Modeling of Pretreatment Condition of Extrusion-Pretreated Prairie Cordgrass and Corn Stover with...

Modeling of Pretreatment Condition of Extrusion-PretreatedPrairie Cordgrass and Corn Stover with Poly (Oxyethylen)20Sorbitan Monolaurate

Anahita Dehkhoda Eckard &

Kasiviswanathan Muthukumarappan & William Gibbons

Received: 3 February 2012 /Accepted: 16 April 2012 /Published online: 3 May 2012# Springer Science+Business Media, LLC 2012

Abstract Extrusion processing has shown potential to be used as a pretreatment method forsecond-generation bioethanol production. Furthermore, surfactants have been shown toreduce enzyme deactivation and increase the efficiency of hydrolysis. Therefore, a sequen-tial pretreatment technique was developed for corn stover (CS) and prairie cordgrass (PCG)in which a single screw extruder was used for the first pretreatment according to a previouslyoptimized condition using 70–180 °C for feed, barrel, and die zones with 65–155 rpm screwspeed. The second pretreatment was optimized in this study at 45–55 °C, 1–4 h, 0.15–0.6 gTween 20/g glucan according to response surface methodology. Optimization of surfactantpretreatment facilitated the estimation of interaction and higher-order effects for majorfactors involved in surfactant treatment (temperature, time, surfactant loading). Using8.6 FPU/g glucan cellulase, the optimum conditions found by fitting appropriate quadraticmodels to the data increased glucose and xylose yield by 27.5 and 33 % for CS and by 21.5and 27 % for PCG, respectively. Tween 20 concentrations and pretreatment temperaturewere the most significant factors affecting sugar yield (p value <0.05). Studies of SDSconcentration at and beyond critical micelle concentration (5.2–100 mM) demonstrated adecrease in sugar yield compared to control.

Keywords Enzymatic hydrolysis . Lignocellulose . Pretreatment . SDS . Tween 20 .

Cellulase . Micelles

Introduction

The current raw materials for production of bioethanol are starch-based materials as well assugar cane and molasses [1]. However, extensive research on developing lignocellulosic-based

Appl Biochem Biotechnol (2012) 167:377–393DOI 10.1007/s12010-012-9698-4

A. D. Eckard (*) : K. MuthukumarappanDepartment of Agricultural and Biosystems Engineering, South Dakota State University,1400 North Campus Drive, Brookings, SD 57007, USAe-mail: [email protected]

W. GibbonsDepartment of Biology and Microbiology, South Dakota State University, 1400 North Campus Drive,Brookings, SD 57007, USA

bioethanol production is underway. The latest estimate by Huber and Dale [2] showed that USAhas the potential to replace 30 % of liquid transportation by corn stover (CS), wheat straw, corncobs, wood, and sugar bagasse by 2030. Moreover, prairie cordgrass (PCG) has a widedistribution throughout the Northeast and Midwest states that hold a great potential forbecoming one of the future alternative resources for lignocellulosic biofuel [3]. In enzymatichydrolysis route, the cellulytic enzymes that are necessary to catalyze the hydrolysis of celluloseand hemicellulose polymers can account for as much as 27–40% of the processing expense in acellulosic ethanol industry [4, 5]. Extensive deactivation of enzymes has previously beenreported in many applications [6, 7]. Therefore, active research on methods that can reducethe enzyme consumption or retain the activity of the enzymes during hydrolysis is a worthwhilepursuit.

Different techniques for increasing or retaining enzyme activity have been suggested.Washing off the substrate with DI water or sodium chloride solution for release of proteins[8], immobilization of enzymes on solid surfaces, or their entrapment [9, 10] are amongthese techniques. Most of these techniques are either complicated or not quite cost-effective.However, the application of surfactants has been among the successful and economicalattempts for partially retaining the enzyme activity and increasing the efficiency of theprocess. Several hypotheses have been suggested for the positive effect of surfactants. Forinstance, it was reported that the non-productive adsorption of enzymes is reduced [11–13]or the nature of the substrate was modified with surfactants by removing the lignin orincreasing the surface area of cellulose [11, 14, 15]; however, this hypothesis was rejected byZheng et al. [16] and Errikson et al. [17]. Another positive role of surfactants was proposedto prevent the enzymes from thermal deactivation [14], which is contrary to that reported byBadley et al. [18] and Rees and Robinson [19] who in fact proposed that the enzyme–surfactant combination may lead to thermal deactivation of enzymes. It was also reportedthat the surfactants increased enzyme stability and reduced enzyme denaturation [20].

In particular, the positive effect of Poly (Oxyethylen)20, 80 sorbitan monolaurate (Tween 20,Tween 80) has been shown on enzymatic hydrolysis of sigma cell 100, newspapers, steam-exploded poplar [11, 12], Avicel [21], lime-pretreated corn stover [22], and steam-pretreatedspruce [17]. Sodium dodecyl sulfate (SDS) is most prevalently known as a protein unfoldingagent due to its application in SDS-PAGE method. However, SDS has been reported to affectthe refolding of protein [23] and decrease the loss of activity in enzymes [24]. Assistance inenzyme solubilization and reformation of protein secondary structure, specifically α-helixes atspecific concentrations beyond CMC, were also reported [25].

It has been shown that the effect of surfactants is dependent on the pretreatmentmethodology applied [21]. Although the impact of different surfactants were shown on purecellulose and lignocellulosic substrates treated with lime, steam explosion, AFEX, diluteacid, pH-adjusted, etc., to our knowledge, there have been no studies that evaluated theeffect of surfactants on extrusion-pretreated lignocellulosic substrate nor examined theimpact of surfactant pretreatment on the enzymatic hydrolysis of prairie cordgrass. Further-more, no studies have explored the possible interactions or higher-order effects of factorscontributing to the surfactant treatment conditions (i.e., time, temperature, concentration).Such information would help us to understand the relationship between variables and designan appropriate treatment condition.

