NC State
Al-Shorgani, N. K., Hamid, A. A., Wan Yusoff, W. M., and Kalil, M. S. (2013). "Pre-optimization of medium for biobutanol production by a new isolate of solvent-producing Clostridium," BioRes. 8(1), 1420-1430.


A Plackett-Burman design was used to pre-optimize the medium composition for biobutanol production using a unique isolate of solvent-producing Clostridium YM1. Various nutrient factors affecting biobutanol production were screened using the Plackett-Burman design. These factors included: glucose, tryptone, yeast extract, peptone, ammonium acetate, KH2PO4, K2HPO4, MgSO4, FeSO4, Na2CO3, and NaCl. The results were analyzed by an analysis of variance (ANOVA), which showed that glucose, tryptone, yeast extract, peptone, K2HPO4, Na2CO3, and MgSO4 had significant effects on biobutanol production. However, ammonium acetate, KH2PO4, and FeSO4 had insignificant effects. The established model from the ANOVA analysis had a significant value of Pmodel > F = 0.0245 and an R2 value of 0.999. The estimated maximum biobutanol production was 9.01 g/L, whereas the optimized medium produced 10.93 g/L of biobutanol.

Download PDF

Full Article

Pre-optimization of Medium for Biobutanol Production by a New Isolate of Solvent-producing Clostridium

Najeeb Kaid Al-Shorgani,a,b Aidil Abdul Hamid,a,* Wan Mohtar Wan Yusoff,a and Mohd Sahaid Kalilb

A Plackett-Burman design was used to pre-optimize the medium composition for biobutanol production using a unique isolate of solvent-producing Clostridium YM1. Various nutrient factors affecting biobutanol production were screened using the Plackett-Burman design. These factors included: glucose, tryptone, yeast extract, peptone, ammonium acetate, KH2PO4, K2HPO4, MgSO4, FeSO4, Na2CO3, and NaCl. The results were analyzed by an analysis of variance (ANOVA), which showed that glucose, tryptone, yeast extract, peptone, K2HPO4, Na2CO3, and MgSOhad significant effects on biobutanol production. However, ammonium acetate, KH2PO4, and FeSOhad insignificant effects. The established model from the ANOVA analysis had a significant value of Pmodel>F = 0.0245 and an Rvalue of 0.999. The estimated maximum biobutanol production was 9.01 g/L, whereas the optimized medium produced 10.93 g/L of biobutanol.

Keywords: Biobutanol production; Novel butanol-producing strain; Plackett-Burman design; Medium optimization

Contact information: a: School of Biosciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia; b: Department of Chemical and Process Engineering, Faculty of Engineering, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia; *Corresponding author:, Fax: +60389252698


Butanol is considered a promising renewable energy source and a future biofuel having potential to replace gasoline. Butanol has more advantageous fuel properties than ethanol, such as higher energy content, less sensitivity to temperature, less corrosivity, and the absence of any required modification in combustion engines (Jang et al. 2012; Lee et al. 2008). Butanol can be produced biologically through a well-known fermentation process called acetone-butanol-ethanol (ABE) fermentation using solvent-producing Clostridium strains. However, ABE fermentation has many shortcomings, including low production of butanol due to butanol toxicity, high cost of substrates (63%) (Jones and Woods 1986), and complications in recovery due to the presence of by-products such as ethanol, acetone, and acids. Hence, isolation and identification of new strains that produce larger amounts of biobutanol and optimization of culture conditions are vital. These can contribute to solving the problem of poor biobutanol production.

The Plackett-Burman design (PBD) is a two level factorial design that allows us to establish experiments with some number between these fractional factorial designs. It was used for the first time in 1946 (Plackett and Burman 1946).

PBD as statistical design is a linear model (Noguchi et al. 2012). It has been successfully used to pre-optimize alkaline protease production (Vaidya et al.2009), bio-ethanol production (Yingling et al. 2011), and phenolic compounds extraction (Anastácio and Carvalho 2013; Dopico-García et al. 2007).

