Response surface optimization of enzymatic hydrolysis of Cistus ladanifer and Cytisus striatus for bioethanol production

Biochemical Engineering Journal 45 (2009) 192–200

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Response surface optimization of enzymatic hydrolysis of Cistus ladanifer and Cytisus striatus for bioethanol production Susana Ferreira a , Ana P. Duarte a , Maria H.L. Ribeiro b , João A. Queiroz a , Fernanda C. Domingues a,∗ a b

Unidade de Materiais Têxteis e Papeleiros, Universidade da Beira Interior, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal Institute for Medicines and Pharmaceutical Sciences (i-Med), Faculdade de Farmácia, Universidade de Lisboa, Av. Prof. Gama Pinto, 1649-003 Lisboa, Portugal

a r t i c l e

i n f o

Article history: Received 25 November 2008 Received in revised form 23 March 2009 Accepted 27 March 2009 Keywords: Enzymatic hydrolysis Enzyme Biomass Lignocellulose degradation Optimization Response surface methodology

a b s t r a c t Current ethanol production processes using crops such as sugar cane and corn are well established; however, utilization of a cheaper substrate such as lignocellulose could make bioethanol more competitive with fossil fuel, without the ethical concerns associated with the use of potential food resources. The sequential configuration employed to obtain cellulosic ethanol implies that the solid fraction of pretreated lignocellulosic material undergoes hydrolysis. In this work, the enzymatic hydrolysis of pretreated Cistus ladanifer and Cytisus striatus was studied following an experimental design as a statistical problem solving approach. Plackett–Burman design was used in order to select the most important variables from the simultaneous study on influence of operating and reactional conditions, and central composite design to optimize the process of enzymatic hydrolysis. The optimization of enzymatic hydrolysis using the response surface methodology allowed a study on the influence of the variables (pH, temperature, cellulases concentration, polymer (PEG) concentration and incubation time) and variability due to the type of substrate (C. ladanifer and C. striatus) used. From the obtained results it can be concluded that the enzymatic hydrolysis was clearly enhanced by temperature, cellulase concentration, and incubation time. Model validation showed a good agreement between experimental results and the predicted responses. © 2009 Elsevier B.V. All rights reserved.

1. Introduction During the last few decades, the excessive consumption of fossil fuels has lead to an increased demand for alternative sources of fuels [1]. These alternative sources may reside in the production of renewable energies, as ethanol. Currently, ethanol for fuel market is mainly produced from sugar or starch; however, the demand for this raw material, which is also used to animal feed and human needs, will not be enough to meet the need for fuel ethanol [2]. Cellulosic ethanol is one of the most promising technological approaches available to reduce emissions of greenhouse gases from the transportation sector [3]. Also, the lignocellulosic biomass is of great availability, low cost feedstock and does not have the ethical concerns associated with the use of potential food resources [4]. For this reason, it is imperative to develop the process of lignocellulosic biomass conversion to ethanol. Overall fuel ethanol production from lignocellulosic biomass includes five main steps: biomass pretreatment, cellulose hydrolysis, fermentation of hexoses, separation and effluent treatment [5].

∗ Corresponding author. Tel.: +351 275 319 827; fax: +351 275 319 730. E-mail address: [email protected] (F.C. Domingues). 1369-703X/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.bej.2009.03.012

Despite being possible to do the hydrolysis step using diluted or concentrated acids, the enzymatic hydrolysis is advantageous as it requires less energy and mild environmental conditions. Thus, the utility cost of the process is lower when compared to alkaline or acid hydrolysis [6]. Moreover, the enzymatic hydrolysis is substrate-specific without byproduct formation. The disadvantage of this process is related to the structural characteristics of lignocellulosic biomass, which require pretreatment to alter the structure and make cellulose more accessible to the enzymes that convert the polysaccharides into fermentable sugars, breaking the lignin seal and disrupting the crystalline structure of cellulose [7]. Enzymatic hydrolysis of cellulose is carried out by the cellulosehydrolyzing enzyme cellulases, a mixture of several enzymes that act hydrolyzing crystalline cellulose to its monomeric component, glucose [8]. There are three classes of enzymes acting synergistically in cellulose hydrolysis: endoglucanases, exoglucanases and ␤glucosidases. The endoglucanases attack the amorphous regions of the cellulose cleaving internal ␤-1,4-glucosidic bonds and providing chain ends for the action of exoglucanases. The exoglucanases act on the reducing and non-reducing ends of cellulose chains to release mainly soluble cellobiose. Finally, the ␤-glucosidases hydrolyze the soluble cellobiose to glucose. The interaction between hydrolytic enzymes and cellulosic substrates is complex, in part due to the significant number of possible