Extrusion, a thermo-mechanical pretreatment technique which is utilized in the plastic andfood industries, carries advantages such as being simple, fast, continuous and effective. Lee etal. [26] and Karunanithy and Muthukumarappan [27, 28] reported sequential pretreatmenttechniques for Douglas fir, eucalyptus woods, and prairie cordgrass by using hot compressedwater and alkali treatment first followed by application of twin- or single screw extruder.

378 Appl Biochem Biotechnol (2012) 167:377–393

Although application of HCW, alkali, or ethylene glycol may enhance sugar recovery fromextrusion technique, these methods have disadvantages such as increase in water consumptionfor pH adjustment of treated biomass, potential sugar degradation at pH above 9 or below 2, andthe resulting cost for downstream processes.

Therefore, with the aim of increasing the sugar yield of lignocellulosic substrate fromextrusion pretreatment technique, it was intended to develop a sequential pretreatment methodthat does not carry the disadvantages of previous sequential methods. The objectives of thisstudy were to (1) assess the effect of Tween and SDS surfactant application on extrusion-pretreated corn stover and prairie cordgrass, (2) determine the optimal condition for surfactantpretreatment by exploring the response surfaces for glucose and xylose yields over a continuousrange of treatment time, temperature, and surfactant loading, and (3) evaluate the effectiveforms of variables, i.e., linear, quadratic, and interaction as a result of optimization. This studycomplements previous studies performed on the surfactant treatment of lignocellulosic biomassfor bioethanol production and aids in finding the optimum pretreatment condition.

Materials and Methods

Feedstock

CS and PCG obtained from a local farm in Brookings, SD, USA, was cut into 8-mm sizes usinga hammer mill screen (Speedy King, Winona Attrition Mill Co, MN, USA). The moisturecontent of the ground CS and PCG was measured according to NREL/TP-510-42621 protocoland thenwas adjusted to 20% (w.b.) by adding water and equilibrating overnight. This moisturebalanced CS and PCG were pretreated using a single-screw extrusion reactor as described in“Extrusion Pretreatment”. Microcrystalline cellulose (Avicel PH-101) was provided by FMCCorporation (Philadelphia, PA, USA) and was used as a source of pure cellulose for compar-ative purposes. Compositional analyses of CS and PCG are shown in Table 1.

Extrusion Pretreatment

A two-step pretreatment was developed for CS and PCG. The first step of pretreatment wasconducted according to previously optimized conditions developed by Karunanithy andMuthukumarappan [29] (Karunanithy and Muthukumarappan, manuscript submitted forpublication) using a single screw extruder. Both moisture-adjusted (20 % w.b.) CS andPCG were manually fed into a single screw extruder (Brabender Plasti-corder Extruder

Table 1 Composition of CS andPCG Components CS % DM PCG % DM

Glucose 34.6 33.1

Xylose 14.9 13.5

Galactose 1.93 –

Mannose 2.85 –

Arabinose 6.02 1.59

Lignin 20.2 21.9

Ash 4.43 5.60

Extractives 8.81 13.0

Appl Biochem Biotechnol (2012) 167:377–393 379

Model PL2000, Hackensack, NJ, USA) with barrel length to screw diameter ratio (L/D) of20:1 and a compression ratio of 3:1. Extrusion was performed for CS by controlling thetemperature of feed zone, transition zone, and die zone at 90, 180, and 180 °C, respectively.The screw speed was set at 155 rpm, providing a shearing effect against the ridged channelson both sides of the barrel. Using the same technique, treatment of PCG was conducted attemperatures of 70, 90, and 90 °C for the feed zone, the transition zone, and the die zone ofthe extruder, respectively. However, the screw speed was set to 65 rpm during PCGpretreatment. Extrudates were collected from the outlet of the extruder in dry form and keptfrozen at −19 °C until further study. No liquid was collected as a result of pretreatment andno washing or filtering was required for the pretreated biomass.

Preliminary Assessment of Surfactant Pretreatment

Non-ionic surfactant Poly (Oxyethylen)20 sorbitan monolaurate (Tween 20) and anionicsurfactant sodium dodecylsulfate (SDS) were used for treatment of extruded CS and PCG. Apreliminary assessment for the effect of these surfactants was conducted, where specificamount of extruded CS and PCG containing 1 % (w/v) cellulose (DM) was mixed with 15(ml) aqueous citrate buffers (pH ~5) and the total volume was adjusted to 30 (ml) with DIwater. Tween 20 and SDS were mixed with the rest of the solution at a concentration of0.15–0.6 and 0.15–2.8 g/g glucan, respectively, and incubated at 50 °C and 150 rpmfor 2.5 h in an air-bath shaker. After pretreatment with surfactants, 8.65 FPU/g glucanof Cellic™ Ctec with CBU/FPU of 6.5 was used along with 65.4 FXU/g glucan(2.06 mg protein/g glucan) Cellic™ Htec for enzymatic hydrolysis. The SDS concentrationsstudied in this work were above critical micelle concentration (CMC) level. The CMC ofSDS was reported to be 8 mM (2.33 g/l) in pure water [17]; however, this concentration wasreported to be 1.6 mM (0.46 g/l) in citrate buffer (pH ~5.4) at 25 °C [30]. Also, the CMCconcentration for Tween 20 in water was reported to be 0.06 mM. Therefore, all concen-trations of Tween 20 applied were above CMC (>0.07 g/l). Based on the results of thepreliminary assessment, Tween 20 was selected for optimization of pretreatment conditionsfor extruded CS and PCG.