PBD was used in this study because of the large number of nutritional factors (11 factors) to be investigated in terms of their effects on biobutanol production. This design can examine N factors in N+1 experiments.

The objective of this study was to optimize the production of biobutanol by screening the effect of nutrient factors using the Plackett-Burman design.


Isolation of Solvent-producing Clostridium

Submerged soil samples were collected from a system of rice intensification (SRI) paddy fields located in Ban 9, Parit 3, Sekinchan, Selangor, Malaysia. The soil samples were transferred immediately into 100 mL serum bottles containing 50 mL sterilized RCM medium, which was pre-sparged with nitrogen to create anaerobic conditions.

The cultures were incubated thereafter at 30°C, and the gas production was observed for 5 days. The gas-producing cultures were then used to inoculate RCM agar plates at 30°C under anaerobic conditions using a generation kit for 2 days. Single colonies were transferred to new RCM agar plates and also incubated at 30°C under anaerobic conditions. Next, Gram staining was carried out to study the cell shape and the reaction with Gram stain. Only Gram positive, rod-shaped cells and gas-producing cultures were taken for further investigations.

The ability of the cultures to produce solvents (ABE) was checked using an acetone test. In this test, 5% sodium nitroperoside solution and ammonium solution (40%) were used. Positive acetone production was indicated by a change in the color of the culture suspension from yellow to purple.

Media Preparation and Butanol Fermentation

To evaluate the ability of isolated strains to produce ABE (able to produce acetone, Gram positive, rod cell shape, and able to form spores), RCM was used as a medium, 30°C as an incubation temperature, 10% inoculum size, and under anaerobic conditions.

Among ABE producer strains isolated, YM1isolate showed the highest ABE production and was selected for ABE production using different media, including reinforced clostridial media (RCM), anaerobic sugar (AnS) medium, P2 medium, and tryptone yeast extract acetate medium (TYA).

RCM medium contained 30 g/L glucose, 10 g/L peptone, 10 g/L beef extract, 3 g/L yeast extract, 5 g/L sodium chloride, 0.5 g/L cysteine HCl, 3 g/L sodium acetate, and 0.5 g/L agar. TYA medium was also used to prepare the inoculum and it was used as a fermentation medium and consisted of the following: 30 g/L glucose, 0.5 g/L KH2PO4, 0.5 g/L K2HPO4, 0.4 g/L MgSO4.7H2O, 0.01 g/L MnSO4.4H2O, 0.01 g/L FeSO4.5H2O, 1.0 g/L yeast extract, and 0.5 g/L cysteine. A final concentration of 80 µg/L biotin and 1 mL of a solution containing 1 mg/L 4-aminobenzoic acid were added to 1 L of P2 medium. AnS medium consisted of the following components: 30 g/L glucose, 10 g/L peptone, 5 g/L yeast extract, 3 g/L K2HPO4, 1 g/L NaCl, 1 g/L (NH4)2SO4, 0.2 g/L MgCl2.6H2O, 0.2 g/L CaCl.2H2O, and 1 g/L Na2CO3.

ABE and Acids Analysis

The ABE and acids (acetic and butyric acids) concentration were measured using gas chromatography (7890A GC-System; Agilent Technologies, Palo Alto, CA, USA) equipped with a flame ionization detector (FID) and 30 m capillary column (Equity1; 30 m × 0.32 mm × 1.0 µm film thickness; Supelco Co, Bellefonate, PA, USA). The oven temperature was programmed to increase from 40 to 130°C at a rate of 8°C/min. The injector and detector temperatures were set at 250 and 280°C, respectively. Helium was the carrier gas and was set at a flow rate of 1.5 mL/min (Al-Shorgani et al. 2012).

Plackett-Burman Design (PBD)

PBD was used for screening the most significant fermentation parameters affecting biobutanol production by solvent-producing Clostridium isolated from system of rice intensification (SRI) soil. Each independent variable was investigated at two levels, high and low, which are indicated by +1 and -1, respectively. The details of the PBD experimental design are shown in Table 1. The variables with a P value less than 5% were considered to have a significant effect on biobutanol production. The PBD was created using Design-Expert version 6.0.8 software (State-Ease Inc., USA). The design involved 11 factors, namely: glucose, tryptone, yeast extract, peptone, ammonium acetate, KH2PO4, K2HPO4, MgSO4, FeSO4, Na2CO3, and NaCl. The aforementioned factors were coded as X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, and X11, respectively.