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Table 1 Chemical composition of plants used in enzymatic hydrolysis [26]. Species

Extractives [%]

Ash [%]

Insoluble lignin [%]

Soluble lignin [%]

Total lignin [%]

Total carbohydrates [%]a

Removed sugars [%]b

Cistus ladanifer (rock-rose) Cytisus striatus (broom)

7.4 4.7

3.1 0.8

32.0 22.4

2.2 2.3

34.2 24.7

55.3 69.8

30.2 (302 mg/g) 28.4 (284 mg/g)

a b

Calculated by difference from the other determined compounds. Removed sugars to liquid fraction during pretreatment.

interactions in the system involving a multi-enzyme complex that adheres to a multicomponent insoluble biomass substrate and acts catalytically upon it [3]. A parameter that has been an object of study is the unspecific and non-productive binding of cellulases by hydrophobic interactions with lignin. Several studies reported improvements in the enzymatic hydrolysis of lignocellulosic biomass with the supplementation with surfactants [9–13] polymers [14,15] and bovine serum albumin (BSA) [10,16]. Also, the addition of polymers can effectively increase enzymatic hydrolysis of lignocelluloses due to a higher availability of enzymes for cellulose degradation [14,15]. There are several factors that affect enzymatic hydrolysis of cellulose including substrates, cellulase activity, and reaction conditions (temperature, pH, etc.). To improve yield and rate of the enzymatic hydrolysis, research has focused on the optimization of the hydrolysis process and enhancement of cellulase activity [6]. The Portuguese forest occupies 3.4 million hectares, i.e. 38.4% of the territory, and can be seen as a source of large amounts of forestry biomass residues, including trees branches, pruning residues and shrubs, such as rock-rose (Cistus ladanifer) and broom (Cytisus striatus). These shrubs are present in native forest and in fallow land (2 million hectares), and are considered in many regions as invasive plants [17]. The application of these residues in bioprocesses is favorable because the environmental problem of their disposal may be resolved, since they must be harvested in order to keep the forests clean and less vulnerable to fires. Therefore, it is important to optimize the different steps in bioethanol production from these lignocellulosic materials, including hydrolysis of cellulose. Response surface methodology (RSM) is a statistical technique for the modelling and optimization of multiple variables, which determines optimum process conditions by combining experimental designs with interpolation by first- or second-order polynomial equations in a sequential testing procedure. This methodology has already been successfully applied for the optimization of enzymatic hydrolysis of several substrates including cellulose [18–23]. In this work, the enzymatic hydrolysis of rock-rose and broom was studied employing preliminary tests and experimental design as a statistical problem solving approach, as the Plackett–Burman method and a response surface methodology (RSM) of central composite rotatable design (CCRD).