Experimental Design for Surfactant Pretreatment Optimization

Response surface methodology (RSM) was utilized to optimize the pretreatment condition withthree independent variables. The independent variables selected for the experiment weretreatment time (X1) 1–4 h, temperature (X2) 45–55 °C, and surfactant loading (X3) 0.15–0.6 gTween/g glucan. Selection of the variables and levels were based on previous studies [16, 21,31–33], which suggested that non-ionic surfactants could improve hydrolysis efficiency.Glucose and xylose are the main sugar components of CS and PCG and hence they wereevaluated as the response variables of (Yg) and (Yx), respectively. RSM was selected for theexperimental design since it enables the evaluation of the response surface over a continuousdesign space, which considers first- and higher-order effects for the process variables as well astheir interaction. The basic theoretical and fundamental assumptions of this technique arediscussed and can be found elsewhere [34]. The RSM applied in this study was a 23 fullfactorial of CCRD design with six replications at the center, six axials points, and eight factorialpoints. Using the designed matrix in Table 3, aqueous buffer solutions of extruded substrates(prepared according to “Enzymatic Hydrolysis”) were incubated with different amounts ofTween for different amounts of time and at different temperatures followed by enzymatichydrolysis for 72 h. A total of 15 different conditions were used for optimization process.


Validation trials were conducted using three replications of selected optimum points that wereobtained from the prediction model.

Role of Lignin in Surfactant Pretreatment

After observing a significant positive effect of Tween on glucose yield from the hydrolysis ofCS and PCG, the effect of Tween 20 was evaluated on pure cellulose. Tween/g glucan, 0.3 g,was injected into a solution of Avicel comprised of 1%w/v of cellulose, 15ml citrate buffer (pH~5), and DI water (to adjust the total volume up to 30 ml). The solution was then incubated in ashaker set at 52 °C and 150 rpm for 2.5 h; after the temperature was dropped to 50 °C, cellulasewas applied to the reaction mixture. Control samples contained the same ingredients excludingTween 20. Enzymatic hydrolysis was followed according to “Enzymatic Hydrolysis”. Sampleswere collected after 72 h of incubation and analyzed for glucose and xylose yields.

Enzymatic Hydrolysis

Following surfactant treatment, samples were enzymatically hydrolyzed using new enzymecocktail Cellic™Ctec derived from Trichoderma reesei provided by Novozymes. In optimi-zation trials, Cellic™ Ctec with 141.2 mg protein/ml or 86.5 FPU/ml and 574.9 CBU/ml(containing main cellobiohydrolases (Cel6A and Cel7A), five endo-1,4- β-glucanases(Cel7B, Cel5A, Cel12A, Cel61A, and Cel45A), β-glucosidase, and a particular glycosidehydrolase family 61 [35]) was used along with Cellic™ Htec containing 34.40 mg protein/ml or 1,090 FXU/ml (xylanase activity). A preliminary trial was conducted to determine theoptimal combination of Ctec and Htec. Ctec was varied from 4.5 to 14.5 FPU and Htec wasevaluated at 20 or 50 % of Ctec volume. As a result of these trials, low enzyme dosages of14.12 mg protein/g glucan (8.65 FPU/g glucan with CBU/FPU of 6.5) Cellic™ Ctec alongwith 2.06 mg protein/g glucan (65.4 FXU/g glucan) Cellic™ Htec were used in optimizationtrials. Either 18.51 mg protein/g glucan or 10.83 FPU/g glucan of Cellic™Ctec2 (xylanaseactivity in addition to cellulase and β-glucosidase activity; containing 185.2 mg protein/ml;108.3 FPU/ml) was also used in the final trial due to its reported higher activity. Enzymatichydrolysis was carried out according to National Renewable Energy Laboratory proto-col (NREL-LAP 009), with the exception that the amount of all components wastripled. All experiments were conducted in 90-ml vials containing a total volume of30 ml, consisting of 15 ml of 0.05 M citrate buffer (pH ~5), a substrate concentrationof 1 % (w/v) cellulose (based on dried biomass), and DI water to adjust the totalvolume to 30 ml. Enzymatic hydrolysis was conducted at 50 °C for 72 h in an air-shaker bath set at 150 rpm. At the end of hydrolysis, enzymes were deactivated bysubmersion in boiling water for 15 min. An aliquot of enzyme-deactivated samples wascentrifuged twice for 15 min to clear the samples from micro-particles. The super-natants were then mixed with 0.7 g of resin, shaken vigorously, and allowed toprecipitate thoroughly. The supernatant from this step was taken for HPLC analysis.Samples were stored frozen at −19 °C until HPLC analysis.

Analytical Methods

Sugar concentrations were determined using high-performance liquid chromatography(Agilent Technologies, Santa Clara, CA, USA) equipped with an Aminex HPX-87 H column(Bio-Rad, Hercules, CA, USA) at 65 °C with a 0.01 N sulfuric acid mobile phase at a flowrate of 0.6 ml/min and an injection volume of 20 μl. Deionized water, 18 MΩ cm resistance


or higher, was used to make the mobile phase. A refractive index detector was used at 35 °Cto identify glucose and xylose.

Statistical Analysis

For the statistical analysis of the collected data, the relationship between two responses of Ygand Yx yields with independent variables of X1, X2, and X3 was defined using a polynomialmodel as follows:

Yi ¼ b0 þXk

i¼1bixi þ

Xk

i¼1bix

2i þ

Xk�1

i¼1

Xk

j¼iþ1bijxixj ð1Þ

where Yi is the predicted value, xi and xj are the coded values of the factors, β0 is a constantcoefficient, βi is the linear coefficient, βij are the quadratic coefficients, and βii are theinteraction coefficients. Model terms were evaluated based on stepwise regression analysisat alpha level of 0.05. Data analysis was performed using a statistical software package,Design-Expert (version 8.1.3, Stat-Ease, Minneapolis, MN, USA) to determine the regres-sion model and optimum points. For model selection and evaluation, sequential model sumof squares was used to select the highest-order polynomial model where the additional termswere not significant and the model was not aliased. Step-wise regression was then used toselect the first-order, second-order, and interaction terms that were significant in 95 %confidence interval. A lack-of-fit test was conducted and a p value greater than 0.05 wasconsidered as insignificant. The lack-of-fit and coefficient of determination were used toassess model fit to the data. Predicted R2 was assessed to ensure that it was no more than 0.2less than the adjusted R2. An adequate precision value of greater than 4 was used as acriterion to indicate an adequate signal-to-noise ratio. Moreover, the normal plot of residualswas evaluated to confirm that the assumptions for ANOVA were met. The coefficient ofvariance (CV) was calculated for both the glucose and xylose responses.

