An enhancement of red pigment production by submerged culture of Monascus purpureus MTCC 410 employing statistical methodology

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An enhancement of red pigment production by submerged culture of Monascus purpureus MTCC 410 employing statistical methodology Vimal S. Prajapati, Nidhi Soni, Ujjval B. Trivedi, Kamlesh C. Patel

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S1878-8181(13)00100-X http://dx.doi.org/10.1016/j.bcab.2013.08.008 BCAB122

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Biocatalysis and Agricultural Biotechnology

Received date: 1 August 2013 Revised date: 25 August 2013 Accepted date: 26 August 2013 Cite this article as: Vimal S. Prajapati, Nidhi Soni, Ujjval B. Trivedi, Kamlesh C. Patel, An enhancement of red pigment production by submerged culture of Monascus purpureus MTCC 410 employing statistical methodology, Biocatalysis and Agricultural Biotechnology, http://dx.doi.org/10.1016/j.bcab.2013.08.008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

An enhancement of red pigment production by submerged culture of Monascus purpureus MTCC 410 employing statistical methodology

Vimal S. Prajapati, Nidhi Soni, Ujjval B. Trivedi, Kamlesh C. Patel* B R D School of Biosciences, Sardar Patel University Sardar Patel Maidan, Vadtal Road, Vallabh Vidyanagar-388 120, Gujarat, India Phone: +91-2692-231041; Fax: +91-2692-231042; E-mail: [email protected]

*Corresponding author. Phone: +91-2692-231041; Fax: +91-2692-231042 E-mail: [email protected] 1

Abstract Pigments produced by Monascus spp. can be used as food grade biocolorant and are preferred over the synthetic variants which elicit various adverse effects. Monascus purpureus MTCC 410 has been investigated in the present study for red pigment production employing submerged fermentation. The medium components influencing the pigment production were identified using Plackett-Burman design. Among various variables screened, glucose, tryptone and pH were found to be highly significant. The optimum concentrations of these significant parameters were determined employing the response surface central composite design. Glucose (28 g/L), tryptone (1 g/L) and pH 8.0 showed highest pigment production.

Keywords: Red pigment; Plackett-Burman design; Central composite design; Monascus purpureus; Medium optimization.

2

1. Introduction The scrutiny and negative assessment of synthetic food dyes by the modern consumer has resulted into a strong interest in natural coloring alternatives. Monascus pigments produced by various species of Monascus have been used as natural colorants and as traditional food additives in East Asia [1]. Monascus spp. produces a complex mixture of three categories of pigments such as orange, red and yellow. Among these pigments, the red pigment (monascorubramine and rubropunctamine) is having high market potential for its use in meat products [2]. Monascus metabolites have been evaluated for their various biological activities such as embryotoxicity, teratogenicity, immunosuppressive properties, antioxidant properties, antibiotic and cytotoxic activity [3]. Monascus pigment fermentations have been performed mainly in solid cultures; however production yield is too low to compensate its economical viability and hence recent research efforts have focused on submerged fermentation to increase the pigment yield [4]. Moreover, solid state fermentation is labour-intensive, time consuming and requires large cultivation areas, and hence submerged culture technique for Monascus pigments has been studied to overcome the problems of space, scale up and process control of solid culture [5]. Studies on red pigment synthesis by various strains of Monascus purpureus in submerged culture have shown that the yield is affected by medium composition, pH and agitation [6]. Composition of the pigments synthesized varies significantly depending on the types of nutrients available, such as nitrogen sources and the strain used [7]. Majority studies on pigment production have been carried out using conventional onefactor-at-a-time method which frequently fails to locate optimal parameters and is also unable to show possible interaction effects between the selected parameters [8]. Silveira et al., [9] have