cose per minute during hydrolysis reaction. One cellobiase unit (CBU) was defined as the amount of enzyme that converts 1 ␮mol of cellobiose per minute during hydrolysis reaction. Both enzyme preparations were from Novozymes (Denmark). 2.2. Substrates C. ladanifer (rock-rose) and C. striatus (broom) were used as substrates in this work. These forestry residues were collected and harvested by “Associac¸ão de Produtores Florestais do Paúl”. Feedstock (Table 1) for the pretreatment was milled to a particle size between 0.180 and 0.500 mm and then subjected to a pretreatment with diluted acid. The pretreatment was carried out with sulfuric acid at 2.6% (w/w) in a plant/liquid ratio of 2 g dried plant/10 mL liquid, during 75 min at 150 ◦ C. After this process, the raw material was washed to remove traces of inhibitors and the solid fraction was used in the enzymatic hydrolysis process [27]. Samples were not dried prior to enzyme digestibility to avoid pore collapse that can occur in the micro-structure of the biomass leading to decreased enzymatic release of glucose from the cellulose [28]. 2.3. Methods 2.3.1. Enzymatic hydrolysis Enzymatic hydrolysis experiments on solid fraction of diluted acid pretreated rock-rose and broom were performed in caped tubes, on a rotary shaker. The reaction mixture was done according to the experimental designs with supplementation of 40 ␮g/mL tetracycline and 30 ␮g/mL cycloheximide to prevent microbial contamination. The enzymatic hydrolysis was stopped by immediate chilling on ice and centrifugation at 5000 × g, 4 ◦ C for 10 min. The supernatants were separated in order to carry out the analytical assays. Different enzymatic hydrolysis conditions were tested according to either Plackett–Burman or CCRD. Tables 2 and 3 show the experimental variables studied. 2.3.2. Analytical assays Enzymatic hydrolysis was evaluated by the ratio of glucose mass released to dry biomass. The mass of released glucose corresponded

2. Experimental

Table 2 Process variables used in Plackett–Burman design.

2.1. Enzymes

Variables

Low level (−1)

High level (+1)

pH (X1 ) Temperature (◦ C) (X2 ) Buffer concentration (mM) (X3 ) Cellulase concentration (FPU/g dry biomass) (X4 ) ␤-Glucosidase concentration (CBU/g dry biomass) (X5 ) Reactional volume (mL) (X6 ) Incubation time (h) (X7 ) Agitation (rpm) (X8 ) Substrate concentration (%dry biomass/V) (X9 ) PEG 4000 concentration (g/g dry biomass) (X10 ) Buffer (X11 )

4.0 30 10 1

6.5 55 250 50

1

300

1.5 24 100 2

6 120 250 12.5

0.01

0.2

Acetate

Citrate

In order to investigate the influence of enzyme concentration on the enzymatic hydrolysis of rock-rose and broom, two commercial enzyme solutions, NS50013 (cellulase complex) and NS50010 (␤-glucosidase), were used. According to the information sheet, the optimum temperature for cellulase complex NS50013 is in the range 45–60 ◦ C and for ␤-glucosidase NS50010 in the range 45–70 ◦ C. Regarding optimum pH, the range for NS50013 is from 4.5 to 6.5 and for NS50010 is from 2.5 to 6.5. Cellulase and ␤-glucosidase activities of NS50013 were 63 FPU/mL and 12 CBU/mL, respectively [24,25]. ␤-Glucosidase activity of NS50010 was 925 CBU/mL [25]. One international filter paper unit (FPU) was defined as the amount of enzyme that releases 1 ␮mol of glu-

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Table 3 Coded and decoded values for each variable of the central composite rotatable design. Coded levels of the experimental factors

X1 : temperature (◦ C)

X2 : pH

X3 : cellulase (FPU/g dry biomass)

X4 : PEG 4000 (g/g dry biomass)

X5 : incubation time (h)