Results and Discussion

Preliminary Assessment of Surfactant Effect

As a result of the preliminary trials conducted to evaluate the optimum combination of Ctecand Htec, it was found that increasing the volume of Htec from 20 to 50 % of the Ctecincreased the glucose yield significantly (Table 2). When Htec was 50 % (w/w) of the Ctecincreasing the Ctec dosage from 4.5 FPU to 10.8 FPU increased the glucose yield signifi-cantly. However, further increase of Ctec up to 14.5 FPU did not add any additional glucoseyield from extrusion-pretreated CS with 3 % solid loading. We selected the lower dosage ofCtec (8.6 FPU) in order to ensure that the enzyme concentration would not mask the effect ofthe surfactant.

When 8.65 FPU/g glucan with CBU/FPU of 6.5 cellulase along with 65.4 FXU/g glucan(2.06 mg protein/g glucan) hemicellulase was used for enzymatic hydrolysis, extrusionpretreatment of CS and PCG resulted in an increase of 117 and 82.1 % in glucose yield,respectively, compared to untreated substrate (control). This represented an increase inglucose yield from 24 to 52.3 % for CS and from 28 to 51.0 % for PCG. The sugar yieldsfrom extrusion pretreatment were increased further by subsequent application of a surfactantpretreatment (according to “Preliminary Assessment of Surfactant Pretreatment”). Although


screening the surfactant’s hydrophilic–lipophilic balances (HLB) was reported to be impor-tant, while a higher HLB has generally shown better results [36], this general rule did notappear to be true for the selection of surfactants that are used for stabilization of enzymesapplied for hydrolysis of lignocellulosioc biomass. SDS does not follow this rule as it has ahigh HLB but causes a significant decrease in sugar yield [17, 25].

The results of this study showed that, when SDSwas used in concentrations below 17.3 mM(5 g/l or 0.5 g/g), a significant decrease of about 32.9 and 29.9 % in glucose yield was observedfor both CS and PCG, respectively (see Fig. 1a, c). Increasing the concentration of SDS up to45 mM (13 g/l or 1.3 g/g) failed to increase the glucose yield further for either CS or PCG butinstead resulted in a relative decrease of 44.2 and 20.7 %, respectively, when compared to thecontrol. This complements the findings of Xiang et al. [30] who reported that, at concentrationsbelow 45 mM of SDS (used with pure cellulose), deformation of the enzyme’s α-helical

Table 2 Glucose yield from thehydrolysis of extrusion-pretreatedcorn stover at different combina-tions of Cellic™Ctec and Cel-lic™Htec in a full factorial design

*Htec was used in 20 and 50 %w/w of the Ctec

Cellic Ctec (FPU) Cellic Ctec %(w/w glucan)

Y glucose (%)

*Htec (50 %) *Htec (20 %)

4.5 6 41.15 37.27

6.4 9 46.38 29.67

8.65 12 51.3 47.32

10.8 15 65.94 50.47

14.5 20 60.56 39.30

0

20

40

60

80

100

0 0.15 0.3 0.5 0.6 0.9 1.3 2.16 2.86

Glu

cose

yie

ld (

%)

SDS concentration (g/g glucan)

0

20

40

60

80

100

0 0.15 0.3 0.5 0.6 0.9 1.3 2.16 2.86

Glu

cose

yie

ld (

%)

SDS concentration (g/g glucan)

0

20

40

60

80

100

0 0.15 0.3 0.5 0.6

Glu

cose

yie

ld (

%)

Time (h)

0

20

40

60

80

100

0 0.15 0.3 0.5 0.6

Glu

cose

yie

ld (

%)

Time (h)

a b

c d

Fig. 1 Preliminary evaluation of surfactant concentrations on CS and PCG. a Effect of SDS concentration onextruded CS after 72 h of hydrolysis. b Effect of Tween 20 concentration on extruded CS after 72 h ofhydrolysis. c Effect of SDS concentration on extruded PCG after 72 h of hydrolysis. d Effect of Tween 20concentration on extruded PCG after 72 h of hydrolysis


structure and consequently decrease in hydrolytic activity were observed. Xiang et al. [30] alsoreported that SDS concentrations beyond 75 mM (21.6 g/l) were required to increase thecellulase activity by 1.3-fold, suggesting that high concentrations of SDS are necessary forcellulase activity to be recovered. However, based on our results, increasing the SDS concen-tration up to 75 and 100 mM did not demonstrate a significant increase in glucose yield foreither CS or PCG.

Contrary with the results for SDS, a significant increase in glucose yield was observedwhen 0.15–0.6 g Tween 20/g glucan was treated with CS and PCG for 2.5 h at 50 °C.Addition of 0.15 g Tween 20/g glucan increased the glucose yield of CS and PCG to asmuch as 21.8 and 11.2 %, respectively. Increasing the Tween concentration to 0.3 and 0.5(g/g glucan) resulted in a minor improvement in glucose yield for CS; however, theglucose yield was increased by 19.5 % in PCG when the Tween concentration increasedto 0.3 g/g glucan compared to control (Fig. 1b, d). Errikson et al. [17] reported a 65 %increase in glucose yield by application of 5 g/l of Tween on steam-exploded spruce.Another report comparing the effect of Tween 20 on the hydrolysis of CS pretreated withvarious methods showed that the application of Tween 20 was specifically effective withSO2− (63 % increase) and lime- pretreated (19 % increase) CS [21]. Based on thepreliminary results, Tween 20 was selected to be used for further optimization of surfactanttreatment conditions on CS and PCG.

