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Modeling and optimization of the process conditions for biomass production and sporulation of a probiotic culture
Process Biochemistry 40 (2005) 2531–2538 www.elsevier.com/locate/procbio
Modeling and optimization of the process conditions for biomass production and sporulation of a probiotic culture Ramkrishna Sena,*, K. Srinivasa Babub a
Centre for Biotechnology, Biological Sciences Group, Birla Institute of Technology and Science (BITS), Pilani, Rajasthan 333031, India b Biotechnology Department, R&D Centre, Cadila Pharmaceuticals Ltd., Ahmedabad 387810, India Received 3 April 2004; received in revised form 21 September 2004; accepted 22 November 2004
Abstract The present study is aimed at evaluating and analyzing the effects of the critical process parameters by using response surface modeling method and optimizing the same to enhance the yields of biomass and endospores of a probiotic culture. A 24 full factorial central composite design followed by a multistage Monte Carlo optimization was employed for experimental design and analysis of the results and process optimization. The optimal process conditions for maximum biomass production were: pH = 6.65; temperature = 38.3 8C; agitation = 247 rpm and aeration = 1.05 vvm and those for the maximum sporulation were: pH = 6.27; temperature = 41.4 8C; agitation = 115 rpm and aeration = 0.33 vvm. Hence, a two-stage strategy with biomass production in exponential phase under the optimal growth conditions in the first stage followed by the second stage in stationary phase under the optimal conditions for sporulation was thus adopted to obtain a maximum probiotic biomass yield of 4.3 g l1 and spore yield of 9 1011 spores g1 of dry biomass for the formulation of effective nutraceuticals. # 2004 Elsevier Ltd. All rights reserved. Keywords: Probiotic; Response surface modeling; Statistical analysis; Process optimization
1. Introduction The gastro-intestinal tract of humans and animals is colonized by a very complex and balanced micro biota. These microorganisms normally prevent infection and have a positive effect on nutrition. Any abrupt change in diet, stress, or antibiotic therapy can upset this microbial balance, making the host susceptible to disease and decreasing the efficiency of food use [1,2]. The term ‘‘probiotic’’ was originally referred to the growth promoting substances associated with a phenomenon, probiosis, observed when two organisms were cultured together (co-cultured), in which metabolites produced by one organism stimulated the growth of the other organism. The term was subsequently used to describe living preparations of microbial cells that * Corresponding author. Present address: Department of Biotechnology, Indian Institute of Technology, IIT Campus, Kharagpur 721302, India. Tel.: +91 1596245073; fax: +91 1596244183. E-mail address: [email protected] (R. Sen). 0032-9592/$ – see front matter # 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.procbio.2004.11.004
could be administered to animals, including humans, with a view to promoting the health of the consumer. This ‘‘friendly bacterial population’’ inhabits the gastro intestinal tract and contributes to optimal human health by boosting the immune system and improving digestion and assimilation of nutrients [3–5]. They are used in foods, especially in fermented dairy products, and also in pharmaceutical formulations. Such pharmaceutical formulations of probiotics are popularly known as nutraceuticals, which are credited with an impressive list of therapeutic and prophylactic attributes. Thus, by definition, a probiotic is a ‘‘live microbial feed supplement which beneficially affects the host animal by improving its intestinal microbial balance’’ [2,4,5]. The marketing of probiotics for human consumption relies heavily on this definition. Lactic acid bacteria conform to this definition and are used as probiotics. Lactobacillus sporogenes, which is mostly used in probiotic pharmaceutical formulations, is a lactic-acid bacillus. Lactic acid bacillus is a new generation spore bearing and lactic acid producing bacterium, a superior probiotic in terms of
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stability, ease of storage, and its highly favorable metabolic activity. Lactic acid bacillus has today virtually replaced traditional lactic acid bacteria such as Lactobacillus acidophilus in pharmaceutical and animal husbandry application. It is now being referred to as Bacillus coagulans in the seventh edition of Bergey’s Manual of Deterministic Bacteriology and is a ‘‘generally regarded as safe’’ (GRAS) product as per the USFDA guidelines for direct-fedmicrobials [6]. It is easily available in many countries as an OTC product. Bacillus coagulans (previously known as Lactobacillus sporogenes) is preferred to other probiotic strains mainly for its ability to form terminal endospores, which can easily survive in the harsh climate of the stomach and L(+)lactic acid, which is easily metabolized. It is safe and has excellent heat-stability thereby allowing for its carefree storage. It reduces intestinal absorption of cholesterol by retarding the secretion of bile salts in the gut [6]. It also helps in strengthening the immune system by probiotic activities [7] and by killing pathogens through the production of a bacteriocin, called coagulin [8]. All these attributes make B. coagulans an ideal probiotic candidate organism for the large-scale manufacture of nutraceuticals in rational combination with digestive enzymes, vitamins and antioxidants. The sporulation characteristics of B. coagulans are the same as other bacillus species. The germ cell or the central protoplast has all the constituents of the future vegetative cell, accompanied by calcium dipicolonate, which is essential to the heat resistance of the endospores. Surrounding the protoplast is a cortex consisting mostly of peptidoglycan (murein), which also plays an important role in the heat and chemical resistance of the spores. The cortical membrane or protoplast wall, called the inner layer, becomes the cell wall of the new vegetative cell after germination. The spore coats, which constitute up to 50% of the volume of the spore, protect it from chemicals and enzymes. Sporulation of B. coagulans cells can be induced by nutrient exhaustion using a sporulation broth medium and/or by subjecting the cells to some adverse environmental conditions including extremes of pH and temperature [9]. For any microorganism to be an effective probiotic, sporulation is an important characteristic in terms of its survival in the stomach and dry pharmaceutical formulations along with other ingredients including antibiotics and also in terms of its storage stability. The mass production of such a probiotic culture as B. coagulans is thus accomplished in the controlled environment of a fermenter. The environmental factors like pH, temperature, rates of agitation and aeration markedly influence the biomass production and endospore formation. With a view to enhancing the yields of biomass and spores of the probiotic culture, an attempt was made to model the fermentation process and optimize the process parameters and to characterize the cumulative and interactive effects of the physical parameters on biomass and spore production using response surface methodology [10,11]. Thus, a 24 full factorial central composite design
using RSM followed by multistage Monte Carlo optimization algorithm was used in this study for regression and graphical analyses of the data obtained [10–12].
