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P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129




  Optimizing factors affecting protease production by a Bacillus cereus using
                    groundnut shell under solid substrate fermentation
                                   P. RATHAKRISHNAN, P. NAGARAJAN
        Department of Chemical Engineering, Annamalai University, Annamalainagar-608002, Tamilnadu, India.


                                                     ABSTRACT
        The physical factors affecting the production of protease from bacillus cereus was investigated. Agro
industrial waste product groundnut shells were used as substrates in solid-state fermentation for protease enzyme
production. The response surface methodology (RSM) was used to optimize protease production by implementing the
central-composite design (CCD). The physiological fermentation factors such as pH (8.0), temperature (43°C),
fermentation time (26 hrs), inoculum level (3.2 ml) and substrate concentration (9.6g) were optimized by statistical
analysis using response surface methodology. The maximum yield of protease production was 76.75U/gds. This was
evidenced by the higher value of coefficient of determination (R 2= 0.9994).
Keywords: RSM, groundnut shells, protease, CCD, bacillus cereus, physical factors optimization.




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                                                   INTRODUCTION
        Proteases (EC 3.4) form an important class of commercially important enzymes and find applications in
detergent, leather, food and pharmaceutical industries, constituting approximately
40% of the total enzyme market [1].
        In recent years, there has been an increasing trend towards efficient utilization and value-addition of agro-
industrial residues such as coffee pulp and husk, cassava husk, cassava bagasse, sugarcane bagasse, sugar beet pulp,
apple pomace, declassified potatoes, etc. [2–7].
        Bacillus strains are known to produce and secrete large quantities of extracellular enzymes and constitute a
major group of industrial enzyme producers due to the robust nature of the organism as well as its enzymes [8].
Bacterial systems are being increasingly investigated for the production of enzymes and metabolites by SSF.
        Solid-state fermentation (SSF) has been defined as the fermentation process which involves solid matrix and
is carried out in absence or near absence of free water; however, the substrate must
Possess enough moisture to support growth and metabolism of the microorganism. The solid matrix could be either
the source of nutrients or simply a support impregnated by the proper nutrients that allows the development of the
microorganisms. [9]
        Statistical approaches offer ideal ways for process optimization studies in biotechnology [10, 11]. Response
surface methodology (RSM) is now being routinely used for optimization studies in several biotechnological and
industrial processes [12-14].Here, we report the use of RSM to study the effects various physicochemical factors on
protease production from B. cereus
        As the demand for proteases increases, it is expected that hyperactive strains will emerge and that the
enzymes produced by new strains could be used as catalysts in different industries. In the present work, the strain
employed was a strain of Bacillus sp. producing alkaline protease in SSF using agro-residue groundnut shell substrate.
In this paper, we report on factors that influence the maximization of alkaline protease production by Bacillus cereus
through SSF.


                                         MATTERIALS AND METHODS
Microorganism and inoculums’ preparation:
        Bacterial strain used in this work is well preserved in the laboratory. Bacterial strain Bacillus cereus was a
stock of the Microbial Type Culture collection Centre (MTCC), Chandigarh, India. The strain was maintained on
nutrient agar medium at 4◦C. The medium composition (g/l) was comprised off the following: Beef extract 1.0; Yeast
extract 2.0; Peptone 5.0; NaCl 5.0 and Agar 2.0. Cells were subcultered at monthly intervals.
Solid-state fermentation:
        Groundnut shell was collected from local market in Panruti, Tamilnadu, India. The shells were washed
thoroughly with tap water and then dried. The dried materials obtained were milled and sieved to powder for using as
a carbon source for protease production. Fermentation was carried out in Erlenmeyer flasks (250 ml) with 10g of



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Groundnut shell powder, supplemented with nutrients concentrations defined by the experimental design NaNO3
0.0386g/gds, Tryptone 0.0338g/gds, K2HPO4 0.2373g/gds and Malt extract 0.2090g/gds. Each flask was covered
with hydrophobic cotton and autoclaved at 121°C for 15 min. After cooling the flasks to room temperature, the flasks
were inoculated with 2 ml 24-h grown culture broth under sterile conditions. The contents of the flasks were well
mixed and incubated at 33±1ºC for 120 hrs.
         During the preliminary screening process, the experiments are carried out for 5 days and it was found that at
the 28 hrs, the maximum production occurs. Hence experiments are carried out for 28 hrs.
Extraction of protease:
         The enzyme was extracted according to the method described by Nagamine et al. (2003) [15]. Fermented
medium was mixed thoroughly with 50 mM glycine–NaOH buffer, pH 11 for 30 min and the extract was separated by
squeezing through a cloth. This process was repeated three times and extracts were pooled together and then
centrifuged. The supernatant was used as enzyme source for protease assay.
Optimization of process parameters:

         A full factorial design, which includes all possible factor combinations in each of the factors, is a powerful tool
for understanding complex processes for describing factor interactions in multifactor systems. RSM is an empirical
statistical technique employed for multiple regression analysis by using quantitative data obtained from properly
designed experiments to solve multivariate equations simultaneously. The experiments with different pH, temperature,
fermentation time, innoculum size and substrate conc. were employed, simultaneously covering the spectrum of
variables for the production of protease in the central composite design. In order to describe the effects of pH,
temperature, fermentation time, inoculums size and substrate conc. on the protease production, batch experiments
were conducted. The coded values of the process parameters were determined by the following equation.
                                                  Xi  X
                                           x               o                 (1)
                                            i         Δx


         Where xi-coded value of the ith variable, Xi-uncoded value of the ith test variable and X0-uncoded value of the
ith test variable at center point.
         The range and levels of individual variables are given in Table 2. The experimental design is given in Table 3,
along with experimental data and predicted responses. The regression analysis was performed to estimate the response
function as a second order polynomial.
                                           k        k       2   k -1    k
                                 Y  β 0   βi X   β ii X           β ij X i X j (2)
                                          i1    i i1      i i1,i j j2

         Where Y is the predicted response, βi, βj, βij are coefficients estimated from regression. They represent the
linear, quadratic and cross products of X1, X2, and X3 on response




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                                                                                Levels
           Variable                   Code
                                                   -2.38        -1               0               +1      +2.38
                pH                      A           6           7                8               9         10

         Temperature ( o C)             B           30          35               40              45        50

   Fermentation Time (hrs)              C           8           16               24              32        40

       Inoculums size (ml)              D           1           2                3               4         5

   substrate concentration
                                        E           3           6                9               12        15
                (g)



Table 2: Levels of different process variables in coded and un-coded form for protease production independent
                                            variable range and levels


  Run         A-pH             B-           C-             D-           E-            Protease activity(u/gds)
  . No                   Tempera      Fermenta      Inoculums       substrate
                                                                                     Experimental     Theoretical
                              ture     tion time         Size    concentra
                                                                       tion

   1        0.00000       0.00000      0.00000       0.00000         0.00000             73.00         73.0603

   2        2.37841       0.00000      0.00000       0.00000         0.00000             62.70         62.6081

   3        1.00000       1.00000      -1.00000      -1.00000        1.00000             53.50         53.2997

   4        -1.00000     -1.00000      1.00000       1.00000         -1.00000            50.00         49.9518

   5        -2.37841      0.00000      0.00000       0.00000         0.00000             56.00         55.8666