Table 1. The Level of Variables Affecting Biobutanol Production by YM1IsolateUsed in the Plackett-Burman Design

PBD with two-level design factors for testing n factors (n= number of runs) in k = n+1 (k= main effects) were used. The higher level value was coded as +1, and the lower level was coded -1, as shown in Table 1. Twelve runs of the PBD were done, as illustrated in Table 3.

Calculation of the effect of individual factors on biobutanol production was based on the first order equation as follows,

E = β0 + ΣβiXi (1)

where E is the effect of the factor under study (biobutanol production), β0 and βare the constant coefficients, and Xi is the coded independent variables or parameters. The response was analyzed by an analysis of variance (ANOVA) to obtain the significance of the fitted model and the significance of the effect of the individual factors on the response (biobutanol production).


Effect of Different Media on Butanol Production by Isolate YM1

In a preliminary study, experiments were done to produce butanol using different media (RCM, AnS, P2, and TYA). Out of these four media, growth was found to be faster and more extensive in RCM (data not shown), while TYA was the best medium for butanol production (Table 2). Therefore, a specific medium to optimize the biobutanol production by the new isolate of Clostridium (YM1 isolate) was designed.

Table 2. Biobutanol Production Using Different Media by Isolate YM1

Evaluation of Parameters Affecting Biobutanol Production

Screening is a very important step, especially when the researcher has many parameters and is unsure what levels are likely to produce optimal or nearly optimal responses. The selection of the levels of the parameters is a difficult part of the experi-mental design; experience and literature can help in choosing these factors (Strobel and Sullivan 1999).

The effects of the eleven medium nutrients, namely, glucose, tryptone, yeast extract, peptone, ammonium acetate, KH2PO4, K2HPO4, MgSO4, FeSO4, Na2CO3, and NaCl on biobutanol production in batch culture of newly isolated Clostridium (YM1) were tested by PBD. The effects of these nutrient components on the biobutanol production and significance levels are illustrated by Table 4.

Statistical analysis showed that the effects of glucose, yeast extract, peptone, tryptone, K2HPO4, MgSO4, Na2CO3, and NaCl had significant effects on biobutanol production. However, ammonium acetate, KH2PO4, and FeSO4 were insignificant factors and had no effect on the production of biobutanol (Table 4).

Table 3. Plackett-Burman Experimental Design for Evaluation of Parameters Affecting Biobutanol Production and the Response Values (Experimental and Predicted)

Table 4. ANOVA Analysis for Selected Factorial Model

R2 = 0.999, R= 0.999, Std. Dev. = 0.08

The most significant nutrients affecting biobutanol production were glucose (P = 0.0115), yeast extract (P = 0.0135), Na2CO3 (P = 0.0230), MgSO4 (P = 0.0258), K2HPO4 (P = 0.0261), tryptone (P = 0.0280), NaCl (P = 0.0284), and peptone (P = 0.0292). The model interaction had a low probability value (Pmodel > F = 0.0245) and F-value of 1004.96, which indicated that the model equation is reliable in its interpretation of the system interactions. The estimated correlation measures for the model regression equation are the multiple correlation coefficients R and R2. The R2 value was found to be 0.999, which indicated that the model could explain 99.9% of the variables content that contributed positively to the response, and only less than 0.1% of the total variations were not clarified by the model. Meanwhile, the R value was closer to 1 (0.9989), which represented good correlation between the experimental and predicted values.

The regression model is considered to have a very strong correlation when the R2 value is greater than 0.9 (Chen et al. 2009). Hence, this model showed fit to the variation and the R2 value represented a very good fit between the observed and predicted values of biobutanol production (Table 3). Meanwhile, the experimental results indicated the obtained values were very close to the predicted values.