−2 −1 0 +1 +2

20 30 40 50 60

3 4 5 6 7

1.5 21 40.5 60 79.5

0.01 0.14 0.27 0.4 0.53

1.5 25 48.5 72 95.5

to the difference of glucose mass after incubation and at reaction time zero. The glucose concentration in the supernatants was assayed using an YSI 7100MBS analyzer (multiparameter bioanalytical system). This is a system employing a glucose oxidase catalyzed reaction to ultimately produce hydrogen peroxide, which is electrochemically oxidized at a platinum anode of an electrochemical probe, producing a probe signal current. 2.3.3. Experimental design Response surface methodology (RSM) is a collection of mathematical and statistical techniques that are useful for modelling and analyzing problems in which a response of interest is influenced by several variables and the objective is to optimize this response [29]. Many variables may potentially affect the efficiency of enzymatic hydrolysis process. In this study, a central composite rotatable design was employed to determine the effects of independent variables on the response and factor interactions, with a total of 58 runs with different combinations of variables. After examining the process and preliminary experiments, 11 variables or factors were identified to include in a Plackett–Burman design. 2.3.3.1. Plackett–Burman design. Plackett–Burman design was introduced in this study as a first optimization step to identify which factors have a significant effect on enzymatic hydrolysis. For the selection of these factors, Design Expert® 7.1.5, Statease, Inc., Minneapolis, USA, was used to generate and analyze the experimental design of Plackett–Burman. Based on Plackett–Burman factorial design, each variable was examined in two levels: −1 for low level and +1 for high level [30]. This design does not consider the interaction effects among variables and it was used to screen and evaluate the important variables that influence the response. In the present work, 11 assigned variables were screened in 12 experimental designs. All experiments were carried out in triplicate and the averages of the ratio of mass of glucose released to dry biomass were taken as response. The factors that were included in the screening experiment and their settings are given in Table 2. 2.3.3.2. Central composite design. The central composite design (CCD) is one of the most commonly used response surface designs for fitting second-order models. A central composite design consists of F factorial points, 2k axial points (±˛), and nc center points. The factorial portion is used to fit all linear and interaction terms. The axial points provide additional levels of the factor for purposes of estimation of the quadratic terms [31]. In our study, the central composite rotatable design (CCRD) of 1/2 fraction type was used to optimize the enzymatic hydrolysis of forestry residues: C. ladanifer (rock-rose) and C. striatus (broom). Five independent variables, namely temperature (X1 ), pH (X2 ), cellulase concentration (X3 ), PEG 4000 concentration (X4 ) and incubation time (X5 ) were studied at five levels with six repetitions at the central point and two replicates at axial and factorial points (Table 3). For each of the five variables studied, high (coded +1) and low (coded −1) set points were selected according to the results obtained with preliminary tests, Plackett–Burman design, taking into consideration

the required experimental conditions and literature. The results of each CCRD were analyzed using Design Expert® software version 7.1.5, from Statease, Inc., Minneapolis, USA. Both linear and quadratic effects of the five variables under study were calculated, as well as their possible interactions, on released mass of glucose by dry biomass. Their significance was evaluated by variance analysis (ANOVA). Three-dimensional surface plots were drawn to illustrate the effects of the independent variables on the dependent variable, being described by a quadratic polynomial equation, fitted to the experimental data. The fit of the models was evaluated by the determination of R-squared coefficient and adjusted R-squared coefficient. The validation of the models optimum values of the selected variables for rock-rose and broom were obtained by solving the regression equation using Design Expert® software version 7.1.5. 3. Results and discussion Enzymatic hydrolysis is a key step in the conversion of cellulose into ethanol. To improve yield and rate of the enzymatic hydrolysis, research has focused on optimizing the hydrolysis process. Preliminary experiments were carried out to screen the appropriate parameters and to determine the experimental domain. These preliminary studies include the evaluation of supplementation of reactional medium with a polymer or surfactant for the improvement of enzymatic hydrolysis (Fig. 1). Eriksson et al. [10] have tested the effect of several surfactants in enzymatic hydrolysis of steam-pretreated spruce and found an increase in the conversion of cellulose to soluble sugars, allowing the reduction of the enzymes used. A yield improvement was also accomplished with steam exploded and ethanol pretreated Lodgepole pine by reaction supplementation with surfactants [13]. The addition of bovine serum albumin also proved to be effective when added

Fig. 1. Released mass of glucose by dry biomass after 72 h hydrolysis of pretreated rock-rose as function of Tween 20 or PEG 4000 concentration. Pretreated rock-rose was hydrolyzed in caped tubes at 50 ◦ C and 250 rpm, on a rotary shaker, using a mixture containing 0.05M citrate buffer, pH 4.8, 2.5% (w/v) of solid concentration, cellulase activity of 25 FPU/g dry biomass (NS50013), 25 CBU/g dry biomass ␤-glucosidase activity (NS50010), and 40 ␮g/mL tetracycline and 30 ␮g/mL cycloheximide, in a final volume of 3 mL.