   6        0.00000       0.00000      -2.37841      0.00000         0.00000             60.00         59.8516

   7        -1.00000      1.00000      -1.00000      -1.00000        1.00000             49.20         49.6277

   8        1.00000      -1.00000      -1.00000      -1.00000        1.00000             62.30         62.2990

   9        1.00000       1.00000      1.00000       -1.00000        1.00000             61.00         61.2235

   10       1.00000       1.00000      1.00000       1.00000         1.00000             68.50         68.7547

   11       1.00000       1.00000      -1.00000      -1.00000        -1.00000            53.00         53.1257

   12       -1.00000      1.00000      -1.00000      1.00000         -1.00000            62.00         61.7850




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P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129

13   -1.00000      1.00000    -1.00000     1.00000      1.00000        58.00      58.0715

14   -1.00000      1.00000    1.00000      1.00000      -1.00000       63.40      63.7275

15   1.00000       1.00000    1.00000      1.00000      -1.00000       60.00      59.9807

16   -1.00000     -1.00000    -1.00000     1.00000      1.00000        50.00      50.2833

17   0.00000       0.00000    0.00000      0.00000      0.00000        73.21      73.0603

18   0.00000       0.00000    0.00000      0.00000      2.37841        56.00      55.6909

19   0.00000       0.00000    0.00000      0.00000      0.00000        73.00      73.0603

20   -1.00000     -1.00000    -1.00000     -1.00000     -1.00000       61.10      61.1468

21   1.00000      -1.00000    -1.00000     1.00000      -1.00000       60.00      60.0563

22   0.00000       0.00000    0.00000      0.00000      0.00000        73.00      73.0603

23   1.00000      -1.00000    1.00000      1.00000      1.00000        58.90      58.7541

24   -1.00000     -1.00000    1.00000      1.00000      1.00000        50.00      50.2884

25   0.00000       0.00000    0.00000      0.00000      0.00000        73.00      73.0603

26   0.00000       0.00000    2.37841      0.00000      0.00000        62.20      62.1231

27   0.00000       0.00000    0.00000      0.00000      0.00000        73.00      73.0603

28   1.00000       1.00000    -1.00000     1.00000      -1.00000       60.00      60.1320

29   1.00000      -1.00000    1.00000      -1.00000     -1.00000       58.00      58.0425

30   1.00000      -1.00000    1.00000      1.00000      -1.00000       52.00      51.9488

31   -1.00000     -1.00000    1.00000      -1.00000     -1.00000       57.40      57.1893

32   0.00000       0.00000    0.00000      0.00000      0.00000        73.21      73.0603

33   -1.00000     -1.00000    -1.00000     -1.00000     1.00000        53.00      52.8833

34   -1.00000     -1.00000    1.00000      -1.00000     1.00000        55.00      54.9446




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      35      0.00000       0.00000      0.00000       0.00000        0.00000          73.21           73.0603

      36      1.00000       1.00000      1.00000       -1.00000      -1.00000          55.35           55.0307

      37      1.00000      -1.00000      1.00000       -1.00000       1.00000          62.00           62.2665

      38      1.00000      -1.00000     -1.00000       -1.00000      -1.00000          64.00           64.0937

      39      -1.00000      1.00000      1.00000       1.00000        1.00000          66.30           66.0328

      40      0.00000      -2.37841      0.00000       0.00000        0.00000          59.00           59.0473

      41      -1.00000     -1.00000     -1.00000       1.00000       -1.00000          56.00           55.9656

      42      -1.00000      1.00000      1.00000       -1.00000      -1.00000          59.60           59.9212

      43      -1.00000      1.00000      1.00000       -1.00000       1.00000          59.70           59.6453

      44      0.00000       0.00000      0.00000       -2.37841       0.00000          42.00           41.9400

      45      0.00000       0.00000      0.00000       2.37841        0.00000          44.90           44.7347

      46      1.00000       1.00000     -1.00000       1.00000        1.00000          62.60           62.8872

      47      0.00000       2.37841      0.00000       0.00000        0.00000          65.00           64.7274

      48      -1.00000      1.00000     -1.00000       -1.00000      -1.00000          56.00           55.9225

      49      0.00000       0.00000      0.00000       0.00000       -2.37841          55.00           55.0838

      50      1.00000      -1.00000     -1.00000       1.00000        1.00000          61.00           60.8428



           Table 3: Experimental conditions and observed response values of 25 Central Composite Design

        A statistical program package Design Expert 7.1.5, was used for regression analysis of the data obtained and
to estimate the coefficient of the regression equation. The equations were validated by the statistical tests called the
ANOVA analysis. The significance of each term in the Equation is to estimate the goodness of fit in each case.
Response surfaces were drawn to determine the individual and interactive effects of the test variable on the protease
production. The optimal values of the test variables were first obtained in coded units and then converted to the
uncoded units.




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Protease assay:

        Protease activity was determined using modified Auson–Hagihara method [16]. In this 1 ml of the enzyme
solution was added to 1 ml casein solution (1%, w/v casein solution prepared in 50 mM glycine–NaOH buffer, pH 11)
and incubated at 70ºC for 20 min. The reaction was terminated by adding 4 ml of 10% trichloroacetic acid and the
contents were filtered through a Whatman No. 1 filter paper. The filtrate absorbance was read at 280 nm using UV–
Visible spectrophotometer and the protease activity was calculated using tyrosine standard curve. One unit of alkaline
protease activity was defined as 1 µg of tyrosine liberated ml -1 under the assay conditions.


                                            RESULT AND DICUSSION
        To examine the combined effect of five different process parameters (independent variables), on the protease
production, a central composite design of 25 = 32 plus 8 centre points and (2x5 = 10) star points leading to a total of
50 experiments were performed. Equation (3) represents the mathematical model relating the protease production
and the second order polynomial coefficient for each term of the equation determined through multiple regression
analysis using the Design Expert 7.1.5.The coded values of the independent variables are given in Table 2. The
experimental and predicted values of protease production are also given in table 3.
        The results were analyzed by using ANOVA i.e., analysis of variance suitable for the experimental design used
and cited in Table 4. The ANOVA of the quadratic regression model indicates the model to be significant. The Model F-
value of 2602.26 implied the model to be significant. Model F-value was calculated as a ratio of mean square
regression and mean square residual. Model P value (Prob>F) is very low [0.0500]. This reiterates that the model is
significant. The P values are used as a tool to check the significance of each of the coefficients, which in turn are
necessary to understand the pattern of the mutual interactions between the test variables. The F value and the
corresponding P values, along with the coefficient estimate are given in Table 4. The smaller the magnitude of the P,
the more significant is the corresponding coefficient. Values of P less than 0.0500 indicates the model terms to be
significant. The coefficient estimates and the corresponding P values along with the coefficient estimate are given in
table 4.The coefficients estimate and the corresponding P values suggests that, among the test variables used in the

study, A, B, C, D, E, AB, AC, AD, AE, BC, BD, BE, CD, CE, DE, A 2, B2, C2, D2, E2 are significant [where A-pH, B-
temperature, C-fermentation time, D-inoculums size and E-substrate conc.] model terms. Values greater than 0.1000
indicate the model terms are not significant.