2. Materials and methods 2.1. Chemicals All the chemicals were of reagent grade and were locally available. All media and components including glucoseyeast extract-acetate (GYEA) broth and agar were procured from Hi-Media, Mumbai, India. 2.2. Microbial culture The probiotic culture of B. coagulans RK-02 was isolated from soil sample containing dried animal excreta from a poultry farm near the city of Ahmedabad, Gujarat, India. Initial screening of the rod-shaped intestinal Bacillus cultures was done by the method of serial dilution and pour plating on Schaedler Anaerobe Agar (Oxoid-CM0437), which helps to eliminate coliforms. Single colonies were then isolated and grown in Hi-Media MRS-broth followed by serial dilution and plating on GYEA-agar medium. The embedded, eye-shaped and glistening colonies of Bacillus sp. RK-02 were picked up and further screened and isolated by the enrichment methods using Hi-Media sporulation broth and agar and tested for its microbiological and biochemical properties using standard methods [13,14]. The major microbiological properties indicated that the culture was Gram positive thin rods, motile with peritrichous flagella and spore-forming as indicated by green-stained small refractile terminal oval-shaped endospores. The analyses of the major biochemical properties demonstrated that the culture was extremely fastidious requiring complex media to grow and produce lactic acid from all important sugars homofermentatively, not able to hydrolyze starch, casein and gelatin, unable to produce hydrogen sulphide and other gases and was found to be catalase positive and indole negative. The pure slant cultures developed from such single colonies were identified and characterized as B. coagulans RK-02 by the microbial type culture collection (MTCC), Institute of Microbial Technology (CSIR, Government of India), Chandigarh, India. Thus B. coagulans RK-02 culture was used for this investigation. This culture was grown in GYEA broth medium at 37 8C for 24 h and maintained on GYEA agar plates at 40 8C. 2.3. Media composition The complex fermentation medium had the following composition: glucose 10 g l1, yeast extract 10 g l1, peptone 10 g l1, sodium acetate 5 g l1, triammonium citrate 5 g l1, phosphate buffer and trace elements like Ca2+, Mn2+, Mg2+, Fe2+, Co2+ in requisite quantities from
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the stock solutions. Glucose and phosphate buffer were sterilized and added separately to the sterile fermentation medium. The pH of the growth medium was adjusted to a value as specified by the experimental design for each experiment. 2.4. Fermenter studies Fermentation in batch mode was carried out in a 20 l Chemap fermenter with 12 l working volume and an inoculum of 10% (v/v) for 30 h. Our initial studies indicated that an inoculum size as high as 10% (v/v) resulted in high density biomass production. The environmental parameters: pH value, temperature (8C), rates of agitation (rpm) and aeration (vvm) were varied on the basis of the experimental design. The range and the levels of these process variables under study are given in Table 1. The pH of the medium was maintained by the controlled addition of either 2N H3PO4 or 2N NaOH. The temperature of fermentation and the rates of agitation and aeration were automatically controlled at their set point values, as specified by the experimental design. The samples were drawn at regular intervals and analyzed for biomass and spores. 2.5. Analytical methods 2.5.1. Biomass measurement Biomass concentrations were obtained by the measurement of absorbance of the broth samples using a UV–vis spectrophotometer (Shimadzu 1601 PC Model) at 600 nm. The broth samples drawn at regular intervals of four hours were diluted to an extent so that the optical density values are within 0.6. The unknown concentrations were determined from a calibration plot and were used as the response values in the experimental design.
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saline and vortexed to prepare a homogeneous suspension. This was used as stock to prepare serially diluted cell suspensions, which were subsequently heated to 75 8C for 30 min to kill the vegetative cells and poured onto plates with the sterile GYEA agar medium and incubated for 48 h. The single colonies which appeared on the plates, incubated with 106–108 times diluted suspensions were counted. The average values were taken to calculate the number of spores per gram of the dried biomass samples, which were subsequently used as the output response values in the design matrix. During fermentation process, standard Gram and spore staining methods were employed to check the sterility and quality of the samples. 2.6. Statistical procedure Response surface methodology (RSM) is an empirical technique employed for multiple regression analysis by using data obtained from designed experiments to simultaneously solve multivariate equations [10]. The graphical representations of these equations are called response surfaces, which can be used to describe the individual, cumulative and interactive effects of the test variables on the response, thereby evaluating the optimal factorial combination that produces maximum response. A central composite design coupled with a full second order polynomial model is a very powerful combination that efficiently provides an adequate representation of most continuous response surfaces without expending much of resources. A full factorial central composite rotatable design [10,11] is usually used to acquire data to fit an empirical second order polynomial model. For four factors, the quadratic model takes the following form: Yˆ ¼ b0 þ b1 x1i þ b2 x2i þ b3 x3i þ b4 x4i þ b11 x21i þ b22 x22i þ b33 x23i þ b44 x24i þ b12 x1i x2i
2.5.2. Isolation of biomass and spore count After the fermentation step, the broth sample was centrifuged in a Sorval RC-5C floor model centrifuge at 10,000 rpm for 20 min. The pellet was washed twice with normal saline solution (0.85–9% NaCl). After repeated washing, biomass was suspended in minimal volume of normal saline, which was used to preserve the cells in an isotonic environment and to protect them in a subsequent lyophilization step. The cell suspension was then dried in a lyophilizer (Unitop 400SI, Virtis). An amount of 1 mg of the freeze-dried biomass was then suspended in 10 ml of normal Table 1 Experimental range and levels of the test variables Variables
pH Temperature (0 8C) Agitation (rpm) Aeration (vvm)
Range and level 2
1
0
+1
+2
3.5 23 50 0
5 30 100 0.2
6.5 37 150 0.5
8 44 200 0.8
9.5 51 250 1.1
þ b13 x1i x3i þ b14 x1i x4i þ b23 x2i x3i þ b24 x2i x4i þ b34 x3i x4i þ ri
(1)
The coefficient b0 is the free or off-set term called intercept. The term r allows for uncertainties between what the model predicts and what was actually measured and stands for residual. In developing the regression equation the independent variables were coded according to the equation: Zi ¼
Xi Xi DXi
(2)
where Zi and Xi are the coded and natural values of the ith independent variable respectively, Xi is the uncoded value of the ith independent variable at the centre point and DXi is the step change value. Thus, a 24 full factorial central composite design [10,11] for four variables, each at five levels with eight star points and six replicates at the centre points employed to fit a
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quadratic model required 30 experiments to be performed. The Design Expert (Version 2.05, Stat-Ease, Inc., Minneapolis, USA) was used for regression and graphical analyses of the data obtained. The optimal values of the experimental conditions were obtained by analyzing the response surface contour plots and also by solving the regression equation using multistage Monte Carlo optimization program [11,12].