Optimization of Pretreatment Condition for Extrusion-Pretreated Corn Stover with Tween 20

Responses obtained from 3-day enzymatic hydrolysis of extrusion surfactant-pretreated CS are shown in Table 3. The sequential sum of squares analysis indicatedthat the quadratic model was the best fit for both glucose and xylose yield as thecubic model did not explain any significant variance in the response variablesbeyond the quadratic model. The fitted quadratic models (in terms of the codedindependent variables) selected by step-wise regression are shown in Eqs. (2) and(3), where Yg and Yx represents glucose and xylose yield, respectively, as a functionof reaction time (X1), temperature (X2), and Tween 20 loading (X3). These quadraticmodels were highly significant (p<0.0001), and the ANOVA for both models arepresented in Table 4.

Yg ¼ þ65:5þ 1:75X2 þ 3:25X3 � 5:15X22 � 5:41X2

2 � 2:11X32 ð2Þ

Yx ¼ þ28:14þ 0:29X1 þ 0:34X2 þ 0:59X3 þ 0:46X2X3 � 0:42X12 � 0:79X2

2 � 1:03X32 ð3Þ

For both of the glucose and xylose yields, the models showed insignificant lack offit relative to pure error (see Table 4), indicating that they were appropriate formodeling the data. Also, both models resulted in a high coefficient of determination(R2) of 0.92 and 0.87 for glucose and xylose yields, respectively. Moreover, predictedR2 values for both glucose and xylose yields were in reasonable agreement withadjusted R2. The normal plot of residuals confirmed that no systematic errors existed(data not shown). An adequate precision of 18.17 and 10.03 was achieved for glucoseand xylose yields, respectively, that indicates a sufficient signal-to-noise ratio (S/N >4is desirable).

Among the variables assessed, the temperature at which the surfactant was incubated withextruded CS solution and the concentration of Tween 20 were the most important factors in


determining the final glucose yield (Table 5). These two variables in their linear and quadraticforms demonstrated a significant impact on glucose yield (see Table 5 for p values). However,varying the incubation time of extrusion-pretreated CS with surfactant between 1 and 4 h didnot change the final glucose yield significantly. This can also be observed in Fig. 2b, whichshows no color change associated with glucose yield across the time axis.

For xylose yield, quadratic and linear forms of Tween 20 concentration and incubationtemperature were the most significant terms of the model. Interestingly, an interaction relation-ship between pretreatment temperature and Tween concentration was observed (p value of0.05). Contrary to glucose yield, varying the incubation time during pretreatment did impact thexylose yield significantly. The linear and quadratic forms of time were shown to be significant,with p values of 0.10 and 0.02, respectively.

As a result of optimization, several conditions were found in which the glucose andxylose yields were predicted to be maximized. A few of these conditions were selected andvalidated. The optimum condition at which 0.47 g Tween/g glucan was incubated withextruded corn stover for 2.1 h at 51.4 °C was predicted to result in 66.7 and 28.1 % glucoseand xylose yields, respectively, after 72 h of enzymatic hydrolysis. After validation of thiscondition, 66.5 % glucose yield and 29.1 % xylose yield were achieved. These resultsshowed 27.5 and 33.0 % increase in glucose and xylose yields, respectively, compared toextruded-only control. The estimated response surfaces for glucose and xylose yields overindependent variables of X1, X2, X3 are shown in Fig. 2a–d.

Table 3 Experimental design in terms of actual and coded values and responses

Run Coded levels Actual levels of variables CS PCG

X1 X2 X3 Time(h)

Temperature(◦C)

Tween loading(g/g glucan)

Yg (%) Yx (%) Yg (%) Yx (%)