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Source    Coefficient      Sum of         DF        Mean          F             P value
          factor           square                   square
                                                                                P>F

Model        73.06            2891.74          20      144.59         2602.26         < 0.0001
                               87.00           1       87.00          1565.76         < 0.0001
  A           1.42             61.76                   61.76          1111.53         <0.0100
                                9.88           1        9.88          177.75          < 0.0001
  B           1.19             14.95           1       14.95          269.08          <0.0001
                                0.71           1        0.71           12.70           0.0013
  C           0.48                             1
                               331.80                  331.80         5971.73         <0.0001
                               216.78          1       216.78         3901.51         <0.0001
  D           0.59
                               253.11          1       253.11         4555.38         <0.0001
                              1534.14          1      1534.14         2761.18         <0.0001
  E           0.13
                               542.37          1       542.37         9761.52         < 0.0001
 A*A          -2.44            65.98           1       65.98          1187.52         <0.0001
                                8.77           1        8.74          157.80          <0.0001
 B*B          -1.98             2.62           1        2.62           47.09          <0.0001
                               83.69           1       83.69          1506.23         <0.0001
 C*C          -2.13            126.60          1       126.60         2278.60         <0.0001
                               243.93          1       243.93         4390.20         < 0.0001
 D*D          -5.25             7.75           1        7.75          139.52          < 0.0001
                                8.46           1        8.46          152.20          <0.0001
 E*E          -3.12                            1
                               72.45                   72.45          1303.96
                               13.33           1       13.33                          <0.0001
 A*B          -1.44
                                1.61           1       0.056          239.83          < 0.0001
 A*C          -0.52                            29      0.069
                               1.53                                    5.88           0.0112
 A*D          0.29             0.083           22      0.012
                              2893.36          7
 A*E          1.62                             49

 B*C          1.99

 B*D          2.76

 B*E          0.49

 C*D          -0.51

 C*E          1.50

 D*E          0.65

Residua

Lack of
  fit

 Pure
 Error

  Cor
 Total




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                      Table 4: Analysis of Variance (ANOVA) for Response Surface Quadratic Model

                 Std. Dev. 0.24; R2 = 99.94%; R2(pred) 99.80%; R2(adj) 99.91%; C.V. % 0.39
Protease activity = +73.06+1.42* A+1.19 * B+0.48 * C+0.59 * D+0.13* E -1.44 * A * B-5.2 * A * C+0.29 * A * D+1.62 * A *

E+1.99 * B * C+2.76 * B * D                             +0.49 * B * E-0.52 * C * D+1.50 * C * E+0.65 * D * E -2.44 *A2 -1.98 * B2-2.13 * C2-

5.25 * D2-3.12 * E2
        The predicted R2 of 0.9980 is in reasonable agreement with the adjusted R 2 of 0.9991. Adequate precision
measures the signal to noise ratio. A ratio greater than 4 is desirable. The fit of the model was also expressed by the
coefficient of regression R2, which was found to be 0.9994 indicating from properly that 99.94% the variability in the
response could be explained by the model. This implies that the prediction of experimental data is quite satisfactory.
The Coefficient of Variation (CV) indicates the degree of precision with which the treatments are compared. Usually,
the higher the value of the CV is, the lower the reliability of the experiment. Here a lower value of CV (0.39) indicates
greater reliability of the experiments performed. The Response surface estimation for protease production as
discussed in the previous section, the response surface methodology was used with five process variables to evaluate
their effect on the protease production. The response Eq. (3) was obtained for the protease production. To investigate
the interactive effect of two factors on the protease production, the response surface methodology was used and
three-dimensional plot was drawn. The inferences so obtained are discussed below. The interaction effects and
optimal levels of the variables were determined by plotting the response surface curves.

        The 3D response surface curves are shown in Figs. 1 to 10. Figure 1 represents the interactive effect of pH
and temperature on protease production. From Fig. 1 it was inferred that with the increase in pH, the protease
production increases with the temperature. The optimum value of both the factors, viz, pH and temperature can be
analyzed by saddle point or by checking the maxima formed by the X and Y coordinates. The combined effect of pH
and fermentation time on protease production shown in Fig 2.
                      ( U /g d s )
                      a c tiv ity




                                            80


                                            70


                                             60
                      P r o te a s e




                                             50


                                                 40


                                                 30




                                             2.38                                                                          2.38
                                                          1.19                                                   1.19
                                                                   0.00                                   0.00
                                                                          -1.19                   -1.19
                                           B: Temperature ( o C)                                                   A: pH
                                                                                  -2.38   -2.38




   Figure 1: Response surface Plot for protease production from groundnut shell by Bacillus cereuss in solid state
                                                        fermentation as a function of pH and Temperature



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         ( U /g d s )
         a c tiv ity
                             80


                             70


                              60
         P r o te a s e




                              50


                                  40


                                  30




                                  2.38                                                                           2.38
                                         1.19                                                         1.19
                                                      0.00                                    0.00
                                                             -1.19                    -1.19
                          C: Fermentation Time (hrs)                                                    A: pH
                                                                      -2.38   -2.38




Figure 2: Response surface Plot for protease production from groundnut shell by Bacillus cereus in solid state
       ( U /g d s )




                                          fermentation as a function of pH and fermentation time
       a c tiv ity




                             80

                             70

                             60
       P r o te a s e




                              50

                              40

                                  30

                                  20




                              2.38                                                                               2.38
                                         1.19                                                         1.19
                                                     0.00                                     0.00
                                                             -1.19                    -1.19
                            D: Inoculums size (ml)                                                       A: pH
                                                                      -2.38   -2.38




Figure 3: Response surface Plot for protease production from groundnut shell by Bacillus cereus in solid state
                                                fermentation as a function of pH and inoculums size




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                           ( U /g d s )
                           a c tiv ity                 80

                                                       70

                                                       60
                           P r o te a s e



                                                        50

                                                        40

                                                            30

                                                            20




                                                        2.38                                                                                                       2.38
                                                                        1.19                                                                             1.19
                                                                                      0.00                                                  0.00
                                                                                                     -1.19                         -1.19
                                             E: substrate concentration (g)                                                                                A: pH
                                                                                                                 -2.38   -2.38




    Figure 4: Response surface Plot for protease production from groundnut shell by Bacillus cereus in solid state
                                                                 fermentation as a function of pH and substrate concentration
        Figure 3 depicts the interaction of pH and Inoculums size, Figure 4 shows the effect of pH and substrate
concentration on the protease production. The shape of the contour show good interaction between the pH and
substrate concentration, which is clearly illustrated in Fig. 4. The combined effect of temperature and fermentation
time was shown in the form of 3D plot in Fig. 5 Combined effect of temperature and innoculum size has been analyzed
          ( U /g d s )




from the CCD three-dimensional plot shown in Fig. 6.
          a c tiv ity




                                            80


                                            70


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          P r o te a s e




                                             50


                                                 40


                                                 30




                                             2.38                                                                                                                  2.38
                                                                 1.19                                                                                   1.19
                                                                               0.00                                                        0.00
                                                                                             -1.19                               -1.19
                            C: Fermentation Time (hrs)                                                                                             B: Temperature ( o C)
                                                                                                             -2.38   -2.38




    Figure 5: Response surface Plot for protease production from groundnut shell by Bacillus cereus in solid state
                                                             fermentation as a function of temperature and fermentation time




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             ( U /g d s )
             a c tiv ity
                                    80