3

reported pigment production by M. purpureus in grape waste using factorial design. Factorial design and response surface techniques are important tools to determine the optimal process conditions. This methodology has been successfully used in many areas of biotechnology, particularly to optimize the production of bioactive molecules [10, 11]. Conventional approaches for increased microbial metabolite production usually employ manipulation of nutritional requirements, physical parameters and genetic makeup of the producing strain. Development of economical medium requires selection of carbon, nitrogen, phosphorous, potassium and trace element sources. Nutritional requirement can be manipulated by the conventional or statistical methods. Conventional method involves changing one independent variable at a time while keeping the others at fixed level. However, statistical method offers several advantages over conventional method being rapid and reliable, short lists significant nutrients, helps understanding the interactions among the nutrients at various concentrations and reduces the total number of experiments tremendously resulting in saving time, glassware, chemicals and manpower [12]. Initial screening of the ingredients is done using Plackett-Burman design to understand the significance of their effect on the product formation and then a few better ingredients are selected for further level optimization employing response surface methodology. In the present study statistical approach has been employed in which a Plackett-Burman design is used for identifying significant variables influencing red pigment production by Monascus purpureus MTCC 410 under submerged fermentation. The levels of the significant variables such as glucose, tryptone and initial pH of the medium, have been further optimized using response surface central composite design to determine the optimum conditions for red pigment production.

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2. Materials and methods 2.1. Microorganism and maintenance The stock culture of Monascus purpureus (MTCC 410) procured from Microbial Type Culture Collection and Gene Bank, Institute of Microbial Technology, Chandigarh, Punjab, India was maintained on YM (Yeast and Mold medium) agar slants containing (g/L): glucose 20, malt extract 3, peptone 5, yeast extract 3 and agar 1.5. The culture was maintained at 4ºC in the laboratory. 2.2. Inoculum preparation and pigment production The plate containing 7 days old growth of the organism was used as inoculum. Two plugs of 8 mm size were taken from this plate and inoculated in 250 ml Erlenmeyer flask containing 100 ml YM broth. The flasks were incubated at 30ºC and 120 rpm on a rotary shaker for red pigment production. 2.3. Pigment estimation After incubation, the mycelia were separated from broth by filtration using Whatman #1 filter paper. Filtrate was subjected for centrifugation at 8000g for 20 min. The supernatant was collected and the pigment production was analyzed by measuring the absorbance of the supernatant using UV-Visible spectrophotometer (Shimadzu) having un-inoculated medium as blank and considering the dilution factor of the sample [13, 14]. Pigment yield was expressed as specific absorbance (Amax) at 500 nm per ml of filtrate (OD U/ml). 2.4. Optimization of process parameters 2.4.1. Identifying the significant variables using Plackett-Burman design

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The present study was aimed at screening of the important medium components with respect to their main effects by Plackett-Burman design. This experimental design is a two factorial design and was used to identify the critical parameters required for optimum red pigment production by screening n variables in n + 1 experiments [15]. The eight variables selected on the basis of our previous study for the present investigation were, amount of malt extract, sucrose, lactose, glucose, tryptone, yeast extract, peptone and salt solution (g/L: K2HPO4 1, MgSO4 0.5, KCl 0.5 and FeSO4 0.01), as well as pH under submerged fermentation (Table 1). The experimental design for the screening of the variables is presented in Table 2. The PlakettBurman design assumes that there are no interactions between different medium components. All the variables were denoted as numerical factors and investigated at two widely spaced intervals designated as -1 (low level) and +1 (high level). The effects of individual parameters on red pigment production were calculated by the following equation: E (Xi) = 2 (M + - M -) / N

(1)

Where E is the effect of parameter under study and M+ and M- are responses (red pigment production) of trials at which the parameter was at its higher and lower levels, respectively and N is the total number of trials. Experimental error was estimated by calculating the variance among the dummy variables as Veff =  (Ed) 2/n (2) Where Veff is the variance of the effect of level, Ed is the effect of level for the dummy variables and n is the number of dummy variables used in the experiment. The standard error (SE, Es) of concentration effect was the square root of variance of an effect, and the significance level (Pvalue) of each concentration effect was determined using the student’s t-test: t (Xi ) = E(Xi ) / Es

(3)

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Where E (Xi) is the effect of variable Xi.

2.4.2. Response surface methodology (RSM) The levels of the significant parameters as revealed from Plackett-Burman experiment and their interaction effects which may influence the red pigment production significantly were analyzed and optimized by response surface central composite design (CCD). RSM is useful for small number of variables (up to five) but is impractical for large number of variables, due to high number of experimental runs required. The level of the three major components glucose, tryptone and pH were optimized, keeping temperature and inoculum size constant. According to the design, the total number of treatment combinations is 2k + 2k + no, where k is the number of independent variables and no is the number of repetition of experiments at the central point. Each factor in the design was studied at five different levels (-, -1, 0, +1, +) as shown in Table 3. This experimental design comprises a two level fractional factorial points (-1 and +1), central point (0) and axial or star points encoded as – and +. All variables were set at a central coded value of zero. The minimum and maximum ranges of variables were determined on the basis of our previous experiments. The full experimental plan with respect to their values in actual and coded form is listed in Table 4. Red pigment production (OD U/ml) was measured in triplicate in 20 different experimental runs. The red pigment production was analyzed using a second order polynomial equation and the data were fitted into the equation by multiple regression procedure. The model equation for analysis is given as: Y = 0 +  iXi +  iiXi2 + ijXiXj

