2.MI671-Ruth Chrisnasari Available online at http://jurnal.permi.or.id/index.php/mionline DOI: 10.5454/mi.7.1.2ISSN 1978-3477, eISSN 2087-8575 Vol 7, No 1, March 2013, p 9-16 *Corresponding author; Phone: , E-mail: +6231-2981399, Fax: +6231-2981278 ruth_c@staff.ubaya.ac.id Because of the recent increase in the gas price and interest in environmental issues, the demand of ethanol as substitute of gasoline has been rapidly increased (Bai et al. 2008). Bioethanol can be produced from many sources of biomass, thus access to raw material is virtually unlimited. The most common raw material is starch and sugar based material because it is easier to convert to ethanol than lignocellulose material (Dumitriu, 1998; Gray et al. 2006). The hydrolysis of starch to monomeric sugars can be achieved in many different ways, including chemical hydrolysis by acid or alkaline and enzymatic hydrolysis. Nowadays, enzymatic hydrolysis is the most widely selected because it gives higher specific product, requires mild reaction conditions, and does not generate side products that inhibit microorganisms. In the last decade, comparison and selection of hydrolysis and fermentation process to obtain higher ethanol yield has gained more interest (Söderström et al. 2005; Ollson et al. 2006; Ohgren et al. 2007; Tu et al. 2009; Drissen et al. 2009). There are two configurations for hydrolysis and fermentation processes, namely separate hydrolysis and fermentation (SHF) and simultaneous saccharification and fermentation (SSF). When enzymatic hydrolysis and fermentation are performed sequentially, it is referred to SHF. However, the two process steps are conducted simultaneously, it is referred to SSF. SSF results in a lower capital cost, reduced risk of contamination and higher ethanol yields than SHF (Wyman et al. 1992; Stenberg et al. 2000). However, when adding biomass solids directly to the bioreactor makes cell recirculation very difficult. In addition, SSF requires compatible fermentation and saccharification reaction conditions, including pH, temperature, and Two different process configurations, simultaneous saccharification and fermentation (SSF), and separate hydrolysis and fermentation (SHF), were compared for ethanol production from Shorgum bicolor grain. Optimization modeling for glucoamylase and Zymomonas mobilis concentration in both of SSF and SHF were carried out to obtain optimal concentration of ethanol production. The optimum condition was achieved using 0.021% (v/v) of glucoamylase, and 30.19% (v/v) of Z. mobilis for SHF. In contrast, the optimum condition for SSF was 0.021% (v/v) of glucoamylase and 17.51% (v/v) of Z. mobilis. The model predicted SHF processing to be superior. The superiority of SHF over SSF was confirmed experimentally, the result showed ethanol yield of -1 SHF was 134.80 g L and ethanol yield of SSF was 115.66 after 72 h incubation time. A high similarity was observed between the predicted and experimental results, demonstrating the accuracy of the model. Key words: ethanol, Shorgum bicolor, separate hydrolysis and fermentation, simultaneous saccharification and fermentation Dua konfigurasi proses yang berbeda, sakarifikasi dan fermentasi simultan (SSF) serta hidrolisis dan fermentasi terpisah (SHF) dibandingkan pada proses produksi etanol dari Shorgum bicolor. Optimasi pemodelan pada konsentrasi glukoamilase dan Zymomonas mobilis dengan konfigurasi SSF dan SHF dilakukan untuk mendapatkan konsentrasi etanol yang optimal. Kondisi optimum dicapai pada konsentrasi glukoamilase 0,021% (v/v) dan Z. mobilis 30,19% (v/v) pada kofigurasi SHF. Namun, kondisi optimum untuk SSF dicapai pada glukoamilase 0,021% (v/v) dan Z. mobilis 17,51% (v/v). Prediksi model menunjukkan konfigurasi SHF lebih unggul. Keunggulan SHF dibanding SSF telah dikonfirmasi secara eksperimental, hasilnya menunjukkan etanol -1 -1 hasil SHF adalah 134,80 g L , dan etanol hasil SSF adalah 115,66 g L setelah 72 jam waktu inkubasi. Kesamaan yang tinggi antara hasil prediksi dan eksperimental menunjukkan keakuratan model. Kata kunci: etanol, Shorgum bicolor, sakarifikasi dan fermentasi simultan, hidrolisis dan fermentasi terpisah -1 g L Optimization Modeling of Ethanol Production from Shorgum bicolor Grain: Comparison between Separate Hydrolysis Fermentation and Simultaneous Saccharification Fermentation RUTH CHRISNASARI*, DAMIATI HARTINI