9 | 0.312 | 72.21 | <0.0001 |

XIII |
0.663 | 3 | 0.226 | 123.81 | 0.0013 |

XV |
0.634 | 3 | 0.213 | 321.21 | 0.0014 |

XVI |
0.481 | 5 | 0.161 | 723.61 | <0.0001 |

XIX |
0.514 | 1 | 0.226 | 54.33 | <0.0001 |

XX |
0.681 | 4 | 0.261 | 222.21 | 0.0017 |

Residual |
1.613 | 4 | 0.246 | - | - |

Pure error |
0.000 | 4 | 0.000 | - | - |

Lack of fit |
1.613 | 1 | 0.661 | - | - |

Total |
10.66 |

R^{2}=0.9991; Adj R^{2}=0.739

**Table 6: **One way ANOVA for Response Surface Quadratic Model for L-methionine production by *Corynebacterium glutamicum* X300.

Shih and Shen applied RSM to examine the yeast extract,
glucose, ammonium sulphate and initial pH on the production
of poly €-lysine by *Streptomyces albulus* IFO 14147 in shake-flask
fermentation. They obtained both the first order with interactions
model (R^{2}=0.9660) which was more adequate than the pure first order
model (R^{2}=0.8759). They have applied CCD for the assessment of the
optimum composition. Their experimental data were fitted with a
second-order polynomial euation by a multiple regression analysis.
The determination of coefficient (R^{2}=0.816) and the Fisher’s test
(significant at upper 5%) indicated a good adequacy of the secondorder
polynomial model used to analyze the data [5]. Nilofer et al.
used RSM to optimize the variables of L-lysine fermentation by *Corynebacterium glutamicum* AEC-2. They used Plackett-Burman
design and obtained four variables (the level of sugar in molasses,
ammonium sulphate, and incubation temperature and inoculum
size) were proved to be significant for L-lysine production.
Furthermore, CCD (24 factorial) was applied to determine the
optimum levels of significant variables. A second order polynomial
regression model was used to explain the experimental data. Using
these models, the production of L-lysine was increased up to 2.6
fold [6]. Pandey and Banik, optimized six factors (namely: pH, temperature, fermentation time, orbital speed, age of inoculum and
inoculums volume) for alkaline phosphate production by *Bacillus
licheniformis* using RSM. They have reported that pH, temperature,
fermentation time and orbital speed were significant (p<0.05) using
Placket Berman design methodology. An increase up to 1.5 fold in
the production was obtained after optimization of the production
using RSM [7]. Shankar et al. examined the invertase production
by *Saccharomyces cerevisiae *MK. The optimum levels of the key
variables (orange peel, yeast extract and methionine) was applied
to determine their interactions on the production using CCD and
RSM. The determined coefficient of determination (R^{2}=0.9994) was
nearer to 1 which satisfied the adjustment of the quadratic model
to the experimental data [1]. Patel et al. compared between one at a
time variation factors and CCD for the production of mycophenolic acid by *Penicillum brevicompactum* MTCC8010 in a 12-day batch
culture. The medium optimization using one-at-a-time variation
gave 6 fold greater titer, whereas CCD gave almost 9 fold greater
titer compare to the production prior to the optimization [21].

The nearness of the coefficient of determinant (R^{2}=0.9982) for
one way ANOVA for full quadratic model and the same for one
way ANOVA for Response Surface Quadratic Model (R^{2}=0.9991)
used for L-methionine production by* Corynebacterium glutamicum* X300 ensured the satisfactory adjustment of the Quadratic model to
explain the data obtained from the present experiment. The maximum
production of L-methionine (52.1 mg/ml) was obtained with 72 h of
incubation.

- Shankar T, Thagamathi P, Rama R, Sivakumar T (2003) Middle East Journal of Scientific Research18: 615-622.
- Plackett RL, Burman JP (1946) The design of optimum multifactorial experiments. Biometrika 33: 305-325.
- Adinarayana K, Ellaiah P (2002) Response surface optimization of the critical medium components for the production of alkaline protease by a newly isolated Bacillus sp. Journal of Pharmacy and Pharmaceutical Sciences 5: 272-278.
- Balusu R, Paduru RR, Kuravi SK, Seenayya G, Reddy G (2005) Optimization of critical medium components using response surface methodology for ethanol production from cellulosic biomass by Clostridium thermocellum SS19. Process Biochemistry 40: 3025-3030.
- Shih IL, Shen MH(2006) Application of response surface methodology to optimize production of poly-ɛ-lysine by Streptomyces albulus IFO 14147. Enzyme and Microbial Technology 39:15-21.
- Nelofer R, Syed Q, Nadeem M (2008) Turkish Journal of Biochemistry 33: 50-57.
- Pandey SK, Banik RM (2010) Optimization of process parameters for alkaline phosphatase production by Bacillus licheniformis using response surface methodology. Journal of Agricultural Technology 6: 721-732.
- Parajo JC, Santos V, Domingnez H, Vazquez M (1995) Applied Biochemistry 55: 133-149.
- Dlamini AM, Peris PS (1997) Biopolymer production by a Klebsiella oxytoca isolate using whey as fermentation substrate. Biotechnology Letter 19: 127-130.
- Montgomery DC (1997) Design and analysis of experiments. John Wiley and Sons, New York, USA, pp: 427-510.
- Vazquez M, Martin AM (1998)Optimization of Phaffia rhodozyma continuous culture through response surface methodology. Biotechnology and Bioengineering 57: 314-320.
- Kaur P, Satyaarayana T (2005) Production of cell-bound phytase by Pichia anomala in an economical cane molasses medium: optimization using statistical tools. Process Biochemistry 40: 3095-3102.
- Ganguly S, Satapathy KB, Banik AK (2014) Research Journal of Pharmaceutical Dose forms and Technology 6:252-261.
- Ganguly S, Satapathy KB(2014) Biosynthesis of L-methionine in Corynebacterium glutamicum X300. European Journal of Chemical Bulletin 3: 637-638.
- Ezemba CC, Anakwrnz VN, Archibong EJ, Anakkwu CG, Obi ZC, et al. (2016) methionine production using native starches and proteins in submerged fermentation by bacillus. World Journal of Pharmacy and Pharmaceutical Sciences 5: 2056-2067.
- Banik AK, Majumder SK (1974) Indian Journal of Experimental Biology 12: 263-265.
- Roy SK, Mishra AK, Nanda G (1984) Microbial Strains Employed for L-Methionine Fermentation: An Extensive Review. Current Science 53: 1296-1297.
- Chisti Y, Moo-Young M (1999) Biotechnology: The Science and the Business.Harwood Academic Publishers, New York, pp: 177-222.
- Shah AH, Hameed A, Ahmad S, Khan GM (2000) Online Journal of Biological Sciences 2: 151-156.
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