(4)

where o, i, ii and ij represent the constant, linear, quadratic effect of Xi and interaction effect between Xi and Xj, respectively for the production of red pigment. Later, validation experiment

7

was performed and maximum production of red pigment was confirmed using the optimum values for variables predicted by response optimization. 2.5. Software and data analysis The results of the experimental design were analyzed and interpreted using Design Expert Version 8.0 (Stat-Ease Inc., Minneapolis, Minnesota, USA) statistical software.

3. Results and discussion 3.1. Screening of significant parameters for red pigment production using Plackett-Burman design The variables selected in the present investigation are on the basis of our previous study in which different carbon and different organic-inorganic nitrogen sources were evaluated for their effects on the red pigment production using one factor-at-a-time approach and shortlisted for the further (Plackett-Burman) experiment. We observed that inorganic nitrogen sources have no effect on the red pigment production while organic nitrogen sources showed prominent effect. Many investigators have reported addition of monosodium glutamate giving maximum red pigment production [16, 17] but in our study we observed that presence of tryptone as compared to monosodium glutamate showed more red pigment production. Hence, tryptone was selected as organic nitrogen source for further optimization studies. The pigments produced by Monascus sp. are soluble only in the organic solvent but the addition of the monosodium glutamate, soyabean meal, and peptone or chitin powder in the fermentation medium lead to the production of water soluble pigment. The addition of amino acid in the fermentation medium lead to the production of more red pigment than the yellow pigment [18, 19]. A comparison of the relative amounts and color quality of the pigments

8

produced using several amino acid show that yellow and orange pigment are unaffected by the nitrogen source, whereas red pigment differ in tone and solubility [20]. In addition to that supplementation of organic nitrogen results in higher growth and hence gives more red pigment production compared to the inorganic nitrogen source. A statistical approach has been used to screen the most effective medium components and select their concentration to achieve highest possible red pigment production by M. purpureus MTCC 410 under submerged fermentation. Usually, for the fermentation processes, initial screening of the ingredients is carried out to understand the significance of their effect on the product formation. Subsequently better ingredients are selected for further optimization. Plackett-Burman design was used to screen eight different medium components including carbon and nitrogen sources along with standard salt solution and a physical process parameter (pH) in a 12 run experiment with 2 level concentration of each variable. Studies were carried out under submerged fermentation at 120 rpm, 30°C for 8 days. The independent variables and their respective high and low concentrations used in optimization study are represented in Table 1, whereas the Plackett-Burman experimental design for 12 trials with two level concentrations of each variable followed for the screening of medium components for red pigment production is given in Table 2. The variable X1-X9 represents the medium constituents and D1-D2 represents the dummy / unassigned variables. Results of the Plackett-Burman experiment with respect to red pigment production, the effect, standard error, t(xi), p and confidence level of each component are represented in Table 2 and 5. The components were screened at the confidence level of 95% on the basis of their effects. When components show significance at or above 95% confidence level and its effect is negative, it is considered effective for production but the amount required may be lower than the indicated (as low, -1) concentration in Plackett-Burman

9

experiment. If the effect is found positive, the amount required may be higher than indicated (as high, +1) concentration. In our experiment, glucose and tryptone gave confidence level >95% and could be considered significant. Remaining components, i.e, malt extract, yeast extract, peptone, sucrose, lactose and salt solution showed confidence level <95% and were considered insignificant in the study. pH also showed <95% significance but our initial studies on effect of pH on the red pigment production, and many research reports have demonstrated its importance in red pigment production and hence we have selected this component for further level optimization. As reported, media pH plays an important role in activating key enzymes involved in pigment production and excretion by M. pupureus CCT3802 [21]. Besides pigment excretion by the cultures, alkaline pH promotes a higher stability of this colorant relative to acidic values [2, 22]. From the present study using Plackett-Burman design, glucose, tryptone and pH were short listed and studied further for optimization of their concentration requirement and to check their interaction effect. Methodology of Plackett-Burman was thus found to be very useful for determination of relevant variables for further optimization. Ahmad et al., [23] used the PlackettBurman design for screening the nutrient parameter for red pigment production under submerged fermentation and also reported that the dextrose is one of the best carbon source for the maximum pigment production. Plackett-Burman experiment used only to screen the significant components but did not give the optimum level of the significant components. In the present study, significant components were screened using Plackett-Burman design followed by their level optimization employing response surface central composite design.