The model equation for the individual parameters’ interaction (as a first order equation) can be shown as follows:

Butanol = 3.66 + 0.09 × glucose – 0.18 × tryptone – 0.74 × yeast extract +

0.21 × peptone + 0.17 × KH2PO4 + 1.28 × K2HPO4 +1.29 × MgSO4

+ 5.58 × FeSO4 – 0.33 × Na2CO3 – 1.36 × NaCl                                                                                (2)

The effect of variables on biobutanol production was presented by a Pareto plot (Fig. 1), which is arranged from the maximal effect in the upper portion to the minimal effect in the lower portion. The Pareto plot shows that the three most important nutrients affecting biobutanol production were glucose, yeast extract, and Na2CO3. In Table 5, the effect estimates and coefficient estimates of the variable interactions are listed.

Table 5. Coefficient, Effect Estimate, and Confidence Level of Variables Affecting Biobutanol Production by YM1 Isolate

a: not included in the model

The estimate of the effect of variables on biobutanol production, as shown in Fig. 2, from the greatest to least positive effect, were glucose, MgSO4, K2HPO4, peptone, FeSO4, and KH2PO4. Similarly, the factors with the greatest to the least negative effect on the production of biobutanol were yeast extract, Na2CO3, tryptone, NaCl, and ammonium acetate. Increasing the concentrations of the factors that had a positive effect and decreasing the concentrations of the factors that had a negative effect should lead to an increase in the production of biobutanol.

Fig. 1. Pareto plot of the Plackett-Burman design for parameter estimation of butanol production by YM1isolate

Supplementing the biobutanol fermentation medium with yeast extract is a common practice, as reported in the literature (Fontaine et al. 2002; Yan et al.1988;Yu et al. 2011). The most significant factor in this study affecting butanol production was the concentration of glucose (p = 0.0115), which was used as a carbon source. The presence of an excess concentration of glucose (60 g/L) in the fermentation medium has been reported as a typical concentration that is essential for the maintenance of ABE produc-tion (Jones and Woods 1986). Glucose had the highest confidence level, at 98.9%, followed by yeast extract, K2HPO4, and Na2CO3, which had a positive and extensive influence on butanol production. This phenomenon can be attributed to the requirements of butanol fermentation and its metabolic nature (Table 5).

Yeast extract was used as a nitrogen source for cell culture and fermentation processes (besides peptone, tryptone, and ammonium acetate), which is enriched with proteins, amino acids, minerals, vitamins, and growth factors that promote the growth of microorganisms (Tran et al. 2011). It was found that yeast extract has a strong effect on the production of biobutanol and sugar utilization during biobutanol fermentation from spoilage date fruits; the addition of yeast extract significantly increased the production of biobutanol (Abd-Alla and Elsadek El-Enany 2012). Chua et al. (2012) investigated the effect of yeast extract on biobutanol production using Clostridium G117 and found that increasing the yeast extract addition from 0.4% to 1% enhanced the production of butanol from 8.52 to 8.61 g/L. This study also found that using 0.1% yeast extract reduced the production of biobutanol (Chua et al.2012). Tryptone, peptone, or hydrolyzed casein have also been used as nitrogen sources in fermentation medium in different quantities in addition to yeast extract (Fontaine et al. 2002; Yan et al. 1988).

Fig. 2. Estimate of the effect of factors on biobutanol production by YM1 isolate

K2HPO4 is the source of phosphate in the medium and it has a buffering effect that maintains the pH during fermentation. In ABE fermentation, pH is decreased during the log phase (acidogenic phase) due to the production of acids (butyric and acetic acids) and then increased in the stationary phase (solventogenic phase) due to the reassimilation of acids to produce solvents. It is believed that pH is responsible for the initiation of the solventogenic enzymes (Nair et al. 1999). Also, it was reported that pH has a main effect on the production of biobutanol from sago starch (Salleh et al. 2008). Carbonate salt (Na2CO3) also has a buffering effect on pH. It was reported that carbonate salt has the ability to enhance the production of butanol and increase the Clostridium’s tolerance against the accumulation of solvent (Richmond et al. 2011).

