An Application of Response Surface Methodology in Microbial Degradation of Azo Dye by Bacillus ...

Maulin P Sha, Kavita A Patel, Sunu S Nair, A M Darji

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An Application of Response Surface Methodology in Microbial Degradation of Azo Dye by Bacillus subtillis ETL-1979

Maulin P Sha1,, Kavita A Patel1, Sunu S Nair1, A M Darji1

1Industrial Waste Water Research Laboratory, Applied & Environmental Microbiology Lab, Enviro Technology Limited (CETP), Gujarat, India

Abstract

This research article deals with biodegradation of azo dyes by a newly isolated bacterial strain from activated sludge. Azo dyes are recalcitrant to the conventional modes of treatment due to their complex structure. This article reports decolorization of azo dye by, Bacillus subtillis ETL-1979. Response surface methodology was used to optimize the important physical parameters screened by Placket–Burman design. Five physical parameters such as pH, temperature (°C), dye concentration (mg/L), inoculum size % (v/v) and time (h) were tested by using Placket–Burman design criterion and all five parameters showed significant effect (P < 0.05) on decolorization of azo dye orange using Bacillus subtillis ETL-1979. The values of parameters was optimized by applying central composite design (CCD) and the most suitable values for orange dye decolorization by Bacillus subtillis ETL-1979, as predicted by the statistical tool, was pH 6.9; temperature 37.0°C; dye concentration 517 mg/L, inoculum size, v/v, (%) 5.5 % and time 23.7 h. At these optimum levels of parameters, bacterial decolorization of orange dye by 94.48% was obtained under static conditions. Biodegradation and decolorization of azo dye, orange, was confirmed using UV-VIS spectrophotometry, thin layer chromatography (TLC) and fourier transform infrared spectroscopy (FTIR) and electron spray ionization mass spectrometry (ESI-MS) analysis.

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Cite this article:

  • Sha, Maulin P, et al. "An Application of Response Surface Methodology in Microbial Degradation of Azo Dye by Bacillus subtillis ETL-1979." American Journal of Microbiological Research 2.1 (2014): 24-34.
  • Sha, M. P. , Patel, K. A. , Nair, S. S. , & Darji, A. M. (2014). An Application of Response Surface Methodology in Microbial Degradation of Azo Dye by Bacillus subtillis ETL-1979. American Journal of Microbiological Research, 2(1), 24-34.
  • Sha, Maulin P, Kavita A Patel, Sunu S Nair, and A M Darji. "An Application of Response Surface Methodology in Microbial Degradation of Azo Dye by Bacillus subtillis ETL-1979." American Journal of Microbiological Research 2, no. 1 (2014): 24-34.