                                     70

                                     60

                                     50
             P r o te a s e




                                      40

                                      30

                                         20

                                          10




                                      2.38                                                                                2.38
                                                  1.19                                                          1.19
                                                            0.00                                   0.00
                                                                   -1.19                   -1.19
                                   D: Inoculums size (ml)                                                 B: Temperature ( o C)
                                                                           -2.38   -2.38




    Figure 6: Response surface Plot for protease production from groundnut shell by Bacillus cereus in solid state
               ( U /g d s )




                                                fermentation as a function of temperature and inoculum size
               a c tiv ity




                                     80

                                     70

                                     60

                                      50
               P r o te a s e




                                      40

                                         30

                                          20

                                          10




                                         2.38                                                                            2.38
                                                  1.19                                                         1.19
                                                            0.00                                   0.00
                                                                   -1.19                   -1.19
                                E: substrate concentration (g)                                            B: Temperature ( o C)
                                                                           -2.38   -2.38




    Figure 7: Response surface Plot for protease production from groundnut shell by Bacillus cereus in solid state
                                           fermentation as a function of temperature and substrate concentration
        Figure 7 shows the effect of temperature and substrate concentration on the protease production. Figure 8
shows the response surface curves of protease production as a function of inoculums size and fermentation time.
Figure 9 shows the response surface curves of protease production as a function of substrate concentration and
fermentation time. Substrate concentration and fermentation time are the most important environmental parameters
influencing the protease production. Figure 10 depicts the interaction of inoculums size and substrate concentration,
where the maximum protease production of 61 U/gds was found to occur with inoculums size of 3ml and substrate
concentration of 8.5 g.




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     ( U /g d s )
     a c tiv ity


                                         80

                                         70

                                          60
     P r o te a s e




                                          50

                                          40

                                              30

                                              20




                                           2.38                                                                                                    2.38
                                                                1.19                                                                 1.19
                                                                          0.00                                         0.00
                                                                                  -1.19                      -1.19
                                        D: Inoculums size (ml)                                                              C: Fermentation Time (hrs)
                                                                                          -2.38    -2.38




Figure 8: Response surface Plot for protease production from groundnut shell by Bacillus cereus in solid state
                      ( U /g d s )




                                                             fermentation as a function of fermentation time and inoculum size
                      a c tiv ity




                                               80

                                                   70

                                                   60
                      P r o te a s e




                                                   50

                                                    40

                                                    30

                                                        20




                                                    2.38                                                                                    2.38
                                                                   1.19                                                           1.19
                                                                           0.00                                      0.00
                                                                                  -1.19                    -1.19
                                       E: substrate concentration (g)                                                 C: Fermentation Time (hrs)
                                                                                          -2.38   -2.38




Figure 9: Response surface Plot for protease production from groundnut shell by Bacillus cereus in solid state
                                              fermentation as a function of fermentation time and substrate concentration




                                                   IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012                                       126
P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129




              ( U /g d s )
              a c tiv ity
                                    80

                                    70

                                    60
              P r o te a s e




                                     50

                                     40

                                         30

                                         20




                                     2.38                                                                               2.38
                                                1.19                                                          1.19
                                                          0.00                                   0.00
                                                                 -1.19                   -1.19
                               E: substrate concentration (g)                                           D: Inoculums size (ml)
                                                                         -2.38   -2.38




   Figure 10: Response surface Plot for protease production from groundnut shell by Bacillus cereus in solid state
                                         fermentation as a function of inoculum size and substrate concentration


        The response surfaces of mutual interactions between the variables were found to be elliptical for most cases.
The stationary point or central point is the point at which the slope of the contour is zero in all directions. The
coordinates of the central point within the highest contour levels in each of these figures will correspond to the
optimum values of the respective constituents. The optimum values drawn from these figures are in close agreement
with those obtained by optimizing the regression model Eq. (3). The sequential quadratic programming in MATLAB 7
is used to solve the second-degree polynomial regression Eq. (3). The optimum values for maximum protease
production were: pH (8.0), temperature (43°C), fermentation time (26 hrs), inoculum level (3.2 ml) and substrate
concentration (9.6g). The optimal values for the variables as predicted by MATLAB were found to be within the
design region. This shows that the model correctly explains the influence of the chosen variables on the protease
production.
Validation of the experimental model:

        Validation of the experimental model was tested by carrying out the batch experiment under optimal
operation conditions pH (8.0), temperature (43°C), fermentation time (26 hrs), inoculum level (3.2 ml) and substrate
concentration (9.6g) established by the regression model. Three repeated experiments were performed and the
results are compared. The protease activity (73.00U/gds) obtained from experiments was close to the actual response
(73.06U/gds) predicted by the regression model, which proved the validity of the model.




                                         IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012                        127
P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129


                                                     CONCLUSION

        The feasibility of using an Agro-residue (groundnut shell) as possible substrate for the protease production
was studied using the response surface methodological approach. The optimum conditions for the maximum protease
production 76.75 U/gds using cassava waste are as follows:           pH 8, temperature 43°C, fermentation time 26hrs,
inoculums size 3.2ml and substrate concentration 9.6g. The enzyme production in this range from this vastly available
by-product is significant.

                                                     REFERENCES
1. Rao MB, Tanksale AM, Ghatge MS, Deshpande VV. Molecular and biotechnological
  aspects of microbial proteases. Microbiol Mol Biol Rev 1998; 62:597–635.
2. A. Pandey, C.R. Soccol, Bioconversion of biomass: a case study of ligno-cellulosics bioconversions
  in solid-state fermentation, Braz. Arch. Biol. Technol. 1998; 41: 379–390.
3. A. Pandey, C.R. Soccol, P. Nigam, V.T. Soccol, Biotechnological potential of agro-industrial
  residues. I. Sugarcane bagasse, Biores. Technol. 2000; 74: 69–80.
4. A. Pandey, C.R. Soccol, P. Nigam, V.T. Soccol, L.P.S. Vandenberghe, R. Mohan, Biotechnological
  potential of agro-industrial residues. II. Cassava bagasse, Biores. Technol. 2000; 74: 81– 87.
5. A. Pandey, P. Selvakumar, C.R. Soccol, P. Nigam, Solid-state fermentation for the production of
  industrial enzymes, Curr. Sci. 1999; 77: 149–162.
6. A. Pandey, C.R. Soccol, Economic utilization of crop residues for the value addition: a futuristic
  approach, J. Sci. Ind. Res. 2000; 59: 12–22.
7. A. Pandey, C.R. Soccol, D. Mitchell, New developments in solid-state fermentation. I. Bioprocesses
  and products, Process Biochem. 2000; 35: 1153–1169.
8. Lin X, Lee CG, Casale ES, Shih JCH. Purification and characterization of a keratinase from a feather-
  degrading Bacillus licheniformis strain. Appl Environ Microbiol 1992;58: 3271–5.
9. A. Pandey, Solid-state fermentation, Biochem. Eng. J. 2003 ; 13:81–84.
10. Gupta R, Beg QK, Lorenz P. Bacterial alkaline proteases: molecular approaches and industrial
  applications. Appl Microbiol Biotechnol 2002; 59:15-/32.
11. Haaland PD. Statistical problem solving. In: Haaland PD, editor. Experimental Design in
   Biotechnology. New York: Marcel Dekker, Inc, 1989;1-/18.
12. Beg QK, Saxena RK, Gupta R. Kinetic constants determination for an alkaline protease from
   Bacillus mojavensis using response surface methodology. Biotechnol Bioeng 2002;78:289-/95.
13. De Coninck J, Bouquelet S, Dumortier V, Duyme F, Denantes VI. Industrial media and
   fermentation process for improved growth and protease production by Tetrahymena thermophila
   BIII. J Ind Microbiol Biotechnol 2000; 24:285-/90.
14. Puri S, Beg QK, Gupta R. Optimization of alkaline protease production from Bacillus sp. using