3.2. Response Surface Methodology (RSM)

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The central composite design was employed to evaluate the interaction among the significant factors and also to determine their optimal levels. In the present work, experiments were planned to obtain a quadratic model consisting of 23 trials. The plan included twenty experiments and two levels of concentration for each factor. In order to study the combined effect of these variables, experiments were performed at different combinations. The central composite experimental plan along with the predicted and observed response for each individual experiment is summarized in Table 4. It also shows the production of red pigment (OD U/ml) corresponding to combined effect of all three components in the specified ranges. The optimum levels of selected variables were obtained by solving the regression equation and by analyzing the response surface contour and surface plots. The larger the magnitude of the t-value and smaller the p-value, more significant is the corresponding coefficient [24]. The regression equation obtained after the analysis of variance (ANOVA) provides an estimate of the level of red pigment production as a function of pH, glucose and tryptone concentration. The production of red pigment may be best predicted by the following model: Y = 0.96 + (0.093*A) – (0.11*B) + (0.20*C) – (0.30*A*B) + (0.20*A*C) - (0.20*B*C) (0.25*A2) - (0.24*B2) - (0.19*C2);

(5)

Where Y = Red pigment production (OD U/ml), A = Glucose (g %), B = Tryptone (g %) and C = pH. The statistical significance of the second order model equation was evaluated by F- test analysis of variance as shown in Table 6 which revealed that this regression is statistically highly significant for red pigment production. The model F-value 6.18 implies that the model is significant. There is only a 0.44% chance that a large model F-Value could occur due to noise. P-

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value less than 0.050 indicate that the model terms are significant. Thus, in this study C, AB, A2, B2 and C2 are significant model terms (Table 6). The Lack of fit F- Value of 4.82 implies the lack of fit is not significant relative to the pure error. Non-significant lack of fit is good for the model to fit. The R2 value (multiple correlation coefficient) closer to 1 denotes better correlation between observed and predicted values. The coefficient of variation (CV) indicates the degree of precision with which the experiment is compared. The lower reliability of the experiment is usually indicated by high value of CV. In the present case low CV (53.43) denotes that the experiment performed is reliable. Adequate precision measures the signal to noise ratio. A ratio greater than 4 is desirable. In present study, the ratio is 8.417 which indicate an adequate signal. The effect of interaction of variables on red pigment yield was studied by changing levels of any two independent variables while keeping the third independent variable at its constant level. The response surface plots or contour plots can be used to predict the optimal values for different test variables. Therefore, three response surface plots were obtained by considering all the possible combinations. Fig. 1 shows the effect of interaction between glucose and tryptone on red pigment production, which revealed that both the components at their lower level have no significant effect on the red pigment production but increase in glucose concentration leads to gradual increase in the pigment production while increase in tryptone concentration leads to decrease in the pigment production. Glucose and tryptone concentration at higher level showed negative effect on pigment production but glucose at higher level and tryptone at lower level showed positive effect on the red pigment production. As shown in Fig. 2 higher glucose concentration and alkaline pH (above 7) lead to maximum pigment production while higher glucose concentration and acidic pH showed negative effect on red pigment production. Thus, both the parameters at their maximum level were found to be significant for red pigment

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production. The interaction between tryptone and pH also play an important role in red pigment production; higher pH (alkaline pH) at lower level of tryptone was found to be significant for red pigment production. It was noticed that tryptone concentration up to certain level supports red pigment production but at higher concentration it negatively affects the red pigment production (Fig. 3). According to the response surface point prediction analysis, glucose concentration of 28 g/L, tryptone (1g/L) with 8.5 pH of medium could give maximum red pigment yield up to 12.47 OD U/ml.