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1. Introduction

Rapid urbanization and industrialization has lead to a vast release of waste to the environment adding to the pollution load. Majority of colored effluents contains dyes released from textile, dyestuff and dyeing industries [17]. There are over 10,000 commercially available dyes with a production of over 7 × 105 tons per year [6]. Azo dyes, accounts for almost 60 to 70% of all the synthetic dyes produced globally. They are extensively used in the textile, paper, food, leather, cosmetics and pharmaceutical industries [21]. Disposal of these dyes into the environment causes serious damage, since they may significantly affect the photosynthetic activity of hydrophytes by reducing light penetration [1]. Moreover, numerous reports indicate that textile dyes and effluents have toxic effects on the germination rates and biomass of several plant species which have important ecological functions, such as providing a habitat for wildlife, protecting soil from erosion and providing the organic matter that is so significant to soil fertility [8]. In addition, azo dyes also have an adverse impact in terms of total organic carbon (TOC), biological oxygen demand (BOD) and chemical oxygen demand (COD) [18]. The presence of unnatural colors is aesthetically unpleasant and tends to be associated with contamination. Without adequate treatment, these dyes will remain in the environment for an extended period of time [15]. Dye house effluent typically contains only 0.6 to 0.8 g L-1 dye, but the pollution it causes is mainly due to durability of the dyes in the wastewater [10]. Moreover, there are many reports on the use of physicochemical methods for the color removal from dye containing effluents [4, 22]. Several methods were adapted for the reduction of azo dyes to achieve decolorization. These include physiochemical methods [5] such as filtration, specific coagulation, use of activated carbon, chemical flocculation etc. Some of these methods (reverse osmosis, nanofiltration and multiple effect evaporators) are found to be effective but quite expensive [14]. Microbial or enzymatic decolorization and degradation is an eco-friendly cost-competitive alternative to chemical decomposition process that could help reduce water consumption compared to physicochemical treatment methods [17]. A number of microorganisms have been found to be able to decolorize textile dyes including bacteria, fungi, and yeasts [15,23][15, ]. Keeping in view the importance of biological treatment over conventional modes of treatment of azo dyes, an attempt has been made to study the decolorization abilities of the newly isolated strain of Bacillus subtillis ETL-1979 for azo dye orange, selected as model azo dye. This article describes optimization of parameters for Orange dye decolorization by B. subtillis ETL-1979. Process optimization by one-factor-at-a-time method involves changing one variable (pH, temperature, dye concentration, inoculum size etc.) while fixing the others at a certain arbitrary levels. The conventional ‘‘one-factor-at-a time’’ approach is laborious and time consuming, especially for large number of variables. Moreover, it seldom guarantees the determination of optimal conditions [3]. These limitations of a single factor optimization process can be overcome by using statistical methods. In statistical based approaches, response surface methodology (RSM) has been extensively used in fermentation media optimization [7, 19]. RSM is a collection of statistical techniques for designing experiments, building models, evaluating the effects of factors and searching for the optimum conditions [11]. It is a statistically designed experimental protocol in which several factors are simultaneously varied. In this work, we have screened out five most effective parameters such as pH, temperature, dye concentration (mg/L), time (h) and inoculum size % (v/v) for decolorization of Orange dye by Bacillus subtillis ETL-1979 using response surface methodlogy (RSM). Placket–Burman design was used to select the factors having significant effect on decolorization of Orange dye by Bacillus subtillis ETL-1979 and optimization of the selected parameters for the decolorization of Orange G was done by central composite design (CCD).

2. Materials & Methods

2.1. Media and Culture Conditions

Bacillus subtillis ETL-1979 isolated from activated sludge sample at Ankleshwar, Gujarat, India by the enrichment technique, is stored at 4°C on nutrient agar slants. The composition of mineral medium used for decolorization studies was (g L-1) KH2PO4 0.68, K2HPO4 1.73, MgSO4.7H2O 0.1, NaCl 0.1, FeSO4.7H2O 0.03, CaCl2.2H2O 0.02, Glucose 5.0, peptone 5.0. The pH was adjusted to 7.0. Inoculum was developed by transferring one loop full of the organism from the slant culture to 50 ml mineral medium in 250 ml Erlenmeyer flask. The flask was incubated in an orbital shaker at 37 ± 1°C and 180 rpm for 24 h for inoculums development.

2.2. Chemicals

The textile dye Orange was obtained from Himedia, India. The dye content was 80%. All the chemicals were of highest purity available and were of analytical grade. Solvents used for ESI-MS analysis were of HPLC grade.

2.3. Acclimatization

The culture was gradually exposed to the increasing concentration of the dye to acclimatize Bacillus subtillis ETL-1979. The successive transfer of culture into fresh mineral medium containing 100, 250, 500, 750, 1000 mg L-1 of the Orange dye was done at 37 ± 1°C in static condition. This acclimatized microorganism was used for all studies.

2.4. Decolorization Studies

The decolorization studies were carried out in 250 ml conical flasks containing 100 ml mineral medium. The medium was inoculated with fully grown culture of Bacillus subtillis ETL-1979. Dye solutions of Orange were filter sterilized as stock solution (1.0% w/v) and added aseptically to the mineral medium to the desired concentration. At first, the effect of five process parameters, including pH, temperature, dye concentration (mg/L), inoculum size/, v/v, (%) and time (h) on decolorization of Orange by Bacillus subtillis ETL-1979 was studied using Placket-Burman design criterion. Decolorization was carried out by adding 5% inoculum of Bacillus subtillis ETL-1979 to 100 ml medium in 250 ml conical flask, amended with 500 mg/L Orange dye and incubated at 37 ± 1°C at static condition for 72 h. The decolorized medium was then centrifuged at 8,000 g at room temperature for 15 min and the cell free supernatant was used for determination of percentage decolorization of Orange dye.