3. Results and discussion The results of probiotic research are mostly protected through patents by the private pharmaceutical companies. This resulted in the scarcity of published reports and information on the fermentative production yields of endospores and biomass of probiotic cultures like L. sporogenes or B. coagulans. There is one paper, which reports a biomass yield of 3.1 g l1 for the strain B. coagulans TB/04 in a batch reactor [15]. Hence this section mainly deals with the results and discussion of the present study. The time course of biomass growth and endospore formation indicated that the maximum biomass concentration was obtained in the late exponential phase of growth and the maximum sporulation was observed during the extended stationary phase of growth. Gram staining and microscopic observations of the broth samples showed crystal violet stained Gram positive thin rods, which sometime appear in chains. Motility of the bacterial population was confirmed in wet mount using the hanging drop method. Spore-staining of the broth samples drawn after 26 h of fermentation showed green stained refractile bodies in mature terminal spores (Fig. 1). The statistical treatment combinations of the process parameters along with the biomass concentrations (g l1) and spore yield (spore g1) as response values are listed in Table 2. The results of the second order response surface model fitting in the form of analysis of variance (ANOVA) are given in Table 3 for biomass yield and in Table 4 for spore yield. ANOVA is required to test the significance and adequacy of the models. The mean squares are obtained by dividing the sum of squares of each of the two sources of variation, the model and the error variance, by the respective degrees of freedom. The Fisher variance ratio, the F-value
(¼ S2r =S2e ), which is a statistically valid measure of how well the factors describe the variation in the data about its mean, can be calculated from ANOVA by dividing the mean square due to model variance by that due to error. The greater the F-value is from unity, the more certain it is that the factors explain adequately the variation in the data about its mean and the estimated factor effects are real. Thus, the Fisher F-test (F0.01 (14,15) = S2r =S2e ¼ 17 for biomass and 23 for spores) with a very low probability value (Pmodel > F = 0.0001) demonstrates a very high significance for the regression models. The correlation measures for the estimation of the regression equation are the multiple correlation coefficients (R) and the determination coefficient (R2). The closer the value of R is to 1, the better is the correlation between the observed and the predicted values. In this case, a higher value of the correlation coefficient, R (=0.97 for biomass and 0.978 for spores), justifies an excellent correlation between the predicted and the experimental values. The goodness of fit of the model was checked by the determination coefficient (R2). The value of the determination coefficient (R2 = 0.94 for biomass and 0.956 for spores) indicates that only about 6–4% of the total variations are not explained by the respective models. The value of the adjusted determination coefficients (Adj. R2 = 0.886 for biomass and 0.915 for spores) are also very high to advocate for a greater significance of the models. Relatively lower values of the coefficients of variation (CV = 12% for biomass and 22.3% for spores) indicate a better precision and reliability of the experiments carried out. The application of response surface methodology [10,11] gave rise to the following regression Eqs. (3a) and (3b) for biomass and spores respectively. These quadratic equations are empirical relationships between biomass yields and the test variables and spore yields and the test variables respectively in coded units: Yˆ biomass ¼ 2:759 0:0317x1 þ 0:068x2 þ 0:22x3 þ 0:26x4 0:525x21 0:31x22 0:098x23 0:0726x24 þ 0:123x1 :x2 þ 0:0475x1 :x3 þ 0:0325x1 :x4 þ 0:005x2 :x3 þ 0:0025x2 :x4 þ 0:276x3 :x4
(3a)
Yˆ spore ¼ 86:85 2:88x1 þ 13:21x2 2:23x3 5:93x4 20:686x21 18:81x22 3:16x23 13:72x24 2:433x1 :x2 þ 0:504x1 :x3 þ 1:46x1 :x4 1:1256x2 :x3 8:427x2 :x4 þ 6:94x3 :x4
Fig. 1. Terminal spores of B. coagulans as seen under microscope.
(3b)
where Yˆ stands for the response value, i.e., the biomass yield (3a) or spore yield (3b) and x1, x2, x3 and x4 are the coded values of the test variables pH, temperature, rates of agitation and aeration respectively.
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Table 2 Full factorial central composite design matrix of four variables in coded and natural units along with the observed responses, biomass concentration and spore yield Run no.
x1
x2
x3
x4
pH
Temperature (8C)
Agitation (rpm)
Aeration (vvm)
Biomass (g l1)
Spores ( 1010 gm)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 0 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 2 2 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 2 2 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 2 2 0 0 0 0 0 0
5 8 5 8 5 8 5 8 5 8 5 8 5 8 5 8 3.5 9.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 40 40 40
30 30 44 44 30 30 44 44 30 30 44 44 30 30 44 44 37 37 23 51 37 37 37 37 37 37 37 37 37 37
100 100 100 100 200 200 200 200 100 100 100 100 200 200 200 200 150 150 150 150 50 250 150 150 150 150 150 150 150 150
0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0 1.1 0.5 0.5 0.5 0.5 0.5 0.5
1.77 1.3 1.78 1.63 1.85 1.33 1.35 1.64 1.95 1.36 1.43 1.67 2.55 2.51 2.7 2.76 0.36 0.57 1 1.65 1.8 2.55 1.9 2.76 2.76 2.77 2.76 2.78 2.77 2.78
15.45 10.36 85.75 65.46 9.33 6.64 56.37 40.56 13.66 9.94 26.65 19.52 17.55 16.33 45.65 37.21 6.67 4.33 3.25 22.79 84.66 66.53 57.67 23.56 86.87 86.84 86.79 87 86.96 87.93
The significance of each coefficient of the above equations was determined by Student’s t-test and P-values, which are listed in Table 4. The P-values are used as a tool to check the significance of each coefficient, which in turn are necessary to understand the pattern of the mutual interactions between the test variables. The larger the magnitude of t-value and smaller the P-value, the more significant is the corresponding coefficient [10] Table 5. From the data presented above, it is evident that the quadratic main effects of pH and temperature (P 0.0001) are more significant than their respective first order effects for biomass, but both the first and second order effects of temperature are equally important in case of spores. Since both the factors are very much significant in the quadratic level, a little variation in their values will affect the rates of growth and sporulation to a great extent. Although the
interactive effect of pH and temperature on biomass production is quite significant (P 0.048), it is not translated into higher biomass production. Whereas the first order main effects of both agitation (X3) and aeration (X4) are highly significant as is understood from their respective P-values (Pagitation 0.0004 and Paerationn 0.0001) as compared to their second order main effects in case of biomass production. These suggest that there exists a direct proportional relationship between the rates of agitation and aeration (X3 X4) and the biomass production. These observations are also substantiated by the fact that the interaction between agitation and aeration is very much significant (P 0.0003) and is found to be solely responsible for achieving a relatively higher biomass yield of about 4–4.3 g l1 as predicted by the model and the response surface contour plot (Fig. 2). Thus this strong
Table 3 Analysis of variance (ANOVA) for the quadratic model for biomass yield Sources of variations
Sum of squares
Model 13.66465 Error 0.85924 Corrected total 14.52390 Root MSE = 0.24; CV = 12.2%; R2 = 0.941; R = 0.97; Adj.
Degrees of freedom
Mean square
F-value
Probability (P) > F
14 15 29 R2 = 0.886
0.97605 0.0573
17.04
0.0001
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Table 4 Analysis of variance (ANOVA) for the quadratic model for spore yield Sources of variations
Sum of squares
Degrees of freedom
Model 28867.48 14 Error 1332.74 15 Corrected total 30200.22 29 Root MSE = 9.426; CV = 22.3%; R2 = 0.956; R = 0.978; Adj. R2 = 0.915
Mean square
F-value
Probability (P) > F
2061.97 88.85
23.21
0.0001
Table 5 Least squares fit and parameter estimates for biomass and endospores Model term
Intercept x1 x2 x3 x4 x1 x1 x2 x2 x3 x3 x4 x4 x1 x2 x1 x3 x1 x4 x2 x3 x2 x4 x3 x4
Parameter estimate
Computed t-value
P-value
Biomass
Spores
Biomass
Spores
Biomass
Spores
2.759 0.0317 0.068 0.22 0.26 0.525 0.31 0.098 0.0726 0.128 0.0475 0.0325 0.005 0.0025 0.276
86.85 2.88 13.21 2.23 5.93 20.69 18.81 3.16 13.72 2.433 0.504 1.461 1.126 8.423 6.94
28.36 0.65 1.399 4.520 5.166 11.54 6.816 2.15 1.4 2.15 0.794 0.543 0.0084 0.0042 4.617
22.67 1.496 6.865 1.157 2.974 11.54 10.5 1.764 6.726 1.03 0.214 0.62 0.478 3.576 2.946
– 0.5267 0.1822 0.0004 0.0001 0.0001 0.0001 0.0483 0.1810 0.0481 0.4397 0.5950 0.9345 0.9672 0.0003
– 0.1554 0.0001 0.2653 0.0095 0.0001 0.0001 0.0980 0.0001 0.3128 0.8334 0.5447 0.6398 0.0028 0.0100
The standard error of mean = 0.0437 (biomass) and 1.72 (spores).