3.3. Validation of the quadratic model In order to confirm the above mentioned optimized medium constitution and condition an experiment for pigment production was performed in duplicate. Under these suggested condition the mean value of the pigment yield was found to be 14.50 O.D. U/ml, which was higher than the predicted value of 12.47 OD/U ml. Thus, the model developed was accurate and reliable for predicting the production of red pigment by M. purpureus MTCC 410.

4. Conclusions To the best of our knowledge, majority of the experiment on the red pigment production by M. purpureus have been carried out using one factor at a time methodology under solid state fermentation. Only few reports are available on the statistical optimization of red pigment production using submerged culture of M. purpureus. Present study has allowed rapid screening of a number of nutrients influencing red pigment production. Interestingly tryptone as nitrogen source has been found to support good pigment production where majority of the investigators have reported monosodium glutamate as effective for the same. The pigment yield and the

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production were found to be significantly influenced by initial pH of medium, glucose and tryptone concentration. The data obtained after optimization has resulted in 14.50 OD U/ml red pigment production. References [1] Carels, M., Shepherd, D., 1977. The effect of different nitrogen sources on pigment production and sporulation of Monascus sp. in submerged shaken culture. Can.. J. Microbiol. 23, 1360-1372.

[2] Fabre, C. E., Santerre, A. L., Loret, M. D., Baberian, R., Parailleux, A., Goma, G., et al., 1993. Production and food application of the red pigments of Monascus ruber. J. Food Sci. 58, 1099-1102.

[3] Loret, M. O., Morel, S., 2010. Isolation and structural characterization of two new metabolites from Monascus. J. Agric. Food Chem. 58, 1800-1803.

[4] Mukherjee, G., Singh, S. K., 2011. Purification and characterization of a new red pigment from Monascus purpureus in submerged fermentation. Process Biochem. 46, 188-192.

[5] Lin, C. F., 1973. Isolation and cultural conditions of Monascus sp. for production of pigment in a submerged culture. J. Ferment. Technol. 51, 407-414.

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[6] Hamdi, M., Blanc, P. J., Goma, G., 1996. Effect of aeration conditions on the production of red pigments by Monascus purpureus growth on prickly pear juice. Process Biochem. 31, 543547.

[7] Miyake, T., Isato, K., Nobuyuki, N., Sammoto, H., 2008. Analysis of pigment composition in various Monascus cultures. Food Sci. Technol. Res. 14, 194-197.

[8] Kalil, S. J., Maugeri, F., Rodrigues, M. I., 2000. Response surface analysis and simulation as a tool for bioprocess design and optimization. Process Biochem. 35, 539-550.

[9] Silveira, S. T., Daroit, D. J., Brandelli, A., 2008. Pigment production by Monascus purpureus in grape waste using factorial design. LWT – Food Sci. Technol. 41, 170-174.

[10] Cladera-Olivera, F., Caron, G. R., Brandelli, A., 2004. Bacteriocin production by Bacillus licheniformis strain P40 in cheese whey using response surface methodology. Biochem. Eng. J. 21, 53-58.

[11] Thys, R. C. S., Guzzon, S. O., Cladera-Olivera, F., Brandelli, A., 2006. Optimization of protease production by Microbacterium sp. in feather meal using response surface methodology. Process Biochem. 41, 67-73.

[12] Srinivas, M. R. S., Naginchand, Lonsane, B. K., 1994. Use of Plackett–Burman design for rapid screening of several nitrogen sources, growth/product promoters, minerals and enzyme

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inducers for the production of alpha-galactosidase by Aspergillus niger MRSS 234 in solid state fermentation. Bioprocess Eng. 10, 139–144.

[13] Chiu, S. W., Poon, K., 1993. Submerged production of Monascus pigments. Mycologia. 85, 214-218. [14] Carvalho, J. C., Pandey, A., Babitha, S., Soccol, C. R., 2003. Production of Monascus biopigmentation: An overview. Agro Food Ind. Hi-Tech. 14, 619-624.

[15] Plackett, R. L., Burman, J. P., 1946. The design of optimum multifactorial experiments. Biometrika. 33, 305-325.

[16] Pastrana, L., Blanc, P. J., Santterre, A. L., Lorret, M. O., Goma, G., 1994. Production of red pigments by Monascus rubber in synthetic media with strictly controlled nitrogen source. Process Biochem. 30, 1200-1203.