2.5. Analytical Methods

Decolorization of Orange dye was monitored spectrophotometrically at 480 nm, which is an absorbance maximum for Orange dye, on a Shimadzu double beam spectrophotometer (UV 1800, Japan). Uninoculated controls were used to compare color loss during the experiment. The percentage of decolorization was calculated from the difference between initial and final values. The biodecolorization and biodegradation analysis was done using UV-VIS spectrometry, thin layer chromatography (TLC) and Fourier transform infrared spectroscopy (FT-IR). The supernatants obtained after decolorization were extracted with dichloromethane and dried over anhydrous Na2SO4 and evaporated to dryness. The residue obtained was first examined by thin layer chromatography. It is further subjected to FTIR spectroscopy. The software used in spectrophotometer was OMNIC. Analysis was carried out at room temperature in the mid IR region of 400 to 4000 cm -1 at a scan speed of 60. Electron spray ionization mass spectrometry (ESI-MS) analysis was carried out to find out the degradation products. ESI-MS was carried out on an SL 1200 system (Agilent) with ion trap detection in the positive ion mode. The software used was Mass Hunter. The culture broth after decolorization in the medium containing azo dye (Orange) was analyzed using ESI-MS. 50 ml of decolorized samples were centrifuged at 8,000 rpm for 15 min and filtered through 0.45 mm membrane filter. The filtrate was then extracted twice with ethyl acetate and evaporated in a vacuum evaporator at 40 to 45°C and residue was dissolved in 50% acetonitrile in water with 0.1% formic acid and used for ESI-MS analysis.

2.6. Response Surface Methodology

Response surface methodology (RSM) was divided in two stages, first to identify the significant process parameters for decolorization of Orange dye, by Bacillus subtillis ETL-1979 using Placket–Burman design criterion and later the significant parameters resulted from Placket–Burman design were optimized by using a central composite design (CCD). The experimental design and statistical analysis of the data were done by using statistical software Minitab 15.


2.6.1. Placket–Burman Design

Each variable was assigned two levels namely a high level denoted by (+1) and a low level denoted by (-1). The levels of the parameters selected were based on the preliminary experiments and the information available in the literature. pH had a lower limit of 4.0 and an upper limit of 8.0. Temperature was varied between 20 and 40°C. Dye concentration was varied between 550 and 750 mg/L. The lower and upper limits of Inoculum size (%) were 4 and 10%, respectively. Time for decolorization was varied between 16 and 22 h. Five variables were screened by conducting twelve experiments. All experiments were conducted in triplicate and the average value of percentage decolorization of Orange G was used for statistical analysis. The variables, found out to be significant at 5% level (P < 0.05) from the regression analysis were considered to have greater impact on decolorization of Orange dye and were further optimized using central composite design.


2.6.2. Central Composite Design

The optimum values, of five most significant process parameters screened from Placket–Burman design criterion, was find out using central composite design (CCD). The effect of the parameters pH, temperature, dye concentration (mg/l), inoculum size (%) and time (h) was studied at five levels: -a, -1, 0, +1 and +a, where a = 2n/4; here n was the number of variables and 0 corresponded to the central point. The levels of factors used for experimental design are given in Table 1. The actual level of each factor was calculated using the following equation [16].

Table 1. Orange dye decolorization by Bacillus subtillis ETL-1979 using significant factors based on CCD criterion

(1)

Regression analysis of Placket–Burman design criterion data was carried out for prediction of significant factors. The response variable was fitted by a second order model in order to correlate the response variable to the independent variables. The general form of the second degree polynomial equation used in this study is:

(2)

Where, Y is the predicted response; xi and xj are input variables which influence the response variable Y; β0 is the offset term; βi is the ith linear coefficient; βii is the ith quadratic coefficient and βij is the ijth interaction coefficient. Analysis of variance (ANOVA) and regression analysis was done and contour plots were drawn by using Statistical Software, Minitab 15.