SUSETYO, ADRIAN PRATAMA SUGIANTO, AND TJANDRA PANTJAJANI Department of Biology, Faculty of Biotechnology, Universitas Surabaya, Jalan Raya Kalirungkut, Surabaya 60292, Indonesia optimum substrate concentration (Ballesteros et al. 2004). One problem associated with SSF are the different optimal conditions for saccharification and fermentation, this means that the conditions chosen for SSF are not optimal for both the microorganism and the enzymes. On the other hand, in the SHF process the optimal condition of enzymatic hydrolysis and fermentation can be performed (Gupta et al. 2009). However, SHF leads to high production of sugars, which is problematic if the enzyme is end product inhibited. SSF may thus exhibit lower inhibition of the enzymes because glucose released by enzyme is simultaneously fermented by the microorganism (Prashant et al. 1999). In the present study, Shorgum bicolor, a grain having high sugar accumulation, was used as raw material for ethanol production. S. bicolor has potential as a raw material for fuel-grade ethanol production due to its rapid growth rate and early maturity, greater water use efficiency, limited fertilizer requirement, high yield production, and wide adoptability (Reddy et al. 2005; Prasad et al. 2007; Antonopoulou et al. 2008). Although SSF and SHF have been investigated extensively, there are still no comparison SSF and SHF modeling to achieve the optimal operating conditions for ethanol production from S. bicolor. Optimization modeling using Response Surface Methodology (RSM) has been reported to be an efficient method for identifying the effect of individual variables and for seeking the optimum conditions for a multivariable system (Ambati and Ayyanna 2001; Ratnam et al. 2005; Bandaru et al. 2006). This method has superiority compare to other methods due to rapidity, limited number of required experiments, and accuracy for describing the optimal conditions. In this present study, SHF and SSF were compared for ethanol production from S. bicolor and optimized by RSM. The RSM approach was adopted to locate optimum level of glucoamylase and Zymomonas mobilis concentration for ethanol production, since these parameters play a key role in the enhancement of ethanol yield (Bandaru et al. 2006; Chrisnasari et al. 2011). MATERIALS AND METHODS Bacterial Strain. Z. mobilis ZM4 (NRRL B- 14234) obtained from ARS Culture Collection National Center for Agricultural Utilization Research, Peoria Il, USA, was used throughout the research. E n z y m e . T h e r m o s t a b l e a l p h a a m y l a s e (Liquozyme®SC DS, Novozymes) originated from 10 CHRISNASARI ET AL. Microbiol Indones Bacillus licheniformis and glucoamylase (Dextrozyme® GA, Novozymes) from Aspergillus niger, were employed for starch hydrolysis. The enzymatic activities of these enzymes as defined by Novozymes were 240 -1 KNU-S g and 270 AGU , respectively. Cultivation Medium and Conditions. Z. mobilis -1 was maintained on medium containing (g L ): glucose, 100; yeast extract, 10; KH PO , 1; (NH ) SO , 1; 2 4 4 2 4 MgSO .7H O, 0.5. Cells were cultured at 35 °C and pH 4 2 of 5.5 with 24 h incubation time. Raw Material Pretreatment. Fermentation medium was made by gelatinized S. bicolor flour (30% w/v) at 100 °C for 15 min. Gelatinized slurry maintained under temperature of 85 °C and pH of 5.5, then added -1 with 20 μl L of alpha amylase and incubated for 2 h during liquefaction process. The liquefied starch then cooled down and adjusted according to the next step condition. Separate Hydrolysis and Fermentation. Saccha- rification was done by keeping the liquefied shorgum starch at 55 °C and a pH of 4.3, followed by addition of glucoamylase at vary concentrations and incubated for 48 h. Saccharified starch was adjusted into pH of 5.5 and centrifuged at 4000 rpm for 15 min. Supernatant was taken for fermentation process. Optimization studies of ethanol production by Z. mobilis were carried out in 250 mL flasks and fermentation conditions were maintained at temperature of 30 °C and shake of 200 rpm for 72 h. The inoculums was derived from Z. mobilis cells that grown overnight which OD at 560 nm was maintained at 1.5 for both SHF and SSF configuration and for each replication. Simultaneous Saccharification and Fermentati- on. The saccharification and fermentation of liquefied starch was carried out simultaneously in one flask. The