interaction between agitation and aeration alone increases biomass concentration by about 55%. So is the case for endospore formation. While the first order individual effect of aeration on sporulation is more significant than that of agitation, the second order main effect of agitation on spore formation is more dominant over that of aeration. This suggests that the parameters, agitation and aeration as well as the related factors like oxygen and nutrient transfer rates are very critical for spore formation. This inference is well supported by the fact that there is a strong interaction between agitation and aeration as exhibited by the corresponding P-value (0.01) and the typical elliptical contour diagram (Fig. 3). Temperature plays an important role in both biomass and endospore production as is evident from its quadratic main effect for biomass and both first and second order main effects for spore production. A relatively strong interaction between temperature and aeration, which is not reflected by the corresponding P-value (0.9624) is understood from the elliptical nature of the contour curves and is responsible for an increase in biomass yield from 2.75 to about 3 g l1 (Fig. 4). Each contour curve represents an infinite number of combinations of two test variables with the other two maintained at their respective zero level. The maximum predicted yield is indicated by the surface confined in the smallest ellipse in the contour diagram. Thus a relatively higher temperature and a very high aeration rate favor biomass production. In case of sporulation, the first order main effects of both temperature and aeration are very
prominent. It is also to be noted that the effect of interaction between temperature and aeration is also very significant as indicated by the corresponding P-value (0.0028) and the response surface diagram (Fig. 5). This close and positive interaction between aeration and temperature can be attributed to the fact that dissolved oxygen concentration and volumetric oxygen transfer coefficient (KLa) play an important role in the process of sporulation as is indicated by the response surface plot. Therefore a judicious combination
Fig. 2. Response surface contour plot showing the effect of interaction between agitation and aeration on biomass with other two variables, temperature and pH held at their zero levels.
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Fig. 3. Response surface contour plot showing the interactive effect between agitation and aeration on spore yield, with temperature and pH maintained at their zero levels.
of higher temperature and lower aeration rate is conducive for sporulation. These findings are particularly of importance as they help in designing a fermentation process. Thus a two-stage strategy with biomass production in exponential phase under the optimal growth conditions in the first stage followed by the second stage in stationary phase under the optimal conditions for sporulation can be adopted to maximize the biomass yield and spore count. From the analysis of the response surface contour plots, it is evident that the optimal values of the process parameters for maximum growth and biomass production were pH = 6.6– 6.7, temperature = 37–38 8C, agitation = 245–255 rpm and
Fig. 4. Response surface contour plot indicating the effect of interaction between temperature and aeration on biomass yield while holding other two factors at their central values.
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Fig. 5. Isoresponse contour diagram to show the effect of the interaction between temperature and aeration on the spore yield with the other two variables kept at their zero levels.
aeration = 1–1.1 vvm and those for the maximum sporulation were pH = 6.2–6.3, temperature = 40.5–42 8C, agitation = 110–120 rpm and aeration = 0.30–0.4 vvm. The multistage Monte Carlo optimization method [12] was used to solve the regression Eqs. (3a) and (3b). The optimal experimental conditions for maximum biomass production in coded unit are x1 = 0.1, x2 = 0.1857, x3 = 1.94, x4 = 1.83 and those for maximum endospore formation are x1 = 0.1533, x2 = 0.6286, x3 = 0.68, x4 = 0.567. The natural values obtained by putting the respective values of xi in Eq. (3a) are pH = 6.65, temperature = 38.3 8C, agitation = 247 rpm and aeration = 1.05 vvm for maximum biomass production and in Eq. (3b) are pH = 6.27, temperature = 41.4 8C, agitation = 116 rpm and aeration = 0.33 vvm for the maximum sporulation. The model predicts a maximum biomass concentration (Yˆ ) of 4.3 g l1 and a maximum spore count of 9.4 1011 spores per gram under the above optimum environmental conditions. The experimental results at the optimized values of the test variables as per the model showed a biomass yield of 4.3 g l1, which is about 39% higher than the previously
Fig. 6. Dynamics of biomass growth and endospore formation under optimum environmental conditions.
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reported result and a maximum spore yield of 9 1011 spores per gram of dry biomass. These data are in close agreement with the model predictions. The dynamics of biomass growth and sporulation can be easily understood from the time-course profiles of the culture pH, cellular growth and endospore formation (Fig. 6).
4. Conclusions Response surface methodology was employed for modeling, optimization and analysis of a fermentation process to enhance the yields of biomass and spores of a probiotic culture. This research endeavor is a part of the process development exercise for the lab-scale manufacture of nutraceutical formulations, which help in maintaining the normal balance of the intestinal micro-flora in antibiotic therapy-associated gastro-intestinal disorders. The optimal process conditions are different for biomass and endospore production. Hence a two-stage strategy with biomass production in the first stage in the optimal environment, which stimulates more growth followed by the second stage involving the production of spores under the optimum operating conditions conducive for sporulation. By adopting such strategy and by employing the optimum experimental conditions, an enhancement in biomass production by about 55% was achieved with an improved spore yield of 9 1011 spores per gram of dry biomass.
Acknowledgement The authors thank the R&D management of the Cadila Pharma Ltd., Ahmedabad, India, for its kind permission to use the Fermentation Laboratory and Pilot Plant facilities for the experimental work. The first author also gratefully acknowledges the financial support as a minor project, received from the University Grants Commission, India
through his institute BITS, Pilani, for the theoretical modeling part of the present study.