3. Results and Discussion

3.1. Screening of Process Parameters Using Placket–Burman Design Criterion

Placket–Burman design was used to find out the process parameters that have a significant effect on decolorization of Orange dye by Bacillus subtillis ETL-1979. Twelve sets of experiments were carried out to study the effect of five parameters on the decolorization of azo dye using Bacillus subtillis ETL-1979, which shows maximum percentage decolorization of Orange dye in the medium having higher level (+1) of pH, temperature, inoculum size, v/v, (%), time (h) and lower level (-1) of dye concentration. Regression analysis of Placket–Burman design criterion data for prediction of significant factors showed that out of five process parameters studied, all of them including pH, temperature, dye concentration, inoculum size, v/v (%) and time (h) showed significant stimulatory effect on Orange dye decolorization as reflected by their P values (< 0.05) obtained. The coefficient of determination (R2) of the model was 0.9538, which indicates the model could explain up to 95.38% variation of the data. Percentage decolorization of Orange dye obtained from Placket–Burman design experiments showed wide variation which indicated towards necessity of further optimization.

3.2. Optimization of Values of Process Parameters by CCD

Thirty two experiments were conducted according to the CCD as shown in Table 1. By applying multiple regression analysis on the application data, the following second order polynomial equation was found to explain the Percentage Orange dye decolorization by Bacillus subtillis ETL-1979.

(3)

Table 2. Regression analysis of CCD criterion data for Orange dye decolorization by Bacillus subtillis ETL-1979

Table 3. Analysis of variance for Orange dye by Bacillus subtillis ETL-1979 Using CCD criterion

Where, Y is the predicted response variable, percentage Orange dye decolorization and X1, X2, X3, X4 and X5 are the values of independent variables, pH, temperature, dye concentration, inoculum size (%) and time (h) respectively. Regression analysis of the experimental data (Table 2) showed that pH, temperature, dye concentration, inoculums size (%) and time (h) had positive effect on percentage Orange dye decolorization as P value of all those factors has a value < 0.05. Analysis of variance for the decolorization of Orange dye obtained from this design is given in Table 3. ANOVA gives the value of the model and can explain whether this model adequately fits the variation observed in Orange dye decolorization with the designed parameters level. The closure the value of R2 (multiple correlation coefficient) to 1, the better the correlation between the observed and predicted values. In the present study, the value of R2 (0.9912) revealed that the model could explain up to 99.12% variation of Orange dye decolorization by Bacillus subtillis ETL-1979. The P value for lack of fit (0.000) indicated that the experimental data obtained fitted well with the model and explained the effect of parameters: pH, temperature, dye concentration (mg/L), inoculum size, v/v, (%) and time (h) on Orange dye decolorization by Bacillus subtillis ETL-1979.

3.3. Interpretation of Parameters Interaction

Figure 1 a to j shows the 2D contours plots of percentage decolorization of Orange dye by Bacillus subtillis ETL-1979 for each pair of process parameters value, keeping the other three parameters constant. The main goal of response surface is to efficiently look out for the optimum values of the variables such that the response is maximized. Contour plots are 2-D plots, which are a useful tool to analyze the interactive effects of factors on the response [9]. The numbers in the contour line inside the contour plots indicate percentage dye decolorization at various decolorization conditions. Figure 1 a, portrays the interactive effect of time and inoculums size on percentage dye decolorization. We can understand from the plot that as the time increases, the dye decolorization increases, while increase in inoculum size up to 5.3% (v/v) increases the percentage dye decolorization and further increase in inoculum size has negative effect on percentage dye decolorization. Figure 1 b depicts the interactive effect of decolorization time and dye concentration on percentage dye decolorization. We can infer from the plot that the percentage dye decolorization increases as the time increases while decrease in dye concentration increases the percentage decolorization. The optimal point for maximum decolorization dye decolorization was found at 485 mg/L dye concentration and fermentation time of 26 h. Further increase in dye concentration, decreases the percentage dye decolorization. Figure 1 c exhibits the interactive effect of inoculums size and dye concentration on percentage dye decolorization. We can deduce from the plot that the increase in inoculum size up to 5.5 % (v/v) increases the dye decolorization, but started decreasing upon further augmentation while the dye decolorization increased as the dye concentration is decreased. Figure 1 d exhibits the interactive effect of decolorization time and temperature on dye decolorization. We can infer from the plot that the rise in temperature above 38°C was detrimental to the dye decolorization, while the dye decolorization increased as the decolorization time increases. Figure 1 e displays the interactive effect of Inoculum size and temperature. We can deduce from the plot that the rise in temperature above 38°C was detrimental to the percentage dye decolorization, while the inoculum size above 5.5% decreases the dye decolorization. Figure 1 f displays the interactive effect of initial dye concentration and temperature on dye decolorization. Dye decolori-zation increased as the temperature was increased from 30 to 38°C, but further increase causes decrease in dye decolorization, whereas decrease in dye concentration favours dye decolorization. Maximum percentage decolorization was obtained at dye conc. of 530 mg/L and temperature of 38°C. Figure 1 g depicts the interaction of decolorization time and pH. Maximum decolorization was obtained at 24 h of decolorization time and pH 6.9. Further increase or decrease in pH and decolorization time causes decrease in the dye decolorization. Figure 1 h depicts the interaction of inoculum size and pH on dye decolorization. Maximum decolorization was obtained at pH 6.9 and at inoculum size 5.5% (v/v). Figure 1 i depicts interaction of Dye concentration and pH on dye decolorization. It was found that maximum percentage dye decolorization was obtained at pH 6.9 and at dye concentration of 524 mg/L. Further increase or decrease in both pH and dye concentration causes decrease in percentage dye decolorization. Figure 1 j depicts interaction of temperature and pH on percentage dye decolorization. It was found out that maximum percentage dye decolorization occurred at temperature 38°C and at pH 6.9. As the temperature and pH values are increased from 30°C and 5.0, respectively; the percentage dye decolorization increases up to 38°C and pH 6.9, respectively. Further increase in temperature and pH values leads to decrease in percentage dye decolorization. The optimal combination of the process parameters for Orange G decolorization as obtained from the surface plots are as follows: p, 6.9; temperature, 37.0°C; dye concentration, 517 mg/L, inoculums size (v/v), 5.5% and time, 23.7 h. At these optimum levels of process parameters, Orange dye decolorization by Bacillus subtillis ETL-1979 of 94.48% was optimized.