liquefied medium was added with vary concentration of glucoamylase and inoculated with vary concentration of Z. mobilis suspension, followed by incubation process which maintained at 37 °C and 200 rpm for 72 h. Analytical Methods. The concentration of reducing sugars was determined by dinitrosalicylic (DNS) colorimetric assay using glucose as sugar standard (Miller 1959). The concentration of starch was determined according to Hall et al. (2000). The amount of ethanol was measured by gas chromatography (Hewlett Packard, HP C1540A). Cell concentration was measured by spectroscopy at 560 nm (OD ).560 Experimental Design and Optimization. The aim of this study was to find the optimum levels of glucoamylase and inoculum concentrations for ethanol -1 g Volume 7, 2013 Microbiol Indones 11 production from S. bicolor. Central composite experimental design RSM (CCD, Box and Wilson, 1951) was used in the optimization of ethanol production. Glucoamylase concentration (X , % v/v) 1 and inoculum (X , % v/v) were chosen as independent 2 -1 variables and ethanol concentration (Yi, g L ) was used as output variable. For statistical calculations the variables Xi were coded as Xi according to Equation (1). Where, x is the dimensionless value of an i independent variable, Xi is the real value of an independent variable, xi; is the real value of the independent variable at the center point and ∆xj is step change. Table 1 shows Independent variable that used 2 in this experimental plan. A 2 -factorial CCD, with four axial points (α =√2) and five replications at the center points (n =5) leading to a total number of 13 0 experiments was employed in Table 1 and 2. The second degree polynomials (Equation (2)) were calculated with the statistical package (Stat-Ease Inc, Minneapolis, MN, USA) to estimate the response of the dependent variable: where Yi is the predicted response, X , X , are 1 2 independent variables, b , is the offset term, b , b , are 0 1 2 linear effects, b ,b are squared effects and b is 11 22 12 interaction terms. RESULTS The two factors that most influence the enzymatic starch hydrolysis and fermentative production of ethanol are glucoamylase concentration and inoculum concentration. RSM was applied to find the optimal condition for each of these. The vicinity of the optimum condition for ethanol production was estimated through steepest ascent step at the preliminary study by varying concentrations of glucoamylase and Z. mobilis inoculum in both SHF a n d S S F c o n f i g u r a t i o n . T h e g l u c o a m y l a s e concentration was varied from 0.005-0.04 % (v/v) and the inoculum was varied from 10-40 % (v/v). The three points closest to the optimum range were used as selected values for the RSM experiment. The preliminary results showed that each fermentation configuration (SHF and SSF) led to a different optimal range for glucoamylase and inoculum concentrations. Both of SHF and SSF showed optimal sugar concentration at 0.02% (v/v) of glucoamylase concentration (Fig 1A) therefore the optimum range of glucoamylase for SHF was 0.015-0.025 % (v/v). In other hand, there was insignificant difference of sugar concentration between 0.02 and 0,025 % (v/v) of glucoamylase concentration in SSF configuration, consequently the optimal range was expanded to 0.010-0.030 % (v/v). In contrast, the optimal inoculums range for SHF configuration was 25-35 % (v/v) and for SSF configuration was 10-25 % (v/v) (Fig 1B). These points then were used as the lower factorial point (-1 of coded factor), center point (0 of coded factor), and upper factorial point (1 of coded factor) in CCD matrix design as listed in Table 1. Using CCD, a total number of 13 experiments with different combinations of glucoamylase and inoculum were performed (Table 2). The response was taken at the maximum ethanol production which was observed at 72 h incubation time. The estimation model analysis was done using Sequential Model Sum of Squares, Lack of Fit Tests and Model Summary Statistics. The result showed that suggestion model was quadratic which means relationship between variables and response followed a quadratic function. The following second order polynomial equation was found to represent the ethanol production adequately for SHF and SSF configuration: Equation for SHF configuration: Equation for SSF configuration: The experimental data as shown at