Reference [1] Tannock GW. Normal Microflora: An Introduction to Microbes Inhabiting the Human Body. London: Chapman & Hall; 1995. [2] Fuller R, editor. Probiotics: Therapeutic and Other Beneficial Effects. London: Chapman & Hall; 1997. [3] Perdigon G, Vintini E, Alvarez S, Medina M, Medici M. Study of the possible mechanisms involved in the mucosal immune system activation by lactic acid bacteria. J Dairy Sci 1999;82(6):1108–14. [4] Sanders ME. Probiotics. Food Technol 1999;53:67–77. [5] Tannock GW. Probiotics: A Critical Review. Norfolk, England: Horizon Scientific Press; 1999. [6] Kumar D. The Advanced Orobiotic. URL: http://www.pharmedmedicare.com/lactopure.html, May 2003. [7] Adami A, Cavazzoni V. Occurrence of selected bacterial groups in the faeces of piglets fed with Bacillus coagulans as probiotic. J Basic Microbiol 1999;39:3–9. [8] Hyronimus B, Le Marrec C, Urdaci MC. Coagulin, a bacteriocin-like inhibitory substance produced by Bacillus coagulans. J Appl Microbiol 1998;85:42–50. [9] Turnbull PCB. Kramer JM, Melling J, Bacillus. In: Parker MT, Duerden BI., editors. Systematic Bacteriology Topley and Wilson’s Principles of Bacteriology Virology and Immunity, vol. 2. Seven Oaks, England: Edward Arnold; 1990. [10] Khuri AI, Cornell JA. Response Surfaces: Design and Analyses. New York: Dekker; 1987. [11] Sen R, Swaminathan T. Application of response surface methodology to evaluate the optimum environmental conditions for the enhanced production of surfactin. Appl Microbiol Biotechnol 1997;47:358–63. [12] Conley WC. Computer Optimization Techniques. Princeton, NJ: Petrocelli Books; 1984. [13] Conway PL, Henriksson A. Strategies for the isolation and characterization of functional probiotics. In: Human Health: The Contribution of Microorganisms. New York: Springer-Verlag; 1994. [14] Kalogridou-Vassiliadou D. Biochemical activities of Bacillus species isolated from flat sour evaporated milk. J Dairy Sci 1992;75:2681–6. [15] Payot T, Chemaly Z, Fick M. Lactic acid production by Bacillus coagulans – kinetic studies and optimization of culture medium for batch and continuous fermentations. Enzyme Microb Technol 1999;24:191–9.
Modeling and optimization of the process conditions for biomass production and sporulation of a probiotic culture Ramkrishna Sena,*, K. Srinivasa Babub a
Centre for Biotechnology, Biological Sciences Group, Birla Institute of Technology and Science (BITS), Pilani, Rajasthan 333031, India b Biotechnology Department, R&D Centre, Cadila Pharmaceuticals Ltd., Ahmedabad 387810, India Received 3 April 2004; received in revised form 21 September 2004; accepted 22 November 2004
Abstract The present study is aimed at evaluating and analyzing the effects of the critical process parameters by using response surface modeling method and optimizing the same to enhance the yields of biomass and endospores of a probiotic culture. A 24 full factorial central composite design followed by a multistage Monte Carlo optimization was employed for experimental design and analysis of the results and process optimization. The optimal process conditions for maximum biomass production were: pH = 6.65; temperature = 38.3 8C; agitation = 247 rpm and aeration = 1.05 vvm and those for the maximum sporulation were: pH = 6.27; temperature = 41.4 8C; agitation = 115 rpm and aeration = 0.33 vvm. Hence, a two-stage strategy with biomass production in exponential phase under the optimal growth conditions in the first stage followed by the second stage in stationary phase under the optimal conditions for sporulation was thus adopted to obtain a maximum probiotic biomass yield of 4.3 g l1 and spore yield of 9 1011 spores g1 of dry biomass for the formulation of effective nutraceuticals. # 2004 Elsevier Ltd. All rights reserved. Keywords: Probiotic; Response surface modeling; Statistical analysis; Process optimization
1. Introduction The gastro-intestinal tract of humans and animals is colonized by a very complex and balanced micro biota. These microorganisms normally prevent infection and have a positive effect on nutrition. Any abrupt change in diet, stress, or antibiotic therapy can upset this microbial balance, making the host susceptible to disease and decreasing the efficiency of food use [1,2]. The term ‘‘probiotic’’ was originally referred to the growth promoting substances associated with a phenomenon, probiosis, observed when two organisms were cultured together (co-cultured), in which metabolites produced by one organism stimulated the growth of the other organism. The term was subsequently used to describe living preparations of microbial cells that * Corresponding author. Present address: Department of Biotechnology, Indian Institute of Technology, IIT Campus, Kharagpur 721302, India. Tel.: +91 1596245073; fax: +91 1596244183. E-mail address: [email protected] (R. Sen). 0032-9592/$ – see front matter # 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.procbio.2004.11.004
could be administered to animals, including humans, with a view to promoting the health of the consumer. This ‘‘friendly bacterial population’’ inhabits the gastro intestinal tract and contributes to optimal human health by boosting the immune system and improving digestion and assimilation of nutrients [3–5]. They are used in foods, especially in fermented dairy products, and also in pharmaceutical formulations. Such pharmaceutical formulations of probiotics are popularly known as nutraceuticals, which are credited with an impressive list of therapeutic and prophylactic attributes. Thus, by definition, a probiotic is a ‘‘live microbial feed supplement which beneficially affects the host animal by improving its intestinal microbial balance’’ [2,4,5]. The marketing of probiotics for human consumption relies heavily on this definition. Lactic acid bacteria conform to this definition and are used as probiotics. Lactobacillus sporogenes, which is mostly used in probiotic pharmaceutical formulations, is a lactic-acid bacillus. Lactic acid bacillus is a new generation spore bearing and lactic acid producing bacterium, a superior probiotic in terms of
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stability, ease of storage, and its highly favorable metabolic activity. Lactic acid bacillus has today virtually replaced traditional lactic acid bacteria such as Lactobacillus acidophilus in pharmaceutical and animal husbandry application. It is now being referred to as Bacillus coagulans in the seventh edition of Bergey’s Manual of Deterministic Bacteriology and is a ‘‘generally regarded as safe’’ (GRAS) product as per the USFDA guidelines for direct-fedmicrobials [6]. It is easily available in many countries as an OTC product. Bacillus coagulans (previously known as Lactobacillus sporogenes) is preferred to other probiotic strains mainly for its ability to form terminal endospores, which can easily survive in the harsh climate of the stomach and L(+)lactic acid, which is easily metabolized. It is safe and has excellent heat-stability thereby allowing for its carefree storage. It reduces intestinal absorption of cholesterol by retarding the secretion of bile salts in the gut [6]. It also helps in strengthening the immune system by probiotic activities [7] and by killing pathogens through the production of a bacteriocin, called coagulin [8]. All these attributes make B. coagulans an ideal probiotic candidate organism for the large-scale manufacture of nutraceuticals in rational combination with digestive enzymes, vitamins and antioxidants. The sporulation characteristics of B. coagulans are the same as other bacillus species. The germ cell or the central protoplast has all the constituents of the future vegetative cell, accompanied by calcium dipicolonate, which is essential to the heat resistance of the endospores. Surrounding the protoplast is a cortex consisting mostly of peptidoglycan (murein), which also plays an important role in the heat and chemical resistance of the spores. The cortical membrane or protoplast wall, called the inner layer, becomes the cell wall of the new vegetative cell after germination. The spore coats, which constitute up to 50% of the volume of the spore, protect it from chemicals and enzymes. Sporulation of B. coagulans cells can be induced by nutrient exhaustion using a sporulation broth medium and/or by subjecting the cells to some adverse environmental conditions including extremes of pH and temperature [9]. For any microorganism to be an effective probiotic, sporulation is an important characteristic in terms of its survival in the stomach and dry pharmaceutical formulations along with other ingredients including antibiotics and also in terms of its storage stability. The mass production of such a probiotic culture as B. coagulans is thus accomplished in the controlled environment of a fermenter. The environmental factors like pH, temperature, rates of agitation and aeration markedly influence the biomass production and endospore formation. With a view to enhancing the yields of biomass and spores of the probiotic culture, an attempt was made to model the fermentation process and optimize the process parameters and to characterize the cumulative and interactive effects of the physical parameters on biomass and spore production using response surface methodology [10,11]. Thus, a 24 full factorial central composite design
using RSM followed by multistage Monte Carlo optimization algorithm was used in this study for regression and graphical analyses of the data obtained [10–12].