1 −1 −1 −1 1.0 45.0 0.15 53.0 25.6 47.6 23.0

2 1 −1 1 4.0 45.0 0.15 55.3 25.4 46.1 23.2

3 −1 1 −1 1.0 55.0 0.15 56.1 25.3 54.1 22.5

4 1 1 −1 4.0 55.0 0.15 58.9 26.0 55.4 24.9

5 −1 −1 1 1.0 45.0 0.60 59.3 24.8 47.7 23.0

6 1 −1 1 4.0 45.0 0.60 56.7 25.7 48.9 23.7

7 −1 1 1 1.0 55.0 0.60 61.3 26.8 56.0 26.5

8 1 1 1 4.0 55.0 0.60 64.3 27.6 57.8 26.1

9 −1.682 0 0 2.5 50.0 0.00 52.0 23.6 47.3 20.6

10 1.682 0 0 2.5 50.0 0.75 67.5 26.8 57.2 27.3

11 0 −1.682 0 2.5 41.6 0.38 48.9 25.7 40.8 20.1

12 0 1.682 0 2.5 58.4 0.38 53.4 26.0 54.7 24.8

13 0 0 −1.682 0.0 50.0 0.38 66.7 26.5 58.3 25.2

14 0 0 1.682 5.0 50.0 0.38 64.2 27.4 60.7 28.7

15 0 0 0 2.5 50.0 0.38 67.1 27.2 60.1 26.6

16 0 0 0 2.5 50.0 0.38 66.9 28.5 58.3 28.5

17 0 0 0 2.5 50.0 0.38 65.2 27.9 58.9 26.4

18 0 0 0 2.5 50.0 0.38 64.4 28.6 61.9 27.1

19 0 0 0 2.5 50.0 0.38 67.0 28.9 59.1 28.0

20 0 0 0 2.5 50.0 0.38 63.0 27.7 59.7 28.0


Effect of Tween Concentration on Sugar Yield from Extruded CS

Exploring the contour plot of temperature versus Tween concentration at an incubation time of2.5 h (plot not shown) suggested that at a fixed treatment temperature of 51 °C, a relativeincrease of 23.8 % (from 54.0 to 66.9 %) and 21.5 % (from 23.2 to 28.2 %) for glucose andxylose yield, respectively could be achieved by increasing the Tween concentration up to0.50 g/g glucan. When Kumar and Wyman [21] varied the Tween concentration between 0.3and 0.6 (g/g glucan) while keeping the other variables constant, a significant impact ofsurfactant on sugar yield was observed on AFEX, ARP, dilute acid, SO2, controlled pH,lime-pretreated CS. While, they reported a limited impact on sugar yield by increasing theTween concentration beyond 0.15 (g/g glucan), higher Tween 20 concentrations did result insignificant increases of sugar yield for lime and SO2-pretreated CS. Similar results were alsoreported by Kaar and Holtzaple [14] in which higher concentrations of Tween was moreeffective. Our results suggest that, at an optimum temperature of 51 °C, increasing the Tweenconcentration up to 0.50 can increase the glucose yield, although with diminishing returns.

We suggest that the increase of Tween concentration might be contributing to the increaseof yield by addition to the aggregation number of surfactants in micelles. According to amodel suggested by Viparelli et al., [37], the micelles act like micro carrier supports for theenzyme, which improve the catalytic reaction that occurs at the interface of the micelle-aqueous phase when compared to that of pure aqueous phase. At concentration below 0.1 M,the increase in surfactant concentration was reported to impact only the micelle numbers[38]. Plus by increasing the concentration of surfactants possibly the number of adsorbed

Table 4 ANOVA for glucose and xylose yield

Biomass Response Source Sum ofsquares

df Meansquares

F value P value CV, % R2/Adj R2/Pred R2

CS Glucose Regression 612.04 4 153 43.53 <0.001S 3.1 0.92/0.89/0.80

Residual 52.73 15 3.52

Lack of fit 38.47 10 3.85 1.35 0.39 NS

Pure error 14.26 5 2.85

Total 664.77 19

Xylose Regression 32.13 7 4.59 12.18 <0.001S 2.31 0.87/0.80/0.61

Residual 4.52 12 0.38

Lack of fit 2.35 7 0.34 0.77 0.63 NS

Pure error 2.18 5 0.44

Total 36.65 19

PCG Glucose Regression 643.65 4 160.9 70.85 <0.001S 2.76 0.94/0.93/0.85

Residual 34.07 15 2.72

Lack of fit 26.12 10 2.61 1.64 0.30 NS


Total 677.72 19

Xylose Regression 106.28 9 21.26 17.22 <0.001S 4.4 0.86/0.81/0.62

Residual 17.28 10 1.23

Lack of fit 13.56 5 1.51 2.02 0.22 NS


Total 123.56 19


surfactants to the hydrophobic surfaces will be increased that prevent from the irreversibleadsorption of proteins and enhances the enzyme solubilization and activity.

For xylose yield, Qi et al. [33] reported an increase in the conversion of xylose fromdilute acid-pretreated wheat straw (WS) as the concentration of Tween was incrementally

45.0 47.0

49.0 51.0

53.0 55.0

0.15 0.26

0.38 0.49

0.60

50

55

60

65

70

Glu

cose

yie

ld (

%)

Tween 20 (g/g glucan) Temperature (°C) 1.00 1.60

2.20 2.80

3.40 4.00

0.15 0.26

0.38 0.49

0.60

50

55

60

65

70

Glu

cose

yie

ld (

%)

Tween 20 (g/g glucan) Time (h)

0.15 0.26

0.38 0.49

0.601.00

1.60 2.20

2.80 3.40

4.00

25

26

27

28

29

Xyl

ose

yiel

d (%

)

Time (h) Tween 20 (g/g glucan) 45.0 47.0

49.0 51.0

53.0 55.0

0.15 0.26

0.38 0.49

0.60

25

26

27

28

29

Xyl

ose

yiel

d (%

)

Tween 20 (g/g glucan) Temperature (°C)

a b

c d

Fig. 2 Optimization of Tween-20 treatment conditions for CS prior to 72 h of enzymatic hydrolysis ofextruded corn stover. a Effect of temperature × Tween on glucose yield, b effect of time × Tween on glucoseyield, c effect of temperature × Tween on xylose yield, d effect of time × Tween on xylose yield

Table 5 Significant model terms with their probability and coefficients in models

Biomass Glucose Xylose

Factor Coefficient F value P value Factor Coefficient F value P value

CS Temperature 1.75 11.87 0.003 Additive 0.59 12.56 0.004

Additive 3.25 41.01 <0.001 Temperature 0.34 4.31 0.063

Temperature2 −5.15 109.93 <0.001 Time 0.29 2.99 0.109

Additive2 −2.11 18.47 <0.001 Time2 −0.42 6.64 0.024

Additive2 −1.03 40.30 <0.001

Temperature2 −0.79 23.75 <0.001

Temperature ×Tween

0.46 4.52 0.055

PCG Temperature 4.13 102.47 <0.001 Time 0.66 4.86 0.044

Additive 1.75 18.34 <0.001 Additive 1.24 16.96 0.001

Temperature2 −4.16 124.69 <0.001 Temperature 1.10 13.35 0.003

Additive2 −2.82 50.97 <0.001 Additive2 −1.25 18.37 <0.001

Temperature2 −1.77 36.83 <0.001


increased. However Christenson [39] reported an insignificant effect of increasing surfactantconcentration on xylose yield from acid-pretreated WS. Our results showed that at anoptimum pretreatment temperature of 51 °C, increasing the Tween concentration increasedthe xylose yield until the Tween concentration reached to 0.44 g/g, at which the furtherincreases of Tween concentration resulted in reduced xylose yield.