[17] Babitha, S., Soccol, C.R., Pandey, A., 2006. Jackfruit seed- a novel substrate for the production of Monascus pigments through solid state fermentation. Food Technol. Biotechnol. 44, 465-471.

[18] Yongsmith, B., Tabloka, W., Yongmanitchai, W., Bavavoda, R., 1993. Culture conditions for yellow pigments formation by Monascus sp. KB10 grown on cassava medium. World J. Microbiol. Biotechmnol. 9, 85-90.

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[19] Juzlova, P., Martinkova, L., Kren, V., 1996. Secondary metabolites of the fungus Monascus: A review. J. Ind. Microbiol. 16, 163-170.

[20] Jung, H., Kim, C., Kim, K., Shin, C. S., 2003. Color characteristic of Monascus pigments derived by fermentation with various amino acids. J. Agric. Food Chem. 51, 1302-1306. [21] Orozco, S. F. B., Kilikian, B.V., 2008. Effect of pH on citrinin and red pigment production by Monascus purpureus CCT3802. World J. Microbiol. Biotechnol. 24, 263-268.

[22] Carvalho, C., Oishi, B. O., Pandey, A., Soccol, C. R., 2005. Biopigments from Monascus: strain selection, citrinin production and color stability. Braz. Arch. Biol. Technol. 48, 885-894.

[23] Ahmad, M., Nomani, S., Panda, B., 2009. Screening of nutrient parameters for red pigment production by Monascus purpureus MTCC 369 under submerged fermentation using PlackettBurman design. Chiang Mai J. Sci. 36, 104-109.

[24] Myers, R. H., Montgomery, R. C., 2002. Response surface methodology: Process and product optimization using design experiments. New York: Wiley.

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Figure captions Fig. 1 Response surface graph showing the interaction effect of glucose and tryptone on red pigment production Fig. 2 Response surface graph showing the interaction effect of glucose and pH on red pigment production Fig. 3 Response surface graph showing the interaction effect of tryptone and pH on red pigment production

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Table 1 Medium components as variable and their values used in Plackett-Burman design for red pigment production using M. purpureus MTCC 410.

Variable

Medium component

+ values

- values

X1

Malt extract

0.5 g

0.05 g

X2

Sucrose

1.0 g

0.1 g

X3

Lactose

1.0 g

0.1 g

X4

Glucose

1.0 g

0.1 g

X5

Tryptone

0.5 g

0.05 g

X6

Yeast extract

0.5 g

0.05 g

X7

Peptone

0.5 g

0.05 g

X8

Salt solution

10 ml

1ml

X9

pH

8

3

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Table 2 Plackett-Burman design generated by fractional rotation of full factorial design where X1 to X9 are independent variables and D1-D2 are dummy variables.

X1

X2

X3

X4

w/ v

w/ v

w/ v

w/ v

1

1

1

-1

1

2

-1

1

1

3

1

-1

4

-1

5

Run

Components X5 X6 X7 w/

X8

X9

D1

D2

Red pigment production (OD U/ml)

w/ v

w/ v

v/ v

1

1

-1

-1

-1

1

-1

10.99±0.403

-1

1

1

1

-1

-1

-1

1

08.42±0.480

1

1

-1

1

1

1

-1

-1

-1

06.47±0.445

1

-1

1

1

-1

1

1

1

-1

-1

12.31±0.770

-1

-1

1

-1

1

1

-1

1

1

1

-1

06.96±0.381

6

-1

-1

-1

1

-1

1

1

-1

1

1

1

05.88±0.296

7

1

-1

-1

-1

1

-1

1

1

-1

1

1

06.59±0.360

8

1

1

-1

-1

-1

1

-1

1

1

-1

1

05.26±0.664

9

1

1

1

-1

-1

-1

1

-1

1

1

-1

04.51±0.417

10

-1

1

1

1

-1

-1

-1

1

-1

1

1

05.10±0.565

11 12

1 -1

-1 -1

1 -1

1 -1

1 -1

-1 -1

-1 -1

-1 -1

1 -1

-1 -1

1 -1

10.52±1.753

v

01.43±0.169

20

Table 3 Experimental range and levels of the independent variables of selected components used for response surface central composite design.