2. Materials and methods 2.1. Chemicals All the chemicals were of reagent grade and were locally available. All media and components including glucoseyeast extract-acetate (GYEA) broth and agar were procured from Hi-Media, Mumbai, India. 2.2. Microbial culture The probiotic culture of B. coagulans RK-02 was isolated from soil sample containing dried animal excreta from a poultry farm near the city of Ahmedabad, Gujarat, India. Initial screening of the rod-shaped intestinal Bacillus cultures was done by the method of serial dilution and pour plating on Schaedler Anaerobe Agar (Oxoid-CM0437), which helps to eliminate coliforms. Single colonies were then isolated and grown in Hi-Media MRS-broth followed by serial dilution and plating on GYEA-agar medium. The embedded, eye-shaped and glistening colonies of Bacillus sp. RK-02 were picked up and further screened and isolated by the enrichment methods using Hi-Media sporulation broth and agar and tested for its microbiological and biochemical properties using standard methods [13,14]. The major microbiological properties indicated that the culture was Gram positive thin rods, motile with peritrichous flagella and spore-forming as indicated by green-stained small refractile terminal oval-shaped endospores. The analyses of the major biochemical properties demonstrated that the culture was extremely fastidious requiring complex media to grow and produce lactic acid from all important sugars homofermentatively, not able to hydrolyze starch, casein and gelatin, unable to produce hydrogen sulphide and other gases and was found to be catalase positive and indole negative. The pure slant cultures developed from such single colonies were identified and characterized as B. coagulans RK-02 by the microbial type culture collection (MTCC), Institute of Microbial Technology (CSIR, Government of India), Chandigarh, India. Thus B. coagulans RK-02 culture was used for this investigation. This culture was grown in GYEA broth medium at 37 8C for 24 h and maintained on GYEA agar plates at 40 8C. 2.3. Media composition The complex fermentation medium had the following composition: glucose 10 g l1, yeast extract 10 g l1, peptone 10 g l1, sodium acetate 5 g l1, triammonium citrate 5 g l1, phosphate buffer and trace elements like Ca2+, Mn2+, Mg2+, Fe2+, Co2+ in requisite quantities from
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the stock solutions. Glucose and phosphate buffer were sterilized and added separately to the sterile fermentation medium. The pH of the growth medium was adjusted to a value as specified by the experimental design for each experiment. 2.4. Fermenter studies Fermentation in batch mode was carried out in a 20 l Chemap fermenter with 12 l working volume and an inoculum of 10% (v/v) for 30 h. Our initial studies indicated that an inoculum size as high as 10% (v/v) resulted in high density biomass production. The environmental parameters: pH value, temperature (8C), rates of agitation (rpm) and aeration (vvm) were varied on the basis of the experimental design. The range and the levels of these process variables under study are given in Table 1. The pH of the medium was maintained by the controlled addition of either 2N H3PO4 or 2N NaOH. The temperature of fermentation and the rates of agitation and aeration were automatically controlled at their set point values, as specified by the experimental design. The samples were drawn at regular intervals and analyzed for biomass and spores. 2.5. Analytical methods 2.5.1. Biomass measurement Biomass concentrations were obtained by the measurement of absorbance of the broth samples using a UV–vis spectrophotometer (Shimadzu 1601 PC Model) at 600 nm. The broth samples drawn at regular intervals of four hours were diluted to an extent so that the optical density values are within 0.6. The unknown concentrations were determined from a calibration plot and were used as the response values in the experimental design.
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saline and vortexed to prepare a homogeneous suspension. This was used as stock to prepare serially diluted cell suspensions, which were subsequently heated to 75 8C for 30 min to kill the vegetative cells and poured onto plates with the sterile GYEA agar medium and incubated for 48 h. The single colonies which appeared on the plates, incubated with 106–108 times diluted suspensions were counted. The average values were taken to calculate the number of spores per gram of the dried biomass samples, which were subsequently used as the output response values in the design matrix. During fermentation process, standard Gram and spore staining methods were employed to check the sterility and quality of the samples. 2.6. Statistical procedure Response surface methodology (RSM) is an empirical technique employed for multiple regression analysis by using data obtained from designed experiments to simultaneously solve multivariate equations [10]. The graphical representations of these equations are called response surfaces, which can be used to describe the individual, cumulative and interactive effects of the test variables on the response, thereby evaluating the optimal factorial combination that produces maximum response. A central composite design coupled with a full second order polynomial model is a very powerful combination that efficiently provides an adequate representation of most continuous response surfaces without expending much of resources. A full factorial central composite rotatable design [10,11] is usually used to acquire data to fit an empirical second order polynomial model. For four factors, the quadratic model takes the following form: Yˆ ¼ b0 þ b1 x1i þ b2 x2i þ b3 x3i þ b4 x4i þ b11 x21i þ b22 x22i þ b33 x23i þ b44 x24i þ b12 x1i x2i
2.5.2. Isolation of biomass and spore count After the fermentation step, the broth sample was centrifuged in a Sorval RC-5C floor model centrifuge at 10,000 rpm for 20 min. The pellet was washed twice with normal saline solution (0.85–9% NaCl). After repeated washing, biomass was suspended in minimal volume of normal saline, which was used to preserve the cells in an isotonic environment and to protect them in a subsequent lyophilization step. The cell suspension was then dried in a lyophilizer (Unitop 400SI, Virtis). An amount of 1 mg of the freeze-dried biomass was then suspended in 10 ml of normal Table 1 Experimental range and levels of the test variables Variables
pH Temperature (0 8C) Agitation (rpm) Aeration (vvm)
Range and level 2
1
0
+1
+2
3.5 23 50 0
5 30 100 0.2
6.5 37 150 0.5
8 44 200 0.8
9.5 51 250 1.1
þ b13 x1i x3i þ b14 x1i x4i þ b23 x2i x3i þ b24 x2i x4i þ b34 x3i x4i þ ri
(1)
The coefficient b0 is the free or off-set term called intercept. The term r allows for uncertainties between what the model predicts and what was actually measured and stands for residual. In developing the regression equation the independent variables were coded according to the equation: Zi ¼
Xi Xi DXi
(2)
where Zi and Xi are the coded and natural values of the ith independent variable respectively, Xi is the uncoded value of the ith independent variable at the centre point and DXi is the step change value. Thus, a 24 full factorial central composite design [10,11] for four variables, each at five levels with eight star points and six replicates at the centre points employed to fit a
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quadratic model required 30 experiments to be performed. The Design Expert (Version 2.05, Stat-Ease, Inc., Minneapolis, USA) was used for regression and graphical analyses of the data obtained. The optimal values of the experimental conditions were obtained by analyzing the response surface contour plots and also by solving the regression equation using multistage Monte Carlo optimization program [11,12].