Effect of Treatment Temperature on Sugar Yield from Extruded CS

Effect of temperature was evaluated by exploring the surface of the 3-dimensional plot ofTween concentration versus temperature (at an incubation time of 2.5 h). When Tweenconcentration was fixed at 0.47 g/g glucan (optimum concentration), it was observed that anincrease in temperature from 45 to 51 °C resulted in a relative increase in glucose and xyloseyield by 10.0 % (from 59.6 to 65.6 %) and 5.2 % (from 26.8 to 28.2 %), respectively.However, further increasing the temperature above 51 °C did not result an additionalpositive effects on glucose and xylose yields. Tween is a water soluble polymer which,due to its inverse solubility character, its hydrophobicity increases as the temperatureincreases [35, 40]. This phenomenon might contribute to the positive effect by increasingthe number of surfactants adsorbing to the lignin (hydrophobic) portion of the biomass. Inagreement with our results, Borjesson et al. [31] reported that the adsorption of PEG 4000 tosteam-pretreated spruce (SPS) was increased by 18 % when the incubation temperatureincreased from 45 to 50 °C. This resulted in increased cellulose conversion.

Optimization of Pretreatment Condition for Extrusion-Pretreated Prairie Cordgrasswith Tween 20

Glucose and xylose yields obtained from the enzymatic hydrolysis of pretreated PCG samplesat different experimental condition are shown Table 1. Sequential sum of squares analysisindicated that a quadratic model was the best fit for both glucose and xylose yield, similar to theoptimization for CS. The fitted quadratic models (in terms of the coded independent variables)selected by step-wise regression are shown in Eqs. (4) and (5), where (Yg) and (Yx) representsglucose and xylose yield, respectively as a function of reaction time (X1), temperature (X2),Tween 20 loading (X3). Both models were highly significant (p values <0.0001), suggesting theimportant role of several surfactant treatment factors for increasing sugar yield from extrusion-pretreated PCG. The ANOVA for both models are presented in Table 4.

Yg ¼ 59:47þ 4:13X2þ1:75X3 � 4:41X22 � 2:82X3

2 ð4Þ

Yx ¼ 27:44þ 0:66X1þ1:10X2 þ 1:24X3 � 1:77X22 � 1:25X3

2 ð5Þ

Insignificant lack of fit for the glucose and xylose models was observed, with p values of0.30 and 0.22, respectively. 94 and 86 % (R2) of the total variation in glucose and xyloseyields, respectively, could be explained by the regression models. Furthermore the predictedR2 for both models were in reasonable agreement with the adjusted R2 (Table 4).

The linear and quadratic forms of Tween concentration and treatment temperature werehighly significant in optimization models of glucose and xylose yields, this suggest thatthese two variables have the most prominent impact on sugar yield from surfactant treat-ment. These results were in agreement with the results found from optimization of extrudedCS with Tween, shown in section “Optimization of Pretreatment Condition for Extrusion-Pretreated Corn Stover with Tween 20”. We believe that higher order terms should not be


neglected and can affect defining the ideal treatment condition. Similar to CS, varying theincubation time of Tween with extruded PCG between 1 and 4 h did not show anysignificant effect on glucose yield (see Table 5 for p values). However, a nearly significanteffect of time could be observed for xylose yield.

As a result of optimization, several conditions were found in which the glucose andxylose yields were predicted to be maximized. A few of these conditions were selected andvalidated by performing 3 replications for each condition. An optimum condition of 0.44 gTween/g glucan incubated with extruded PCG for 3.43 h at 52.3 °C was predicted to result in61.8 and 28.2 % glucose and xylose yield, respectively. Validation results from 3 replicationsshowed 64.0 % glucose and 29.0 % xylose yield, representing a 21.5 and 27.0 % increase inglucose and xylose yields, respectively compared to extruded-only control.

The predicted response surfaces for xylose and glucose yields over independent variablesX1, X2, and X3 are shown in Fig. 3a–d.

Effect of Tween Concentration on Sugar Yield from Extruded PCG

Examining the response surface for yield as a function of Tween concentration and temperature inFig. 2a–d suggested that, at a fixed incubation time of 2.5 h and a fixed optimum treatmenttemperature of 52 °C, increasing the Tween concentration up to 0.44 g/g glucan resulted in arelative increase of 21.8 % (from 49.8 to 60.7 %) and 26.4 % (from 21.9 to 27.7 %) in glucose andxylose yield (Compared to control), respectively. Further increasing the Tween concentration above0.44 g/g glucan resulted in a decrease in glucose and xylose yields. Similar to our study on extrudedCS (“Optimization of Pretreatment Condition for Extrusion-Pretreated Corn Stover with Tween20”) and to the results reported by Qi et al. [33] and Kumar andWyman [21], an increase in sugaryield could be observed when the Tween concentrations were increased up to a specific value.

1.00 1.60

2.20 2.80

3.40 4.00

0.15 0.26

0.38 0.49

0.60

45

50

55

60

65

Glu

cose

yie

ld (

%)

Tween 20 (g/g glucan) Time (h)

0.15 0.26

0.38 0.49

0.6045.0 47.0

49.0 51.0

53.0 55.0

45

50

55

60

65

Glu

cose

yie

ld (

%)

Temperature (°C ) Tween 20 (g/g glucan)

1.00 1.60

2.20 2.80

3.40 4.00

0.15 0.26

0.38 0.49

0.60

20

22

24

26

28

30

Xyl

ose

yiel

d (%

)

Tween 20 (g/g glucan) Time (h) 45.00 47.00

49.00 51.00

53.00 55.00

0.15 0.26

0.38 0.49

0.60

20

22

24

26

28

30

Xyl

ose

yiel

d (%

)