                       IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012                         128
P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129

   response surface methodology. Curr Microbiol 2002; 44:286-/90.
15. Nagamine K., Murashima K., Kato T., Shimoi H., Ito K., Mode of alpha-amylase production by the Shochu Koji Mold
    Aspergillu skawachii, Biosci. Biotechnol. and Biochem. 2003; 67: 2194–2202.
16. Hagihara B., Matsubara H., Nakai M., Okunuki K., Crystalline bacterial proteinase of Bacillus subtilis, The Journal of
    Biochem. 1958; 45: 185–194.




                       IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012                             129

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Optimizing factors affecting protease production by a bacillus cereus using groundnut shell under solid substrate fermentation

  • 1. P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129 Optimizing factors affecting protease production by a Bacillus cereus using groundnut shell under solid substrate fermentation P. RATHAKRISHNAN, P. NAGARAJAN Department of Chemical Engineering, Annamalai University, Annamalainagar-608002, Tamilnadu, India. ABSTRACT The physical factors affecting the production of protease from bacillus cereus was investigated. Agro industrial waste product groundnut shells were used as substrates in solid-state fermentation for protease enzyme production. The response surface methodology (RSM) was used to optimize protease production by implementing the central-composite design (CCD). The physiological fermentation factors such as pH (8.0), temperature (43°C), fermentation time (26 hrs), inoculum level (3.2 ml) and substrate concentration (9.6g) were optimized by statistical analysis using response surface methodology. The maximum yield of protease production was 76.75U/gds. This was evidenced by the higher value of coefficient of determination (R 2= 0.9994). Keywords: RSM, groundnut shells, protease, CCD, bacillus cereus, physical factors optimization. IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012 114
  • 2. P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129 INTRODUCTION Proteases (EC 3.4) form an important class of commercially important enzymes and find applications in detergent, leather, food and pharmaceutical industries, constituting approximately 40% of the total enzyme market [1]. In recent years, there has been an increasing trend towards efficient utilization and value-addition of agro- industrial residues such as coffee pulp and husk, cassava husk, cassava bagasse, sugarcane bagasse, sugar beet pulp, apple pomace, declassified potatoes, etc. [2–7]. Bacillus strains are known to produce and secrete large quantities of extracellular enzymes and constitute a major group of industrial enzyme producers due to the robust nature of the organism as well as its enzymes [8]. Bacterial systems are being increasingly investigated for the production of enzymes and metabolites by SSF. Solid-state fermentation (SSF) has been defined as the fermentation process which involves solid matrix and is carried out in absence or near absence of free water; however, the substrate must Possess enough moisture to support growth and metabolism of the microorganism. The solid matrix could be either the source of nutrients or simply a support impregnated by the proper nutrients that allows the development of the microorganisms. [9] Statistical approaches offer ideal ways for process optimization studies in biotechnology [10, 11]. Response surface methodology (RSM) is now being routinely used for optimization studies in several biotechnological and industrial processes [12-14].Here, we report the use of RSM to study the effects various physicochemical factors on protease production from B. cereus As the demand for proteases increases, it is expected that hyperactive strains will emerge and that the enzymes produced by new strains could be used as catalysts in different industries. In the present work, the strain employed was a strain of Bacillus sp. producing alkaline protease in SSF using agro-residue groundnut shell substrate. In this paper, we report on factors that influence the maximization of alkaline protease production by Bacillus cereus through SSF. MATTERIALS AND METHODS Microorganism and inoculums’ preparation: Bacterial strain used in this work is well preserved in the laboratory. Bacterial strain Bacillus cereus was a stock of the Microbial Type Culture collection Centre (MTCC), Chandigarh, India. The strain was maintained on nutrient agar medium at 4◦C. The medium composition (g/l) was comprised off the following: Beef extract 1.0; Yeast extract 2.0; Peptone 5.0; NaCl 5.0 and Agar 2.0. Cells were subcultered at monthly intervals. Solid-state fermentation: Groundnut shell was collected from local market in Panruti, Tamilnadu, India. The shells were washed thoroughly with tap water and then dried. The dried materials obtained were milled and sieved to powder for using as a carbon source for protease production. Fermentation was carried out in Erlenmeyer flasks (250 ml) with 10g of IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012 115
  • 3. P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129 Groundnut shell powder, supplemented with nutrients concentrations defined by the experimental design NaNO3 0.0386g/gds, Tryptone 0.0338g/gds, K2HPO4 0.2373g/gds and Malt extract 0.2090g/gds. Each flask was covered with hydrophobic cotton and autoclaved at 121°C for 15 min. After cooling the flasks to room temperature, the flasks were inoculated with 2 ml 24-h grown culture broth under sterile conditions. The contents of the flasks were well mixed and incubated at 33±1ºC for 120 hrs. During the preliminary screening process, the experiments are carried out for 5 days and it was found that at the 28 hrs, the maximum production occurs. Hence experiments are carried out for 28 hrs. Extraction of protease: The enzyme was extracted according to the method described by Nagamine et al. (2003) [15]. Fermented medium was mixed thoroughly with 50 mM glycine–NaOH buffer, pH 11 for 30 min and the extract was separated by squeezing through a cloth. This process was repeated three times and extracts were pooled together and then centrifuged. The supernatant was used as enzyme source for protease assay. Optimization of process parameters: A full factorial design, which includes all possible factor combinations in each of the factors, is a powerful tool for understanding complex processes for describing factor interactions in multifactor systems. RSM is an empirical statistical technique employed for multiple regression analysis by using quantitative data obtained from properly designed experiments to solve multivariate equations simultaneously. The experiments with different pH, temperature, fermentation time, innoculum size and substrate conc. were employed, simultaneously covering the spectrum of variables for the production of protease in the central composite design. In order to describe the effects of pH, temperature, fermentation time, inoculums size and substrate conc. on the protease production, batch experiments were conducted. The coded values of the process parameters were determined by the following equation. Xi  X x  o (1) i Δx Where xi-coded value of the ith variable, Xi-uncoded value of the ith test variable and X0-uncoded value of the ith test variable at center point. The range and levels of individual variables are given in Table 2. The experimental design is given in Table 3, along with experimental data and predicted responses. The regression analysis was performed to estimate the response function as a second order polynomial. k k 2 k -1 k Y  β 0   βi X   β ii X    β ij X i X j (2) i1 i i1 i i1,i j j2 Where Y is the predicted response, βi, βj, βij are coefficients estimated from regression. They represent the linear, quadratic and cross products of X1, X2, and X3 on response IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012 116