3. Results and discussion The results of probiotic research are mostly protected through patents by the private pharmaceutical companies. This resulted in the scarcity of published reports and information on the fermentative production yields of endospores and biomass of probiotic cultures like L. sporogenes or B. coagulans. There is one paper, which reports a biomass yield of 3.1 g l1 for the strain B. coagulans TB/04 in a batch reactor [15]. Hence this section mainly deals with the results and discussion of the present study. The time course of biomass growth and endospore formation indicated that the maximum biomass concentration was obtained in the late exponential phase of growth and the maximum sporulation was observed during the extended stationary phase of growth. Gram staining and microscopic observations of the broth samples showed crystal violet stained Gram positive thin rods, which sometime appear in chains. Motility of the bacterial population was confirmed in wet mount using the hanging drop method. Spore-staining of the broth samples drawn after 26 h of fermentation showed green stained refractile bodies in mature terminal spores (Fig. 1). The statistical treatment combinations of the process parameters along with the biomass concentrations (g l1) and spore yield (spore g1) as response values are listed in Table 2. The results of the second order response surface model fitting in the form of analysis of variance (ANOVA) are given in Table 3 for biomass yield and in Table 4 for spore yield. ANOVA is required to test the significance and adequacy of the models. The mean squares are obtained by dividing the sum of squares of each of the two sources of variation, the model and the error variance, by the respective degrees of freedom. The Fisher variance ratio, the F-value
(¼ S2r =S2e ), which is a statistically valid measure of how well the factors describe the variation in the data about its mean, can be calculated from ANOVA by dividing the mean square due to model variance by that due to error. The greater the F-value is from unity, the more certain it is that the factors explain adequately the variation in the data about its mean and the estimated factor effects are real. Thus, the Fisher F-test (F0.01 (14,15) = S2r =S2e ¼ 17 for biomass and 23 for spores) with a very low probability value (Pmodel > F = 0.0001) demonstrates a very high significance for the regression models. The correlation measures for the estimation of the regression equation are the multiple correlation coefficients (R) and the determination coefficient (R2). The closer the value of R is to 1, the better is the correlation between the observed and the predicted values. In this case, a higher value of the correlation coefficient, R (=0.97 for biomass and 0.978 for spores), justifies an excellent correlation between the predicted and the experimental values. The goodness of fit of the model was checked by the determination coefficient (R2). The value of the determination coefficient (R2 = 0.94 for biomass and 0.956 for spores) indicates that only about 6–4% of the total variations are not explained by the respective models. The value of the adjusted determination coefficients (Adj. R2 = 0.886 for biomass and 0.915 for spores) are also very high to advocate for a greater significance of the models. Relatively lower values of the coefficients of variation (CV = 12% for biomass and 22.3% for spores) indicate a better precision and reliability of the experiments carried out. The application of response surface methodology [10,11] gave rise to the following regression Eqs. (3a) and (3b) for biomass and spores respectively. These quadratic equations are empirical relationships between biomass yields and the test variables and spore yields and the test variables respectively in coded units: Yˆ biomass ¼ 2:759 0:0317x1 þ 0:068x2 þ 0:22x3 þ 0:26x4 0:525x21 0:31x22 0:098x23 0:0726x24 þ 0:123x1 :x2 þ 0:0475x1 :x3 þ 0:0325x1 :x4 þ 0:005x2 :x3 þ 0:0025x2 :x4 þ 0:276x3 :x4
(3a)
Yˆ spore ¼ 86:85 2:88x1 þ 13:21x2 2:23x3 5:93x4 20:686x21 18:81x22 3:16x23 13:72x24 2:433x1 :x2 þ 0:504x1 :x3 þ 1:46x1 :x4 1:1256x2 :x3 8:427x2 :x4 þ 6:94x3 :x4
Fig. 1. Terminal spores of B. coagulans as seen under microscope.
(3b)
where Yˆ stands for the response value, i.e., the biomass yield (3a) or spore yield (3b) and x1, x2, x3 and x4 are the coded values of the test variables pH, temperature, rates of agitation and aeration respectively.
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Table 2 Full factorial central composite design matrix of four variables in coded and natural units along with the observed responses, biomass concentration and spore yield Run no.
x1
x2
x3
x4
pH
Temperature (8C)
Agitation (rpm)
Aeration (vvm)
Biomass (g l1)
Spores ( 1010 gm)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 0 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 2 2 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 2 2 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 2 2 0 0 0 0 0 0
5 8 5 8 5 8 5 8 5 8 5 8 5 8 5 8 3.5 9.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 40 40 40
30 30 44 44 30 30 44 44 30 30 44 44 30 30 44 44 37 37 23 51 37 37 37 37 37 37 37 37 37 37
100 100 100 100 200 200 200 200 100 100 100 100 200 200 200 200 150 150 150 150 50 250 150 150 150 150 150 150 150 150
0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0 1.1 0.5 0.5 0.5 0.5 0.5 0.5
1.77 1.3 1.78 1.63 1.85 1.33 1.35 1.64 1.95 1.36 1.43 1.67 2.55 2.51 2.7 2.76 0.36 0.57 1 1.65 1.8 2.55 1.9 2.76 2.76 2.77 2.76 2.78 2.77 2.78
15.45 10.36 85.75 65.46 9.33 6.64 56.37 40.56 13.66 9.94 26.65 19.52 17.55 16.33 45.65 37.21 6.67 4.33 3.25 22.79 84.66 66.53 57.67 23.56 86.87 86.84 86.79 87 86.96 87.93
The significance of each coefficient of the above equations was determined by Student’s t-test and P-values, which are listed in Table 4. The P-values are used as a tool to check the significance of each coefficient, which in turn are necessary to understand the pattern of the mutual interactions between the test variables. The larger the magnitude of t-value and smaller the P-value, the more significant is the corresponding coefficient [10] Table 5. From the data presented above, it is evident that the quadratic main effects of pH and temperature (P 0.0001) are more significant than their respective first order effects for biomass, but both the first and second order effects of temperature are equally important in case of spores. Since both the factors are very much significant in the quadratic level, a little variation in their values will affect the rates of growth and sporulation to a great extent. Although the
interactive effect of pH and temperature on biomass production is quite significant (P 0.048), it is not translated into higher biomass production. Whereas the first order main effects of both agitation (X3) and aeration (X4) are highly significant as is understood from their respective P-values (Pagitation 0.0004 and Paerationn 0.0001) as compared to their second order main effects in case of biomass production. These suggest that there exists a direct proportional relationship between the rates of agitation and aeration (X3 X4) and the biomass production. These observations are also substantiated by the fact that the interaction between agitation and aeration is very much significant (P 0.0003) and is found to be solely responsible for achieving a relatively higher biomass yield of about 4–4.3 g l1 as predicted by the model and the response surface contour plot (Fig. 2). Thus this strong
Table 3 Analysis of variance (ANOVA) for the quadratic model for biomass yield Sources of variations
Sum of squares
Model 13.66465 Error 0.85924 Corrected total 14.52390 Root MSE = 0.24; CV = 12.2%; R2 = 0.941; R = 0.97; Adj.