Table 2 were statistically analyzed using the Fischer's statistical test for analysis of variance (ANOVA) and the results are shown in Table 3. The ANOVA of the quadratic regression model indicated that the model for SHF and SSF were highly significant where the Prob >F-value of the model was less than 0.0500. The coefficient estimate and the corresponding Prob >F-values demonstrated that glucoamylase concentration had a significant effect on ethanol production in SHF configuration but not in SSF configuration. However, inoculum concentrations were observed to have an insignificant effect on ethanol production in both SSF and SHF configuration. The analysis also showed that there were insignificant interactions between glucoamylase and inoculums concentration for both Xi-xi ∆ Xj i = 1,2,3,....kXi = (1) 2 2 Yi=b +b X +b X +b X +b X +b X X +0 1 1 2 2 11 1 22 2 12 1 2 (2) y=-39.30774+6029.10590 X +7.54986 X +6.99500 1 2 2 2 X X -1.52150E+005 X -0.12739 X1 2 1 2 (3) y=-89.32138+9446.26618 X +11.83360 X +21.04800 1 2 2 2 X X -2.30690E+005 X -0.35069 X1 2 1 2 (4) 12 CHRISNASARI ET AL. Microbiol Indones SSF and SHF. In addition, there were significant effects of quadratic function for SSF and SHF configuration respectively. The coefficient determination of SHF configuration, 2 2 R is 0.9785 and for SSF configuration, R is 0.9278 which implies that most of the sample variation in the ethanol yield was attributed to the independent variables. Adequate precision measures the signal to noise ratio. Adequate precision ratio of 18.228 for SHF configuration and 9.837 for SSF configuration indicate an adequate signal, so that these models can be used to navigate the design space. The predicted optimum levels glucoamylase and inoculums were obtained by applying the regression analysis to the Equation (3) and (4). The predicted and experimental ethanol production at the optimum levels of glucoamylase and inoculums also determined by using Equation (3) and (4). Fig 2 represent the response Fig 1 The effect of glucoamylases concentration to the amount of sugar released and the effect of inoculum concentration to the amount of ethanol produced at 72 h incubation time in (A) SHF configuration, (B) SSF configuration. Table 1 Independent variable in the experimental plan Variable Actual value of each coded level -1.414 -1 0 1 1.414 SHF: - - 0.0129 22.9 0.015 25.0 0.020 30.0 0.025 35.0 0.0271 37.1 SSF: - Glucoamylase concentrations (% v/v), X 1 - Inoculum (% v/v), X 2 concentration 0.0059 6.9 0.010 10.0 0.020 17.5 0.030 25.0 0.0341 28.1 Glucoamylase concentrations (% v/v), X 1 0.04 30 0 0.01 0.02 0.03 0.04 Glucoamylase (% v/v) -1 R ed u ci n g s u g ar ( g L ) 240 220 200 180 160 140 120 100 240 220 200 180 160 140 120 100 -1 R ed u ci n g s u g ar ( g L ) 0 0.01 0.02 0.03 Glucoamylase (% v/v) 140 135 130 125 120 115 110 105 -1 E th an o l (g L ) 100 0 10 20 30 50 Inoculum (%) 40 140 135 130 125 120 115 110 105 -1 E th an o l (g L ) 100 0 5 10 15 25 Inoculum (%) 20 A B Inoculum concentration (% v/v), X2 Volume 7, 2013 Microbiol Indones 13 SHF SSF Glucoamylase (% v/v) Inoculum (% v/v) Actual ethanol -1 (g L ) Predicted -1 ethanol (g L ) Glucoamylase (% v/v) Inoculum (% v/v) Actual -1 (g L ) ethanol Predicted -1 ethanol (g L ) 0.015 25.0 129.38 128.65 0.010 10.0 67.82 67.44 0.025 25.0 130.31 129.83 0.030 10.0 77.51 76.03 0.015 35.0 128.73 128.76 0.010 25.0 58.37 63.99 0.025 35.0 130.36 130.64 0.030 25.0 74.38 78.89 0.020 30.0 135.86 136.46 0.020 17.5 116.65 114.38 0.020 30.0 135.77 136.46 0.020 17.5 100.58 114.38 0.020 30.0 136.60 136.46 0.020 17.5 120.78 114.38 0.020 30.0 137.38 136.46 0.020 17.5 109.66 114.38 0.020 30.0 136.69 136.46 0.020 17.5 124.24 114.38 0.0129 30.0 127.37 127.77 0.0059 17.5 62.80 59.94 0.0271 30.0 129.88 129.93 0.0341 17.5 77.83 76.55 0.020 22.9 128.99 129.76 0.020 6.9 72.97 75.14 0.020 37.1 130.73 130.42 0.020 28.1 81.03 74.72 Table 2 Experimental and the predicted value of ethanol yield Table 3 Analysis of variance (ANOVA) for the response surface quadratic model Source SHF SSF Sum of square df Mean square F-value p-value prob>F Sum of square df Mean square F-value p-value prob>F Model 157.04 31.41 <0.0001 5965.76 5 1193.15 17.98 0.0007 X1 0.0177 1 275.67 4.15 0.0809 X2 0.3799 1 0.18 0.00265 0.9604 X X1 2 0.6334 1 9.97 0.15 0.7099 2 X1 100.65 100.65 204.43 <0.0001 3702.11 1 3702.11 55.78 0.0001 2 X2 170.55 143.30 <0.0001 2707.05 1 2707.05 40.78 0.0004 Residual 7 66.37 Lack of fit 63.79 9.51 0.88 0.25 1.29 0.3916 3 36.18 0.41 0.7573 Pure error 4.68 0.43 0.12 70.55 3.45 1.70 1.75 4.68 0.43 0.12 0.49 0.57 0.44 275.67 0.18 9.97 464.56 108.53 356.03 4 89.01 Corrected Total 160.49 5 1 1 1 1 1 7 3 4 12 6430.32 12 Variables Optimum level -1 Optimum ethanol yield (g L ) Experimental Predicted SHF: - Glucoamylase concentrations (% v/v), X 1 - Inoculum concentration (% v/v), X2 0.021 30.19 134.80 136.50 SSF: - Glucoamylase concentrations (% v/v), X 1 - Inoculum concentration (% v/v), X 2 0.021 17.51 115.66 114.76 Table 4 The predicted and experimental ethanol yield at the optimum levels of glucoamylase and inoculums predicted. In other hand, the optimum condition for SSF configuration can be achieved at the glucoamylase concentration of 0.021% (v/v) and inoculum concentration of 17.51% (v/v) which gives the -1 maximum ethanol production of 114.76 g L . Either experimental or predicted ethanol production at the surface and contour plots for the optimization of inoculums vs. glucoamylase on ethanol production. The optimum condition of SHF configuration for highest ethanol production can be achieved at the glucoamylase concentration of 0.021% (v/v) and 30.19 % (v/v) of inoculums. At these optimum conditions -1 maximum ethanol production of 136.50 g L was Microbiol Indones14 CHRISNASARI ET AL. Fig 2 Response surface and contour plot of glucoamylase vs. inoculums on ethanol production: (A) SHF configuration, (B) SSF configuration. 35.00 128.5 130.725 132.95 135.175 137.4 E th an o l 0.015 0.018 0.020 0.022 0.025 25.00 27.50 30.00 32.50 A: Glucoamylase B: Inoculum A 25.00 58 74.75 91.5 108.25 125 E th an o l B A: Glucoamylase B: Inoculum 0.0100 0.0150 0.0200 0.0250 0.0300 10.00 13.75 17.50 21.25 25.00 production from palmyra jaggery using response surface method. World J Microbiol Biotechnol. 17(4): 331-335. doi:10.1023/A:1016613322396. Antonopoulou G, Gavala HN, Skiadas IV, Angelopoulos K, Lyberatos G. 2008. Biofuels generation from sweet sorghum: Fermentative hydrogen production and anaerobic digestion of the remaining biomass. Bioresour Technol. 99(1):110-119. doi:10.1016/j.biortech.2006.11.048. Anuradha R, Suresh AK, Venkatesh KV. 1999. Simultaneous saccharification and fermentation of starch to lactic acid. Bai FW, Anderson WA, Young MM. 2008. 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Separate hydrolysis and fermentation (SHF) of Prosopis juliflora, a woody substrate, for the production of cellulosic ethanol by Saccharomyces cerevisiae and Pichia stipitis-NCIM 3498. 100(3):1214-1220. doi:10.1016/j.biortech.2008.08.033. Hall MB, Ennings JP, Lewis JBA, Robertson JB. 2000. Evaluation of starch analysis methods for feed samples. J Sci Food Agric. 81(1):17-21. doi:10.1002/1097- 0010(20010101)81:1<17::AID-JSFA758>3.0.CO;2-B. optimum condition for SHF and SSF configuration were also determined (Table 4). The modeling result predicted SHF processing to be superior to SSF. The superiority of SHF over SSF was confirmed experimentally, the result showed ethanol yield of SHF -1 was 134.80 g L and ethanol yield of SSF was 115.66 g -1 L after 72 h incubation time. DISCUSSIONS In the present study, we demonstrated the production of high ethanol concentrations from S. bicolor using the SHF and SSF configurations. The results demonstrated that glucoamylase and inoculum concentrations influenced to the enzymatic starch hydrolysis and fermentative production of ethanol. Increasing glucoamylase concentration positively influenced ethanol production in SHF configuration, as the liquefied starch in sorghum gets converted to glucose in the presence of glucoamylase enzyme. The increasing of glucoamylase concentration has lead to increasing of rate conversion of liquefied starch to glucose which resulted increasing of glucose concentration in medium. As in SHF hydrolysis and fermentation are separate, the best reaction conditions for each can be independently selected (Gupta et al. 2009; Ohgren et al. 2007). However, for SSF one set of conditions has to be selected. This usually means selecting a lower than optimal temperature for the enzyme to accommodate the microorganism (Anuradha et al. 1999). This problem recommends the superiority of ethanol production of SHF over than SSF configuration. 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