  • 4. P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129 Levels Variable Code -2.38 -1 0 +1 +2.38 pH A 6 7 8 9 10 Temperature ( o C) B 30 35 40 45 50 Fermentation Time (hrs) C 8 16 24 32 40 Inoculums size (ml) D 1 2 3 4 5 substrate concentration E 3 6 9 12 15 (g) Table 2: Levels of different process variables in coded and un-coded form for protease production independent variable range and levels Run A-pH B- C- D- E- Protease activity(u/gds) . No Tempera Fermenta Inoculums substrate Experimental Theoretical ture tion time Size concentra tion 1 0.00000 0.00000 0.00000 0.00000 0.00000 73.00 73.0603 2 2.37841 0.00000 0.00000 0.00000 0.00000 62.70 62.6081 3 1.00000 1.00000 -1.00000 -1.00000 1.00000 53.50 53.2997 4 -1.00000 -1.00000 1.00000 1.00000 -1.00000 50.00 49.9518 5 -2.37841 0.00000 0.00000 0.00000 0.00000 56.00 55.8666 6 0.00000 0.00000 -2.37841 0.00000 0.00000 60.00 59.8516 7 -1.00000 1.00000 -1.00000 -1.00000 1.00000 49.20 49.6277 8 1.00000 -1.00000 -1.00000 -1.00000 1.00000 62.30 62.2990 9 1.00000 1.00000 1.00000 -1.00000 1.00000 61.00 61.2235 10 1.00000 1.00000 1.00000 1.00000 1.00000 68.50 68.7547 11 1.00000 1.00000 -1.00000 -1.00000 -1.00000 53.00 53.1257 12 -1.00000 1.00000 -1.00000 1.00000 -1.00000 62.00 61.7850 IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012 117
  • 5. P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129 13 -1.00000 1.00000 -1.00000 1.00000 1.00000 58.00 58.0715 14 -1.00000 1.00000 1.00000 1.00000 -1.00000 63.40 63.7275 15 1.00000 1.00000 1.00000 1.00000 -1.00000 60.00 59.9807 16 -1.00000 -1.00000 -1.00000 1.00000 1.00000 50.00 50.2833 17 0.00000 0.00000 0.00000 0.00000 0.00000 73.21 73.0603 18 0.00000 0.00000 0.00000 0.00000 2.37841 56.00 55.6909 19 0.00000 0.00000 0.00000 0.00000 0.00000 73.00 73.0603 20 -1.00000 -1.00000 -1.00000 -1.00000 -1.00000 61.10 61.1468 21 1.00000 -1.00000 -1.00000 1.00000 -1.00000 60.00 60.0563 22 0.00000 0.00000 0.00000 0.00000 0.00000 73.00 73.0603 23 1.00000 -1.00000 1.00000 1.00000 1.00000 58.90 58.7541 24 -1.00000 -1.00000 1.00000 1.00000 1.00000 50.00 50.2884 25 0.00000 0.00000 0.00000 0.00000 0.00000 73.00 73.0603 26 0.00000 0.00000 2.37841 0.00000 0.00000 62.20 62.1231 27 0.00000 0.00000 0.00000 0.00000 0.00000 73.00 73.0603 28 1.00000 1.00000 -1.00000 1.00000 -1.00000 60.00 60.1320 29 1.00000 -1.00000 1.00000 -1.00000 -1.00000 58.00 58.0425 30 1.00000 -1.00000 1.00000 1.00000 -1.00000 52.00 51.9488 31 -1.00000 -1.00000 1.00000 -1.00000 -1.00000 57.40 57.1893 32 0.00000 0.00000 0.00000 0.00000 0.00000 73.21 73.0603 33 -1.00000 -1.00000 -1.00000 -1.00000 1.00000 53.00 52.8833 34 -1.00000 -1.00000 1.00000 -1.00000 1.00000 55.00 54.9446 IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012 118
  • 6. P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129 35 0.00000 0.00000 0.00000 0.00000 0.00000 73.21 73.0603 36 1.00000 1.00000 1.00000 -1.00000 -1.00000 55.35 55.0307 37 1.00000 -1.00000 1.00000 -1.00000 1.00000 62.00 62.2665 38 1.00000 -1.00000 -1.00000 -1.00000 -1.00000 64.00 64.0937 39 -1.00000 1.00000 1.00000 1.00000 1.00000 66.30 66.0328 40 0.00000 -2.37841 0.00000 0.00000 0.00000 59.00 59.0473 41 -1.00000 -1.00000 -1.00000 1.00000 -1.00000 56.00 55.9656 42 -1.00000 1.00000 1.00000 -1.00000 -1.00000 59.60 59.9212 43 -1.00000 1.00000 1.00000 -1.00000 1.00000 59.70 59.6453 44 0.00000 0.00000 0.00000 -2.37841 0.00000 42.00 41.9400 45 0.00000 0.00000 0.00000 2.37841 0.00000 44.90 44.7347 46 1.00000 1.00000 -1.00000 1.00000 1.00000 62.60 62.8872 47 0.00000 2.37841 0.00000 0.00000 0.00000 65.00 64.7274 48 -1.00000 1.00000 -1.00000 -1.00000 -1.00000 56.00 55.9225 49 0.00000 0.00000 0.00000 0.00000 -2.37841 55.00 55.0838 50 1.00000 -1.00000 -1.00000 1.00000 1.00000 61.00 60.8428 Table 3: Experimental conditions and observed response values of 25 Central Composite Design A statistical program package Design Expert 7.1.5, was used for regression analysis of the data obtained and to estimate the coefficient of the regression equation. The equations were validated by the statistical tests called the ANOVA analysis. The significance of each term in the Equation is to estimate the goodness of fit in each case. Response surfaces were drawn to determine the individual and interactive effects of the test variable on the protease production. The optimal values of the test variables were first obtained in coded units and then converted to the uncoded units. IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012 119
  • 7. P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129 Protease assay: Protease activity was determined using modified Auson–Hagihara method [16]. In this 1 ml of the enzyme solution was added to 1 ml casein solution (1%, w/v casein solution prepared in 50 mM glycine–NaOH buffer, pH 11) and incubated at 70ºC for 20 min. The reaction was terminated by adding 4 ml of 10% trichloroacetic acid and the contents were filtered through a Whatman No. 1 filter paper. The filtrate absorbance was read at 280 nm using UV– Visible spectrophotometer and the protease activity was calculated using tyrosine standard curve. One unit of alkaline protease activity was defined as 1 µg of tyrosine liberated ml -1 under the assay conditions. RESULT AND DICUSSION To examine the combined effect of five different process parameters (independent variables), on the protease production, a central composite design of 25 = 32 plus 8 centre points and (2x5 = 10) star points leading to a total of 50 experiments were performed. Equation (3) represents the mathematical model relating the protease production and the second order polynomial coefficient for each term of the equation determined through multiple regression analysis using the Design Expert 7.1.5.The coded values of the independent variables are given in Table 2. The experimental and predicted values of protease production are also given in table 3. The results were analyzed by using ANOVA i.e., analysis of variance suitable for the experimental design used and cited in Table 4. The ANOVA of the quadratic regression model indicates the model to be significant. The Model F- value of 2602.26 implied the model to be significant. Model F-value was calculated as a ratio of mean square regression and mean square residual. Model P value (Prob>F) is very low [0.0500]. This reiterates that the model is significant. The P values are used as a tool to check the significance of each of the coefficients, which in turn are necessary to understand the pattern of the mutual interactions between the test variables. The F value and the corresponding P values, along with the coefficient estimate are given in Table 4. The smaller the magnitude of the P, the more significant is the corresponding coefficient. Values of P less than 0.0500 indicates the model terms to be significant. The coefficient estimates and the corresponding P values along with the coefficient estimate are given in table 4.The coefficients estimate and the corresponding P values suggests that, among the test variables used in the study, A, B, C, D, E, AB, AC, AD, AE, BC, BD, BE, CD, CE, DE, A 2, B2, C2, D2, E2 are significant [where A-pH, B- temperature, C-fermentation time, D-inoculums size and E-substrate conc.] model terms. Values greater than 0.1000 indicate the model terms are not significant. IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012 120
