Optimization of Amylase Production from Bacillus sp

Published: Last Edited:

This essay has been submitted by a student. This is not an example of the work written by our professional essay writers.

Using Statistics Based Experimental Design


Production of amylase under submerged fermentation Bacillus sp. was investigated using wheat bran, soybean meal and CaCO3 (WSC) medium. Response surface methodology (RSM) was used to evaluate the effect of the main variables, i.e., pH (11.35), temperature (35.16oC) and inoculum size (2.95%) on amylase production by applying a full factorial central composite design (CCD). The mutual interaction between these variables resulted into 4.64 fold increase in amylase activity as compared to the non-optimized environmental factors in the basal medium.

Key words

Amylase, Bacillus sp., central composite design, response surface methodology


Amylases are the hydrolytic enzymes that cleave the α- 1-4 glucosidal linkage of complex polysaccharides. Amylases are obtained from various origins like plant, animal, bacterial and fungal. Amylase production was achieved by many researchers using Bacillus sp. (Yuguo et al., 2000; Young et al., 2001;Dharani, 2004; Zambare, 2010a) Amylase has many applications in food, textile, paper and pulp, pharmaceuticals, baking and beverages, detergent and leather industries (Pandey et al., 2000; Reddy et al., 2003; Kar et al., 2010). Industrially important enzymes including amylases have traditionally been obtained from submerged cultures because of easy handling, greater control of environmental and nutritional factors. The most frequently used operation in biotechnology is to improve the fermentation conditions for maximizing cell density, high level of desired metabolic product or enzyme levels in microbial system. This approach is time consuming and also ignores the combined interactions between physical as well as nutritional factors.

In contrast, RSM includes factorial design and regression analysis which helps in evaluating the effective factors and their interaction and to find out the optimum conditions of variables for a desirable response (Tunga and Banerjee, 1999; Coninck et al., 2000; Reddy et al., 2003; Kunamneni et al., 2005; Gangadharan et al., 2008). Recently, a number of statistical experimental designs with response surface methodology have been employed for optimizing enzyme production from microorganisms (Koteswara et al., et al., 2006; Thys et al., 2006; Zambare, 2010b; Mohandas et al., 2010). However, 3D and counter plots for response surfaces can provide a good way for visualizing the parameter interaction. Therefore, statistical technique is often used for predicting optimum process conditions for microbial enzyme production. It is well known that extracellular enzyme production in microorganisms is greatly influenced by nutritional factors like carbon sources, nitrogen sources and mineral salts (De et al., 2001; Adinarayana and Elliaiah, 2002; Chauhan and Gupta, 2004). Enhancement in extracellular amylase production from Bacillus sp. by environmental factor optimization has not been attempted so far. Therefore, considering the many industrial applications of amylase, we report here the optimization of extracellular amylase production from Bacillus sp. as a result of the interactive effects of three variables (i.e. pH, temperature and inoculum size) using response surface methodology.

Materials and Methods


Bacillus sp. isolated from soil showed true potential in extracellular amylase secretion. It was maintained on 2% Nutrient agar slants at 40C and also as a glycerol stocks at -200C.

Chemicals and media

Chemicals and media were all of analytical grade and purchased from Sigma (St Louis, MO, USA).

Production medium

Production medium containing wheat bran (1%), soybean meal (1%) and CaCO3 (0.3%), was used for the growth and amylase production by Bacillus sp.

Inoculum preparation

Seed inoculum was prepared by growing the isolate on Nutrient agar in Roux bottle at 30°C for 24h. The cells were suspended in saline and cell density was measured spectrophotometrically (Shimatzu UV-2501 PC, Japan) at 600 nm.

Experimental design and optimization by RSM

In the RSM the interactive effects of three variables, i.e. pH, temperature and inoculum size was studied for amylase production. Each factor in the CCD was studied at three different levels (-1, 0, +1). The minimum and maximum ranges of variables were investigated with respect to their values in actual and coded form (Table 1). To optimize the conditions for amylase production, Design-Expert 8.0 CCD-RSM software (State-Ease, Minneapolis, MN, U.S.A.) was used. A 23 factorial CCD proposed by Box et al., (1978) with three factors leading to a total of 20 sets per experiment was formulated to optimize the process parameters. This experiment included 8 factorial design, 6 star and 6 central points. All the variables i.e. pH, temperature and inoculum size were taken at a central coded value and considered as zero. The conditions of these environmental factors for the production medium were varied according to the experimental design (Table 2). All the experiments were carried out in duplicates. The experiments were conducted in 250 ml Erlenmeyer flasks containing 100 ml of sterilized WSC medium of different pH 10-12, inoculated with the freshly prepared 1-5% (2 x 108 cells/ml) inoculum (as discussed earlier) and incubated for 12 hrs at 30-400C under shaking culture condition (150 rpm). After fermentation, the cell-free supernatant was obtained by centrifugation at 10,000 rpm and used for amylase activity.