Figure 1. a. Contour plot of % decolorization: Effect of time and inoculums size on the % decolorization of Orange dye by B. subtillis ETL-1979, while keeping pH, temperature & dye concentration at constant values
Figure 1. b. Contour plot of % decolorization: Effect of time and dye concentration on the % decolorization of Orange dye by B. subtillis ETL-1979, while keeping pH, temperature & inoculums size at constant values
Figure 1. c. Contour plot of % decolorization: Effect of inoculums size and dye concentration on the % decolorization of Orange dye by B. subtillis ETL-1979, while keeping pH, temperature & time at constant values
Figure 1. d. Contour plot of % decolorization: Effect of time and temperature on the % decolorization of Orange dye by B. subtillis ETL-1979, while keeping pH, dye concentration & inoculums size at constant values
Figure 1. e. Contour plot of % decolorization: Effect of inoculums size and temperature concentration on the % decolorization of Orange dye by B. subtillis ETL-1979, while keeping pH, dye concentration & time at constant values
Figure 1. f. Contour plot of % decolorization: Effect of dye concentration and temperature on the % decolorization of Orange dye by B. subtillis ETL-1979, while keeping pH, inoculums size & time at constant values
Figure 1. g. Contour plot of % decolorization: Effect of time and pH on the % decolorization of Orange dye by B. subtillis ETL-1979, while keeping temperature, dye concentration & inoculums size at constant values
Figure 1. h. Contour plot of % decolorization: Effect of inoculums size and pH concentration on the % decolorization of Orange dye by B. subtillis ETL-1979, while keeping temperature, dye concentration & time at constant values
Figure 1. i. Contour plot of % decolorization: Effect dye concentration and pH concentration on the % decolorization of Orange dye by B. subtillis ETL-1979, while keeping temperature, inoculums size & time at constant values
Figure 1. j. Contour plot of % decolorization: Effect of inoculums temperature and pH concentration on the % decolorization of Orange dye by B. subtillis ETL-1979, while keeping dye concentration, inoculums size & time at constant values
3.4. Validation of the Optimized Decolorization Parameters

Experiments were done in triplicate using the optimized condition to verify the modeling results. It was found that for dye concentration of 520 mg/L, pH 6.9 and temperature 37°C, percentage decolorization of 95% was obtained in 24 h. Majority of azo dye reducing bacteria reported [2, 20] so far were able to reduce the dye at near neutral pH, showing similarity with the present report.