  • 8. P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129 Source Coefficient Sum of DF Mean F P value factor square square P>F Model 73.06 2891.74 20 144.59 2602.26 < 0.0001 87.00 1 87.00 1565.76 < 0.0001 A 1.42 61.76 61.76 1111.53 <0.0100 9.88 1 9.88 177.75 < 0.0001 B 1.19 14.95 1 14.95 269.08 <0.0001 0.71 1 0.71 12.70 0.0013 C 0.48 1 331.80 331.80 5971.73 <0.0001 216.78 1 216.78 3901.51 <0.0001 D 0.59 253.11 1 253.11 4555.38 <0.0001 1534.14 1 1534.14 2761.18 <0.0001 E 0.13 542.37 1 542.37 9761.52 < 0.0001 A*A -2.44 65.98 1 65.98 1187.52 <0.0001 8.77 1 8.74 157.80 <0.0001 B*B -1.98 2.62 1 2.62 47.09 <0.0001 83.69 1 83.69 1506.23 <0.0001 C*C -2.13 126.60 1 126.60 2278.60 <0.0001 243.93 1 243.93 4390.20 < 0.0001 D*D -5.25 7.75 1 7.75 139.52 < 0.0001 8.46 1 8.46 152.20 <0.0001 E*E -3.12 1 72.45 72.45 1303.96 13.33 1 13.33 <0.0001 A*B -1.44 1.61 1 0.056 239.83 < 0.0001 A*C -0.52 29 0.069 1.53 5.88 0.0112 A*D 0.29 0.083 22 0.012 2893.36 7 A*E 1.62 49 B*C 1.99 B*D 2.76 B*E 0.49 C*D -0.51 C*E 1.50 D*E 0.65 Residua Lack of fit Pure Error Cor Total IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012 121
  • 9. P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129 Table 4: Analysis of Variance (ANOVA) for Response Surface Quadratic Model Std. Dev. 0.24; R2 = 99.94%; R2(pred) 99.80%; R2(adj) 99.91%; C.V. % 0.39 Protease activity = +73.06+1.42* A+1.19 * B+0.48 * C+0.59 * D+0.13* E -1.44 * A * B-5.2 * A * C+0.29 * A * D+1.62 * A * E+1.99 * B * C+2.76 * B * D +0.49 * B * E-0.52 * C * D+1.50 * C * E+0.65 * D * E -2.44 *A2 -1.98 * B2-2.13 * C2- 5.25 * D2-3.12 * E2 The predicted R2 of 0.9980 is in reasonable agreement with the adjusted R 2 of 0.9991. Adequate precision measures the signal to noise ratio. A ratio greater than 4 is desirable. The fit of the model was also expressed by the coefficient of regression R2, which was found to be 0.9994 indicating from properly that 99.94% the variability in the response could be explained by the model. This implies that the prediction of experimental data is quite satisfactory. The Coefficient of Variation (CV) indicates the degree of precision with which the treatments are compared. Usually, the higher the value of the CV is, the lower the reliability of the experiment. Here a lower value of CV (0.39) indicates greater reliability of the experiments performed. The Response surface estimation for protease production as discussed in the previous section, the response surface methodology was used with five process variables to evaluate their effect on the protease production. The response Eq. (3) was obtained for the protease production. To investigate the interactive effect of two factors on the protease production, the response surface methodology was used and three-dimensional plot was drawn. The inferences so obtained are discussed below. The interaction effects and optimal levels of the variables were determined by plotting the response surface curves. The 3D response surface curves are shown in Figs. 1 to 10. Figure 1 represents the interactive effect of pH and temperature on protease production. From Fig. 1 it was inferred that with the increase in pH, the protease production increases with the temperature. The optimum value of both the factors, viz, pH and temperature can be analyzed by saddle point or by checking the maxima formed by the X and Y coordinates. The combined effect of pH and fermentation time on protease production shown in Fig 2. ( U /g d s ) a c tiv ity 80 70 60 P r o te a s e 50 40 30 2.38 2.38 1.19 1.19 0.00 0.00 -1.19 -1.19 B: Temperature ( o C) A: pH -2.38 -2.38 Figure 1: Response surface Plot for protease production from groundnut shell by Bacillus cereuss in solid state fermentation as a function of pH and Temperature IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012 122
  • 10. P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129 ( U /g d s ) a c tiv ity 80 70 60 P r o te a s e 50 40 30 2.38 2.38 1.19 1.19 0.00 0.00 -1.19 -1.19 C: Fermentation Time (hrs) A: pH -2.38 -2.38 Figure 2: Response surface Plot for protease production from groundnut shell by Bacillus cereus in solid state ( U /g d s ) fermentation as a function of pH and fermentation time a c tiv ity 80 70 60 P r o te a s e 50 40 30 20 2.38 2.38 1.19 1.19 0.00 0.00 -1.19 -1.19 D: Inoculums size (ml) A: pH -2.38 -2.38 Figure 3: Response surface Plot for protease production from groundnut shell by Bacillus cereus in solid state fermentation as a function of pH and inoculums size IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012 123
  • 11. P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129 ( U /g d s ) a c tiv ity 80 70 60 P r o te a s e 50 40 30 20 2.38 2.38 1.19 1.19 0.00 0.00 -1.19 -1.19 E: substrate concentration (g) A: pH -2.38 -2.38 Figure 4: Response surface Plot for protease production from groundnut shell by Bacillus cereus in solid state fermentation as a function of pH and substrate concentration Figure 3 depicts the interaction of pH and Inoculums size, Figure 4 shows the effect of pH and substrate concentration on the protease production. The shape of the contour show good interaction between the pH and substrate concentration, which is clearly illustrated in Fig. 4. The combined effect of temperature and fermentation time was shown in the form of 3D plot in Fig. 5 Combined effect of temperature and innoculum size has been analyzed ( U /g d s ) from the CCD three-dimensional plot shown in Fig. 6. a c tiv ity 80 70 60 P r o te a s e 50 40 30 2.38 2.38 1.19 1.19 0.00 0.00 -1.19 -1.19 C: Fermentation Time (hrs) B: Temperature ( o C) -2.38 -2.38 Figure 5: Response surface Plot for protease production from groundnut shell by Bacillus cereus in solid state fermentation as a function of temperature and fermentation time IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012 124