Tween 20 (g/g glucan) Temperature (°C )

a b

c d

Fig. 3 Optimization of Tween-20 treatment conditions for PCG prior to 72 h of enzymatic hydrolysis ofextruded corn stover: a effect of temperature × Tween on glucose yield, b effect of time × Tween on glucoseyield, c effect of temperature × Tween on xylose yield, d effect of time × Tween on xylose yield


Effect of Treatment Temperature on Sugar Yield from Extruded PCG

The effect of temperature was evaluated by exploring the surface of the 3-D plot of Tweenconcentration versus temperature, where the incubation time and Tween concentration wereheld constant at 0.44 (g/g glucan) and at 2.5 h, respectively. It was observed that, when thetemperature was increased from 45 to 52 °C, the glucose and xylose yield was increased by18.5 % (from 51.2 to 60.7 %) and 12.1 % (from 24.70 to 27.7 %), respectively. However,further increase of temperature beyond 52 °C did not result in additional positive effects forglucose and xylose yield. These results were also in agreement to what we found forextruded CS in “Effect of Tween Concentration on Sugar Yield from Extruded CS”.

Role of Lignin in Surfactant Pretreatment

The aim of this experiment was to determine whether Tween 20 can also have a positive effecton Avicel or its effectiveness was restricted to lignin-containing substrates. After 3-dayhydrolysis of 10 g/l Avicel pre-incubated with 0.3 g/g glucan of Tween 20 for 2.5 h at 52 °C,the glucose yield increased by ~12.7 % (from 71.2 to 80.2 %) compared to control and theresults from T-test analysis showed a significant (p<0.05) difference between treatments. Theresult found from this study was consistent with those of previous authors. Errikson et al. [17]showed that the glucose yield was increased moderately by application of Tween on Avicel.Zheng et al. [16] also showed a 3-% increase in glucose yield when Tween 20 was used withAvicel. Helle et al. [11] reported an enhancement of sevenfold in sugar yield when Tween 80was used with Sigma Cell 100. Moreover, Kumar and Wyman [21] showed 64 % increase inhydrolysis yield when Avicel was treated with 0.3 g Tween/g glucan for 4 h. Increased sugaryield of Avicel hydrolysis in the presence of Tween compared to control (No Tween) suggestthat not only a non-productive adsorption of enzyme to lignin but also an irreversible adsorptionto crystalline cellulose can decrease sugar yield [16].

Maximum Yield versus Economical Consideration

An evaluation of the contour plot of Tween concentration versus incubation temperature forCS (Fig. 4a) showed that, when the pretreatment time was kept constant at 2.5 h (the centerpoint), an optimum region with Tween concentration between 0.30 and 0.54 g/g glucan andincubation temperature between 49 and 53 °C was found to achieve the highest sugar yield.

Fig. 4 Contour plot of the glucose yield as a function of incubation temperature and Tween concentration at afixed optimum incubation time for corn stover (a); prairie cordgrass (b)


For PCG, at a fixed incubation time of 2.5 h, the contour plot of temperature versussurfactant concentration (Fig. 4b) demonstrated an optimum region for maximizing glucoseyield with Tween 20 concentration and incubation temperature varying between 0.26 and0.55 g/g glucan and 50 and 55 °C, respectively.

As can be observed in these figures, the optimum concentration of Tween obtained to achievea maximum yield (refer to “Optimization of Pretreatment Condition for Extrusion-PretreatedCorn Stover with Tween 20” and “Optimization of Pretreatment Condition for Extrusion-Pretreated Prairie Cordgrass with Tween 20”) is not necessarily the same as the economicalconcentration of Tween. As can be observed in Fig. 4a, at optimum temperature, increasing theTween concentration from 0.3 to 0.47 g/g glucan increases the glucose yield by 2 %. Also, inPCG, increasing the Tween concentration from 0.34 to 0.44 g/g glucan increases the glucoseyield by 1.8 %. So, the economical dosage of Tween 20 in an industrial scale should be selectedbased on the specific criteria that benefit the production process most.

Conclusion

A sequential pretreatment technique was developed for conversion of lignocellulosic biomassto digestible sugars for ethanol production. CS and PCG were pretreated with extrusiontechnique followed by pretreatment with non-ionic and anionic surfactants. Using responsesurface methodology, models were developed for the prediction of sugar yield from hydrolysisof lignocellulosic biomass pretreated with a surfactant. The optimum condition for pretreatmentof CS required incubation with 0.47 g Tween/g glucan for 2.1 h at 51.4 °C, while the optimumcondition for PCG required incubation with 0.44 g Tween/g glucan for 3.43 h at 52.3 °C. Theseconditions increased the glucose and xylose yields by 27.5 and 33.0 % for CS and by 21.5 and27.0 % for PCG, respectively. Of the variables studied for surfactant pretreatment, temperatureand Tween concentration played the most significant role in their linear and quadratic forms onboth xylose and glucose yield, while time of incubation was found to be a non-significant factorfor glucose yield (p<0.05). Increasing the Tween 20 concentration and temperature, up to aspecific value, increased both the glucose and xylose yield. The observed increase in sugar yielddue to increase of surfactant concentration and incubation temperature might be explained bythe increased aggregation of Tween micelles in solution or on the hydrophobic sites on thesurface of the biomass, which would enhance the catalytic reaction at the interface of themicelleor prohibit the irreversible adsorption of enzyme to non-productive sites [35, 36, 38].SDS was found to have an inhibiting impact on enzymatic hydrolysis. SDS applied atconcentrations above CMC, up to 100 mM, continued to show an adverse impact onenzymatic hydrolysis. This could be due to deformation of enzyme substructure,specially the α-helixes.

Acknowledgments Funding support was provided by Sun Grant project titled “Development of pretreatmentstrategies”. The authors would also like to thank Novozymes Inc. for providing the commercial enzymes. Wewould also thank Dr. Karunanithy who helped in the extrusion of corn stover and prairie cordgrass.

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