Applying the optimized medium obtained from the PBD, which contained glucose (50 g/L), yeast extract (1.09 g/L), tryptone (3.01 g/L), ammonium acetate (4.06 g/L), K2HPO4 (0.99 g/L), MgSO4 (0.86 g/L), peptone (6.62 g/L), Na2CO3 (1.86 g/L), NaCl (0.1g/L), FeSO4 (0.001 g/L), and KH2PO4 (0.62 g/L), biobutanol production was 10.93 g/L with total ABE of 16.85 g/L, which was more than the predicted value by PBD. This indicated the strength of the model in this study as well as the potential value of this strain in biobutanol production.

It was reported that the wild-type of solvent-producing Clostridium strains were able to produce 9 to 12 g/L of butanol in a batch culture in the presence of 40 to 60 g/L of glucose in the medium (Chua et al. 2012; Formanek et al. 1997; Monot et al. 1982).

The results of this study suggested that higher glucose concentration, lower yeast extract concentration, lower tryptone concentration, and higher K2HPO4concentration are able to increase the production of biobutanol using a new isolate of Clostridium in batch fermentation. Moreover, the analysis exhibited that there is a probability of interaction among the significant variables that will affect the production of biobutanol. Hence, the interactions among these significant factors will be considered further in medium optimization using response surface methodology (RSM) in order to improve biobutanol fermentation by the new isolate of Clostridium.


  1. The pre-optimization of medium composition for biobutanol production in batch culture by a novel isolate of a solvent-producing strain was successfully conducted through screening of significant nutrient factors using the PBD design.
  2. Among the 11 factors tested, glucose, tryptone, yeast extract, peptone, K2HPO4, Na2CO3, and MgSOwere found to be significant parameters affecting biobutanol production.


This study was funded by Universiti Kebangsaan Malaysia under grant UKM/DLP 2012-007.


Abd-Alla, M. H.,and Elsadek El-Enany, A.-W. (2012). “Production of acetone-butanol-ethanol from spoilage date palm (Phoenix dactylifera L.) fruits by mixed culture of Clostridium acetobutylicum and Bacillus subtilis,”Biomass and Bioenergy 42, 172-178.

Al-Shorgani, N., Ali, E., Kalil, M.,and Yusoff, W. (2012). “Bioconversion of butyric acid to butanol by Clostridium saccharoperbutylacetonicum N1-4 (ATCC 13564) in a limited nutrient medium,”BioEnergy Research 5(2), 287-293.

Anastácio, A. and Carvalho, I. S. (2013). “Phenolics extraction from sweet potato peels: Key factors screening through a Placket–Burman design,” Industrial Crops and Products 43, 99-105.

Chen, X.-C., Bai, J.-X., Cao, J.-M., Li, Z.-J., Xiong, J., Zhang, L., Hong, Y.,and Ying, H.-J. (2009). “Medium optimization for the production of cyclic adenosine 3′,5′-monophosphate by Microbacterium sp. no. 205 using response surface methodology,”Bioresource Technology 100(2), 919-924.

Chua, T. K., Liang, D.-W., Qi, C., Yang, K.-L., and He, J. (2012). “Characterization of a butanol–acetone-producing Clostridium strain and identification of its solventogenic genes,” Bioresource Technology (

Dopico-García, M. S., Valentão, P., Guerra, L., Andrade, P. B., and Seabra, R. M. (2007). “Experimental design for extraction and quantification of phenolic compounds and organic acids in white “Vinho Verde” grapes,” Analytica Chimica Acta 583(1), 15-22.

Fontaine, L., Meynial-Salles, I., Girbal, L., Yang, X., Croux, C.,and Soucaille, P. (2002). “Molecular characterization and transcriptional analysis of adhE2, the gene encoding the NADH-dependent aldehyde/alcohol dehydrogenase responsible for butanol production in alcohologenic cultures of Clostridium acetobutylicum ATCC 824,”Journal of Bacteriology 184(3), 821-30.