Degrees of freedom
Mean square
F-value
Probability (P) > F
14 15 29 R2 = 0.886
0.97605 0.0573
17.04
0.0001
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Table 4 Analysis of variance (ANOVA) for the quadratic model for spore yield Sources of variations
Sum of squares
Degrees of freedom
Model 28867.48 14 Error 1332.74 15 Corrected total 30200.22 29 Root MSE = 9.426; CV = 22.3%; R2 = 0.956; R = 0.978; Adj. R2 = 0.915
Mean square
F-value
Probability (P) > F
2061.97 88.85
23.21
0.0001
Table 5 Least squares fit and parameter estimates for biomass and endospores Model term
Intercept x1 x2 x3 x4 x1 x1 x2 x2 x3 x3 x4 x4 x1 x2 x1 x3 x1 x4 x2 x3 x2 x4 x3 x4
Parameter estimate
Computed t-value
P-value
Biomass
Spores
Biomass
Spores
Biomass
Spores
2.759 0.0317 0.068 0.22 0.26 0.525 0.31 0.098 0.0726 0.128 0.0475 0.0325 0.005 0.0025 0.276
86.85 2.88 13.21 2.23 5.93 20.69 18.81 3.16 13.72 2.433 0.504 1.461 1.126 8.423 6.94
28.36 0.65 1.399 4.520 5.166 11.54 6.816 2.15 1.4 2.15 0.794 0.543 0.0084 0.0042 4.617
22.67 1.496 6.865 1.157 2.974 11.54 10.5 1.764 6.726 1.03 0.214 0.62 0.478 3.576 2.946
– 0.5267 0.1822 0.0004 0.0001 0.0001 0.0001 0.0483 0.1810 0.0481 0.4397 0.5950 0.9345 0.9672 0.0003
– 0.1554 0.0001 0.2653 0.0095 0.0001 0.0001 0.0980 0.0001 0.3128 0.8334 0.5447 0.6398 0.0028 0.0100
The standard error of mean = 0.0437 (biomass) and 1.72 (spores).
interaction between agitation and aeration alone increases biomass concentration by about 55%. So is the case for endospore formation. While the first order individual effect of aeration on sporulation is more significant than that of agitation, the second order main effect of agitation on spore formation is more dominant over that of aeration. This suggests that the parameters, agitation and aeration as well as the related factors like oxygen and nutrient transfer rates are very critical for spore formation. This inference is well supported by the fact that there is a strong interaction between agitation and aeration as exhibited by the corresponding P-value (0.01) and the typical elliptical contour diagram (Fig. 3). Temperature plays an important role in both biomass and endospore production as is evident from its quadratic main effect for biomass and both first and second order main effects for spore production. A relatively strong interaction between temperature and aeration, which is not reflected by the corresponding P-value (0.9624) is understood from the elliptical nature of the contour curves and is responsible for an increase in biomass yield from 2.75 to about 3 g l1 (Fig. 4). Each contour curve represents an infinite number of combinations of two test variables with the other two maintained at their respective zero level. The maximum predicted yield is indicated by the surface confined in the smallest ellipse in the contour diagram. Thus a relatively higher temperature and a very high aeration rate favor biomass production. In case of sporulation, the first order main effects of both temperature and aeration are very
prominent. It is also to be noted that the effect of interaction between temperature and aeration is also very significant as indicated by the corresponding P-value (0.0028) and the response surface diagram (Fig. 5). This close and positive interaction between aeration and temperature can be attributed to the fact that dissolved oxygen concentration and volumetric oxygen transfer coefficient (KLa) play an important role in the process of sporulation as is indicated by the response surface plot. Therefore a judicious combination
Fig. 2. Response surface contour plot showing the effect of interaction between agitation and aeration on biomass with other two variables, temperature and pH held at their zero levels.
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Fig. 3. Response surface contour plot showing the interactive effect between agitation and aeration on spore yield, with temperature and pH maintained at their zero levels.
of higher temperature and lower aeration rate is conducive for sporulation. These findings are particularly of importance as they help in designing a fermentation process. Thus a two-stage strategy with biomass production in exponential phase under the optimal growth conditions in the first stage followed by the second stage in stationary phase under the optimal conditions for sporulation can be adopted to maximize the biomass yield and spore count. From the analysis of the response surface contour plots, it is evident that the optimal values of the process parameters for maximum growth and biomass production were pH = 6.6– 6.7, temperature = 37–38 8C, agitation = 245–255 rpm and
Fig. 4. Response surface contour plot indicating the effect of interaction between temperature and aeration on biomass yield while holding other two factors at their central values.
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Fig. 5. Isoresponse contour diagram to show the effect of the interaction between temperature and aeration on the spore yield with the other two variables kept at their zero levels.
aeration = 1–1.1 vvm and those for the maximum sporulation were pH = 6.2–6.3, temperature = 40.5–42 8C, agitation = 110–120 rpm and aeration = 0.30–0.4 vvm. The multistage Monte Carlo optimization method [12] was used to solve the regression Eqs. (3a) and (3b). The optimal experimental conditions for maximum biomass production in coded unit are x1 = 0.1, x2 = 0.1857, x3 = 1.94, x4 = 1.83 and those for maximum endospore formation are x1 = 0.1533, x2 = 0.6286, x3 = 0.68, x4 = 0.567. The natural values obtained by putting the respective values of xi in Eq. (3a) are pH = 6.65, temperature = 38.3 8C, agitation = 247 rpm and aeration = 1.05 vvm for maximum biomass production and in Eq. (3b) are pH = 6.27, temperature = 41.4 8C, agitation = 116 rpm and aeration = 0.33 vvm for the maximum sporulation. The model predicts a maximum biomass concentration (Yˆ ) of 4.3 g l1 and a maximum spore count of 9.4 1011 spores per gram under the above optimum environmental conditions. The experimental results at the optimized values of the test variables as per the model showed a biomass yield of 4.3 g l1, which is about 39% higher than the previously
Fig. 6. Dynamics of biomass growth and endospore formation under optimum environmental conditions.
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reported result and a maximum spore yield of 9 1011 spores per gram of dry biomass. These data are in close agreement with the model predictions. The dynamics of biomass growth and sporulation can be easily understood from the time-course profiles of the culture pH, cellular growth and endospore formation (Fig. 6).
4. Conclusions Response surface methodology was employed for modeling, optimization and analysis of a fermentation process to enhance the yields of biomass and spores of a probiotic culture. This research endeavor is a part of the process development exercise for the lab-scale manufacture of nutraceutical formulations, which help in maintaining the normal balance of the intestinal micro-flora in antibiotic therapy-associated gastro-intestinal disorders. The optimal process conditions are different for biomass and endospore production. Hence a two-stage strategy with biomass production in the first stage in the optimal environment, which stimulates more growth followed by the second stage involving the production of spores under the optimum operating conditions conducive for sporulation. By adopting such strategy and by employing the optimum experimental conditions, an enhancement in biomass production by about 55% was achieved with an improved spore yield of 9 1011 spores per gram of dry biomass.
Acknowledgement The authors thank the R&D management of the Cadila Pharma Ltd., Ahmedabad, India, for its kind permission to use the Fermentation Laboratory and Pilot Plant facilities for the experimental work. The first author also gratefully acknowledges the financial support as a minor project, received from the University Grants Commission, India
through his institute BITS, Pilani, for the theoretical modeling part of the present study.
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