Using RSM, the relationship among the variables, i.e. pH, temperature and inoculum size were expressed mathematically in the form of a polynomial model, which gave the response as a function of relevant variables. The present work was based on the CCD to obtain the experimental data, which would fit in an empirical, full second-order polynomial model representing the response surfaces over a relatively broad range of parameters as show in Eq. (1).

........................... (1)

where, y was the predicted response (amylase production) used as a dependent variable; xi (i = 1, 2 and 3) were the input predictors or controlling variables; and a0, ai (i = 1, 2, 3) and aij (i = 1, 2, 3; j = i, . . . , 3) were the model coefficient parameters. The coefficient parameters were estimated by multiple linear regression analysis using the least-squares method. A second-order polynomial equation was then fitted to the data by least-squares optimization technique. This resulted in an empirical model that related the response measured to the independent variables of the experiment.

Assay of amylase

The amylase activity in the cell free supernatant (CFS) was measured by incubating 0.5 ml of CFS with 0.5 ml of 2% (w/v) starch at 370C in 2 ml phosphate buffer (0.1 M, pH 6.0). The reducing sugars released were measured by 3,5-dinitrosalicylic acid method (Miller, 1959). A separate blank was set for each sample to correct the non-enzymatic release of sugars. One unit of amylase was defined as the amount of enzyme that released 1 µg of reducing sugar as maltose per minute under the standard assay conditions.

Results and Discussion

RSM had not only been used for optimization of medium components in the fermentation process (Puri et al., 2002) but also for studying the combined effects of culture parameters (Dutta et al., 2004; Nawani and Kapadnis, 2005). A submerged culture was used for the production of extracellular amylase from Bacillus sp. Preliminary experiments on amylase production from the above strain indicated that the most important environmental factors were pH, temperature and inoculum size. Hence these three factors were considered as the independent variables and their effect on amylase production was studied using a CCD of RSM. The results of CCD experiments for studying the effects of three independent variables, viz., pH, temperature and inoculum size, on amylase production are presented in Table 3 along with the predicted and observed responses. The standard deviations on the observed responses are also presented in Table 3. The coefficients of the model were determined of least-squares optimization by the Gauss-Newton technique (Table 4). The overall second order polynomial equation for amylase production is given in Eq. (2).

Amylase activity (y) = 511.77 + 35.77X1 - 7.75X2 - 42.17X3 + 21.25X12 - 12.19X23 + 0.31X13 - 21.83X11 -184.33X22 - 219.75X33 ........................... (2)

where, X1-pH, X2-temperature in degree Celsius and X3-inoculum size in %. The larger the magnitudes of F- value, smaller is the p- value, the more significant value is the corresponding coefficient (Akhnazarova and Kafarov, 1982; Rubinder et al., 2002). The results of the second order response surface model fitting in the form of analysis of variance (ANOVA) are in Table. 5. The fisher F-test with a very low probability value demonstrated a very high significance for the regression model (Olivera et al., 2004; Zambare, 2010b). The fitting of the model was checked by the determination coefficient (R2). In this case, the value of the determination coefficient (R2= 0.818) indicates that only 18.2% of the total variations are not explained by the model. The value of the adjusted determination coefficient (Adj. R2= 0.655) is also high, which indicates a higher significance (p value < 0.01) of the model (Adinarayana and Elliaiah, 2002; Olivera et al., 2004). Adequate precision measures the signal to noise ratio. An adequate precision value (5.74) was greater than 4 which indicates adequate signal. At the same time a relatively lower value of the coefficient of variation (CV=42.70) indicates improved precision and reliability of the conducted experiments (Adinarayana and Elliaiah, 2002).

The 3-D counter plots for response surfaces corresponding to the combined effects of pH-temperature (Fig. 1), pH-inoculum (Fig. 2) and temperature-inoculum (Fig. 3) were plotted. The response surfaces obtained were suggesting that Bacillus sp. secreted amylase in more alkaline condition at moderate temperature and inoculum size. Thus the optimum operating conditions obtained from the RSM model were pH (11.35), temperature (35.160C) and inoculum (2.95%) with predicted amylase activity of 515.30 U/ml. After optimization, 4.64 fold amylase activity (515.30 U/ml) was enhanced when compared with non-optimized environmental factors (pH 10, temperature 30oC, inoculum size 1%) in basal medium (110.83 U/ml). Thus, RSM could be a very powerful and flexible tool for modeling the fermentation process due to corrective action arising from methodology and the associated estimation procedure. The application of properly designed models with multi-factor analysis allows process and biochemical engineers to design scale up strategies for increasing enzyme production.


The result obtained in the present study indicated that Bacillus sp. could be a potential strain for amylase production in submerged fermentation using wheat bran like easily available carbon substrates. The RSM allowed the optimization of process parameters such as pH (11.35), temperature (35.160C) and inoculum size (2.95%) for attaining higher yield of amylase.