  • 12. P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129 ( U /g d s ) a c tiv ity 80 70 60 50 P r o te a s e 40 30 20 10 2.38 2.38 1.19 1.19 0.00 0.00 -1.19 -1.19 D: Inoculums size (ml) B: Temperature ( o C) -2.38 -2.38 Figure 6: Response surface Plot for protease production from groundnut shell by Bacillus cereus in solid state ( U /g d s ) fermentation as a function of temperature and inoculum size a c tiv ity 80 70 60 50 P r o te a s e 40 30 20 10 2.38 2.38 1.19 1.19 0.00 0.00 -1.19 -1.19 E: substrate concentration (g) B: Temperature ( o C) -2.38 -2.38 Figure 7: Response surface Plot for protease production from groundnut shell by Bacillus cereus in solid state fermentation as a function of temperature and substrate concentration Figure 7 shows the effect of temperature and substrate concentration on the protease production. Figure 8 shows the response surface curves of protease production as a function of inoculums size and fermentation time. Figure 9 shows the response surface curves of protease production as a function of substrate concentration and fermentation time. Substrate concentration and fermentation time are the most important environmental parameters influencing the protease production. Figure 10 depicts the interaction of inoculums size and substrate concentration, where the maximum protease production of 61 U/gds was found to occur with inoculums size of 3ml and substrate concentration of 8.5 g. IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012 125
  • 13. P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129 ( U /g d s ) a c tiv ity 80 70 60 P r o te a s e 50 40 30 20 2.38 2.38 1.19 1.19 0.00 0.00 -1.19 -1.19 D: Inoculums size (ml) C: Fermentation Time (hrs) -2.38 -2.38 Figure 8: Response surface Plot for protease production from groundnut shell by Bacillus cereus in solid state ( U /g d s ) fermentation as a function of fermentation time and inoculum size a c tiv ity 80 70 60 P r o te a s e 50 40 30 20 2.38 2.38 1.19 1.19 0.00 0.00 -1.19 -1.19 E: substrate concentration (g) C: Fermentation Time (hrs) -2.38 -2.38 Figure 9: Response surface Plot for protease production from groundnut shell by Bacillus cereus in solid state fermentation as a function of fermentation time and substrate concentration IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012 126
  • 14. P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129 ( U /g d s ) a c tiv ity 80 70 60 P r o te a s e 50 40 30 20 2.38 2.38 1.19 1.19 0.00 0.00 -1.19 -1.19 E: substrate concentration (g) D: Inoculums size (ml) -2.38 -2.38 Figure 10: Response surface Plot for protease production from groundnut shell by Bacillus cereus in solid state fermentation as a function of inoculum size and substrate concentration The response surfaces of mutual interactions between the variables were found to be elliptical for most cases. The stationary point or central point is the point at which the slope of the contour is zero in all directions. The coordinates of the central point within the highest contour levels in each of these figures will correspond to the optimum values of the respective constituents. The optimum values drawn from these figures are in close agreement with those obtained by optimizing the regression model Eq. (3). The sequential quadratic programming in MATLAB 7 is used to solve the second-degree polynomial regression Eq. (3). The optimum values for maximum protease production were: pH (8.0), temperature (43°C), fermentation time (26 hrs), inoculum level (3.2 ml) and substrate concentration (9.6g). The optimal values for the variables as predicted by MATLAB were found to be within the design region. This shows that the model correctly explains the influence of the chosen variables on the protease production. Validation of the experimental model: Validation of the experimental model was tested by carrying out the batch experiment under optimal operation conditions pH (8.0), temperature (43°C), fermentation time (26 hrs), inoculum level (3.2 ml) and substrate concentration (9.6g) established by the regression model. Three repeated experiments were performed and the results are compared. The protease activity (73.00U/gds) obtained from experiments was close to the actual response (73.06U/gds) predicted by the regression model, which proved the validity of the model. IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012 127
  • 15. P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129 CONCLUSION The feasibility of using an Agro-residue (groundnut shell) as possible substrate for the protease production was studied using the response surface methodological approach. The optimum conditions for the maximum protease production 76.75 U/gds using cassava waste are as follows: pH 8, temperature 43°C, fermentation time 26hrs, inoculums size 3.2ml and substrate concentration 9.6g. The enzyme production in this range from this vastly available by-product is significant. REFERENCES 1. Rao MB, Tanksale AM, Ghatge MS, Deshpande VV. Molecular and biotechnological aspects of microbial proteases. Microbiol Mol Biol Rev 1998; 62:597–635. 2. A. Pandey, C.R. Soccol, Bioconversion of biomass: a case study of ligno-cellulosics bioconversions in solid-state fermentation, Braz. Arch. Biol. Technol. 1998; 41: 379–390. 3. A. Pandey, C.R. Soccol, P. Nigam, V.T. Soccol, Biotechnological potential of agro-industrial residues. I. Sugarcane bagasse, Biores. Technol. 2000; 74: 69–80. 4. A. Pandey, C.R. Soccol, P. Nigam, V.T. Soccol, L.P.S. Vandenberghe, R. Mohan, Biotechnological potential of agro-industrial residues. II. Cassava bagasse, Biores. Technol. 2000; 74: 81– 87. 5. A. Pandey, P. Selvakumar, C.R. Soccol, P. Nigam, Solid-state fermentation for the production of industrial enzymes, Curr. Sci. 1999; 77: 149–162. 6. A. Pandey, C.R. Soccol, Economic utilization of crop residues for the value addition: a futuristic approach, J. Sci. Ind. Res. 2000; 59: 12–22. 7. A. Pandey, C.R. Soccol, D. Mitchell, New developments in solid-state fermentation. I. Bioprocesses and products, Process Biochem. 2000; 35: 1153–1169. 8. Lin X, Lee CG, Casale ES, Shih JCH. Purification and characterization of a keratinase from a feather- degrading Bacillus licheniformis strain. Appl Environ Microbiol 1992;58: 3271–5. 9. A. Pandey, Solid-state fermentation, Biochem. Eng. J. 2003 ; 13:81–84. 10. Gupta R, Beg QK, Lorenz P. Bacterial alkaline proteases: molecular approaches and industrial applications. Appl Microbiol Biotechnol 2002; 59:15-/32. 11. Haaland PD. Statistical problem solving. In: Haaland PD, editor. Experimental Design in Biotechnology. New York: Marcel Dekker, Inc, 1989;1-/18. 12. Beg QK, Saxena RK, Gupta R. Kinetic constants determination for an alkaline protease from Bacillus mojavensis using response surface methodology. Biotechnol Bioeng 2002;78:289-/95. 13. De Coninck J, Bouquelet S, Dumortier V, Duyme F, Denantes VI. Industrial media and fermentation process for improved growth and protease production by Tetrahymena thermophila BIII. J Ind Microbiol Biotechnol 2000; 24:285-/90. 14. Puri S, Beg QK, Gupta R. Optimization of alkaline protease production from Bacillus sp. using IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012 128
  • 16. P. Rathakrishnan et al., IJSIT, 2012, 1(2), 114-129 response surface methodology. Curr Microbiol 2002; 44:286-/90. 15. Nagamine K., Murashima K., Kato T., Shimoi H., Ito K., Mode of alpha-amylase production by the Shochu Koji Mold Aspergillu skawachii, Biosci. Biotechnol. and Biochem. 2003; 67: 2194–2202. 16. Hagihara B., Matsubara H., Nakai M., Okunuki K., Crystalline bacterial proteinase of Bacillus subtilis, The Journal of Biochem. 1958; 45: 185–194. IJSIT (www.ijsit.com), Volume 1, Issue 2, November-December 2012 129