Figure 2. (a) UV-Visible spectrum of Orange dye by Bacillus subtillis ETL-1979. Lowering of peak clearly indicates decolorization due to removal of Orange dye. (b) TLC experiment showing different Rf values of Orange dye solution & decolorized sample, indicating its degradation to intermediate products. All the experiments are performed in static condition with the optimized process parameter values from RSM
Figure 3. FT-IR spectra of (a) Orange dye, control dye & (b) degraded sample after treatment with Bacillus subtillis ETL-1979 with the optimized process parameter values from RSM
Figure 4. ESI-MS spectra of degradation of Orange dye by Bacillus subtillis ETL-1979 revealing the degradation mechanism
3.5. Identification of Metabolic Intermediates

Bacillus subtillis ETL-1979 successfully resulted in the decolorization of the dye, Orange dye. The decolorization was confirmed by UV-VIS spectrum. The UV-VIS spectrum, as shown in Figure 2 a, corresponds to initial and final samples of decolorization experiments. The absorbance values were analyzed from 300 to 800 nm. The initial dye solution, before decolorization showed high peak at the wavelength of 480 nm. The decolorized sample showed lowering of peak to a smaller absorbance value for dye concentration of 500 mg/L, which informs that the decolorization is due to removal or degradation of dye (Figure 2 a). The degradation of azo dye, Orange dye, by Bacillus subtillis ETL-1979 was further supported by thin layer chromatography (TLC). The spots observed with the initial dye solution varied a lot from the spot observed with the supernatant obtained after decolorization (Figure 2 b). The original dye was quite different from the supernatant obtained after dye decolorization, which was suggested by different values of retention factors obtained in the TLC experiment. This difference confirms that decolorization was due to breakdown of dyes into unknown intermediate products. The most important step in bacterial degradation of dye is reductive cleavage of N = N (azo) bond leading to formation of colorless aromatic amines. These amines are converted to simpler forms, subjected to oxidation [13]. FT-IR analysis was done to characterize the breakdown products generated. The FT-IR analysis of the dye Orange dye and sample obtained after decolorization showed many peaks (Figure 3). The FT-IR spectra of Orange dye control dye display peaks at 3535.9, 1634, 1494, 1198.2 and 1035.4. The peak characteristic of azo bond at 1494 of Orange dye was not found in the decolorized sample, indicating degradation of Orange dye to aromatic amines as metabolites. Peaks at 3341 and 3161 cm-1 shows the presence of amine, whereas 3482 cm-1 indicates phenolic group. Degradation of aromatic amines obtained after break down of Orange dye, leads to formation of aldehyde as an intermediate, which was confirmed by the spot test using 2, 4-dinitro phenyl hydrazine reagent which indicates color test due to presence of aldehyde. Similar results were obtained by Kolekar et al. (2008). ESI-MS analysis of the supernatant obtained after decolorization confirms degradation to intermediate compounds (Figure 4). During the degradation, there is asymmetric cleavage of azo bonds in Orange dye resulting in formation of Phenyl hydrazine, while the naphthalene part of the dye was degraded giving rise to smaller compounds. Further biodegradation of naphthalene part with opening of ring, giving rise to the formation of aldehyde as one of the intermediate is confirmed from the IR data. Thus, it is clear from the analytical methods used that the azo dye, Orange dye, is degraded to intermediate compounds as a result of cleavage of azo bond (N = N). The color produced by Orange dye was only due to the presence of azo (N = N) group. The intermediates, obtained, phenyl hydrazine, naphthalene derivatives and aldehyde are devoid of any chromophores like azo group (N = N) and hence are colorless and thus are responsible for the decolorization caused by Bacillus subtillis ETL-1979.

4. Conclusions

The present study confirms the ability of newly isolated bacterial culture Bacillus subtillis ETL-1979 to decolorize the textile dye Orange dye with decolorization efficiency of 95%, thus suggesting its application for decolorization of dye bearing industrial wastewaters. The anaerobic decolorization of Orange dye occurs as a result of reduction of N = N- bond accompanied by the formation of aromatic amines. The ability of the novel strain needs to be tested in continuous reactor containing real dye bearing wastewater. There is no information available in open literature concerning optimization of process parameters by applying statistical software for decolorization of Orange dye by Bacillus subtillis ETL-1979. Validity of the response model was verified by agreement of the predicted and experimental values as by keeping pH, 6.9 and temperature, 37.0°C, decolorization of 95% was obtained for Orange dye concentration of 520 mg/L in 24 h. The results of this study could be used to design a suitable process to get higher percentage decolorization of Orange dye.

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