Formanek, J., Mackie, R., and Blaschek, H. P. (1997). “Enhanced butanol production by Clostridium beijerinckii BA101 grown in semidefined P2 medium containing 6 percent maltodextrin or glucose,” Applied and Environmental Microbiology 63(6), 2306-2310.

Jang, Y. S., Lee, J., Malaviya, A., Seung do, Y., Cho, J. H.,and Lee, S. Y. (2012). “Butanol production from renewable biomass: Rediscovery of metabolic pathways and metabolic engineering,”Biotechnology Journal 7(2), 186-98.

Jones, D. T.,and Woods, D. R. (1986). “Acetone-butanol fermentation revisited,”Microbiological Reviews 50(4), 484-524.

Lee, S. Y., Park, J. H., Jang, S. H., Nielsen, L. K., Kim, J., and Jung, K. S. (2008). “Fermentative butanol production by clostridia,” Biotechnology and Bioengineering 101(2), 209-228.

Monot, F., Martin, J. R., Petitdemange, H., and Gay, R. (1982). “Acetone and butanol production by Clostridium acetobutylicum in a synthetic medium,” Appl. Environ. Microbiol. 44(6), 1318-1324.

Nair, R. V., Green, E. M., Watson, D. E., Bennett, G. N., and Papoutsakis, E. T. (1999). “Regulation of the sol locus genes for butanol and acetone formation in Clostridium acetobutylicum ATCC 824 by a putative transcriptional repressor,” Journal of Bacteriology 181(1), 319-330.

Noguchi, H., Ojima, Y., and Yasui, S. (2012). “A practical variable selection for linear models,” In: Lenz, H.-J., Schmid, W. and Wilrich, P.-T. (eds.). Frontiers in Statistical Quality Control 10, 349-360 Physica-Verlag HD.

Plackett, R. L., and Burman, J. P. (1946). “The design of optimum multifactorial experiments,”Biometrika 33(4), 305-325.

Richmond, C., Han, B., and Ezeji, T. C. (2011). “Stimulatory effects of calcium carbonate on butanol production by solventogenicClostridiumspecies,”Continental J. Microbiology 5(1), 18-28.

Salleh, M., Tsuey, L., and Ariff, A. (2008). “The profile of enzymes relevant to solvent production during direct fermentation of sago starch by Clostridium saccharobutylicum P262 utilizing different pH control strategies,”Biotechnology and Bioprocess Engineering 13(1), 33-39.

Strobel, R. J.,and Sullivan, G. R. (1999). “Manual of industrial microbiology and biotechnology,”Experimental Design for Improvement of Fermentations, A. L. DemainandG. E. Davies (eds.),ASM Press, UK.

Tran, H. T. M., Cheirsilp, B., Umsakul, K., and Bourtoom, T. (2011). “Response surface optimisation for acetone-butanol-ethanol production from cassava starch by co-culture of Clostridium butylicum and Bacillus subtilis,”International Journal of Science and Technology 5(3), 374-389.

Vaidya, B., Mutalik, S., Joshi, R., Nene, S., and Kulkarni, B. (2009). “Enhanced production of amidase from Rhodococcus erythropolis MTCC 1526 by medium optimisation using a statistical experimental design,” Journal of Industrial Microbiology & Biotechnology 36(5), 671-678.

Yan, R.-T., Zhu, C.-X., Golemboski, C.,and Chen, J.-S. (1988). “Expression of solvent-forming enzymes and onset of solvent production in batch cultures of Clostridium beijerinckii (Clostridium butylicum),”Applied and Enviromental Microbiology 54(3), 642-648.

Yingling, B., Zongcheng, Y., Honglin, W., and Li, C. (2011). “Optimization of bioethanol production during simultaneous saccharification and fermentation in very high-gravity cassava mash,” Antonie van Leeuwenhoek 99(2), 329-339.

Yu, M., Zhang, Y., Tang, I. C.,and Yang, S.-T. (2011). “Metabolic engineering of Clostridium tyrobutyricum for n-butanol production,”Metabolic Engineering 13(4), 373-382.

Article submitted: November 13, 2012; Peer review completed: January 11, 2013; Revised version received and accepted: January 27, 2013; Published: January 30, 2013.