Variable Components

Range

Levels of variable studied -

-1

0

+1

+

X1

Glucose

0.1-3.0 (g%)

-0.89

0.1

1.55

3

3.99

X2

Tryptone

0.1-1.5 (g%)

-0.88

0.1

0.88

1.5

1.98

X3

pH

3.0-10

0.61

3

6.5

10

12.39

21

Table 4 Full experimental central composite design with actual and coded level of variables and the response function.

Run

A: Glucose

B: Tryptone

No.

(g %)

(g %)

Red pigment production

C: pH

(OD U/ml)

Actual

Coded

Actual

Coded

Actual

Coded

Observed

Predicted

1

0.10

-1

0.10

-1

3.00

-1

0.6±0.035

1.94

2

3.00

+1

0.10

-1

3.00

-1

0.68±0.028

1.92

3

0.10

-1

1.50

+1

3.00

-1

3.05±0.049

5.80

4

3.00

+1

1.50

+1

3.00

-1

0.52±0.049

2.22

5

0.10

-1

0.10

-1

10.00

+1

0.58±0.028

2.00

6

3.00

+1

0.10

-1

10.00

+1

17.83±1.768

13.75

7

0.10

-1

1.50

+1

10.00

+1

4.5±0.282

1.93

8

3.00

+1

1.50

+1

10.00

+1

0.54±0.0141

1.77

9

-0.89

-

0.80

0

6.50

0

1.0±0.212

0.91

10

3.99

+

0.80

0

6.50

0

2.09±0.070

4.03

11

1.55

0

-0.38

-

6.50

0

3.0±0.071

4.71

12

1.55

0

1.98

+

6.50

0

1.0±0.084

1.15

13

1.55

0

0.80

0

0.61

-

0.91±0.212

1.04

14

1.55

0

0.80

0

12.39

+

6.0±0.210

7.73

15

1.55

0

0.80

0

6.50

0

9.8±0.141

9.64

16

1.55

0

0.80

0

6.50

0

9.2±0.141

9.64

17

1.55

0

0.80

0

6.50

0

8.2±0.070

9.64

18

1.55

0

0.80

0

6.50

0

12.0±0.141

9.64

19

1.55

0

0.80

0

6.50

0

8.0±0.070

9.64

20

1.55

0

0.80

0

6.50

0

11.0±0.070

9.64

22

Table 5 Statistical analysis of components for red pigment production by M. purpureus MTCC 410.

Components

Effect

Standard error

t- value

P

Confidence (%)

Malt extract

0.07

0.052

1.413

0.293

70.68

Sucrose

0.145

0.052

2.913

0.100

89.96

Lactose

-0.008

0.052

-0.16

0.887

11.24

Glucose

0.301

0.052

6.033

0.026

97.36

Tryptone

0.452

0.052

9.046

0.012

98.80

Yeast extract

0.058

0.052

1.173

0.361

63.85

Peptone

0.065

0.052

1.306

0.321

67.86

Salt Solution

0.015

0.052

0.313

0.783

21.63

pH

0.107

0.052

2.146

0.164

83.50

23

Table 6 Analysis of variance (ANOVA) for the fitted quadratic polynomical model for level optimization of red pigment production using M. purpureus MTCC 410.

Source

Sum of Squares

Df

Mean

F-

p-value

Square

Value

Prob > F

Model

4.011796

9

0.445755

6.181912

0.0044

Significant

A-Glucose

0.117603

1

0.117603

1.630968

0.2304

B-Tryptone

0.152756

1

0.152756

2.118488

0.1762

C-pH

0.540156

1

0.540156

7.491104

0.0209

Significant

AB

0.709241

1

0.709241

9.836035

0.0106

Significant

AC

0.309685

1

0.309685

4.29483

0.0650

BC

0.306545

1

0.306545

4.251284

0.0662

2

A

0.925971

1

0.925971

12.84174

0.0050

Significant

B2

0.812171

1

0.812171

11.26352

0.0073

Significant

C2

0.498323

1

0.498323

6.910948

0.0252

Significant

Residual

0.721063

10

0.072106

Lack of Fit

0.597263

5

0.119453

4.824422

0.0546

Pure Error

0.1238

5

0.02476

Cor Total

4.732859

19

not significant

R2 = 0.84; Adeq Pre = 8.41

24

Highlights ¾ Enhanced red pigment production using statistical methodology under Smf. ¾ Study reports tryptone as better nitrogen source for red pigment production. ¾ Alkaline pH has been found more effective for red pigment production.

25

Figure 1

Figure 2

Figure 3