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to the mixture before the enzymatic reaction [10,16]. According to Borjesson [14,15] the addition of non-ionic surfactants and polymers containing poly(ethylene oxide) can effectively increase enzymatic hydrolysis of lignocellulose. So, a surfactant (Tween 20) and a polymer (poly(ethylene)glycol 4000) were tested and compared in enzymatic hydrolysis of pretreated rock-rose. A significant improvement in the hydrolysis yield was observed by the addition of any of those compounds. However, the best results were obtained with PEG 4000. Tween 20 was described as leading to a decrease of cellulase binding to lignin, occupying the hydrophobic regions on the surface of lignin and therefore increasing the concentration of enzymes available for hydrolysis of cellulose [10]. Poly(ethylene)glycol (PEG) is also described as adsorbing to lignin and preventing unspecific adsorption of enzymes [14]. Considering the obtained results, PEG 4000 was selected for use in the tests with experimental design methodology. It is well known that enzymes show optimal conditions of operation, such as temperature and pH. So, these two variables were studied in conjunction with ionic strength, buffer system, concentration of enzymes (cellulases and ␤-glucosidase), reactional volume, incubation time, agitation, substrate concentration and polymer concentration (PEG 4000). 3.1. Plackett–Burman design Plackett–Burman design was used as a screening method to determine which of the 11 factors significantly affect enzymatic hydrolysis procedure; this is achieved by simultaneously shifting variables from a low value to a high value. The values for some variables were chosen taking into account the preliminary assays performed and the possibility of enzyme inactivation or denaturation at some experimental conditions. In Plackett–Burman design, the ratio of released mass of glucose by dry biomass varied between 0.004 and 0.242 g glucose/g dry biomass for rock-rose enzymatic hydrolysis and 0.008–0.435 g glucose/g dry biomass for broom. The experimental results were interpreted based on the estimation of how each of the factors affected the response. Those effects were estimated by statistical analysis. Table 4 shows the standardized effects of each variable on the release of glucose when using each pretreated biomass type. These effects were determined by the difference between the average of measurements made at the high level and the low level of the factor. The negative sign shows that the shift of the variable from the low level to the high level produced a decrease in released glucose, while the positive sign means that this change increased such response [32]. Regarding the examined levels of variables, it is apparent from Table 4 that cellulase concentration was the most significant vari-

195

able, providing a 6.7-fold increase in glucose released from low to high level and presenting a positive standardized effect for rockrose and broom of 0.150 and 0.220, respectively. The buffer system and the buffer concentration are nonsignificant factors for rock-rose, but when these two factors are significant for broom, their effects on glucose release are negative, meaning that cellulose conversion to glucose is lower with an increase of buffer concentration and when using acetate buffer instead of citrate buffer. Also, the variable incubation time and agitation are non-significant factors for rock-rose; this fact could be attributed to inactivation of the enzymes or lack of stability after some time. When they are significant their effect in broom hydrolysis is presented as positive. ␤-Glucosidase concentration, reactional volume, substrate concentration and PEG concentration were non-significant variables when broom hydrolysis was studied. The fact of ␤-glucosidase concentration being a non-significant variable when using broom and presenting a negative effect for rock-rose can presumably be related with a product inhibition or saturating levels already provided by the cellulase complex. The supplementation with ␤-glucosidase is reported in literature as important factor in obtaining a high glucose yield in enzymatic hydrolysis of steam-pretreated softwood, increasing cellulose conversion up to a certain concentration [19]. However, the effectiveness of a given ␤-glucosidase loading may vary with substrate, pretreatment and time of hydrolysis [19,33]. A similar or more efficient glucose release can be achieved without this supplementation in our study when using a PBD with pretreated rock-rose and broom, as was shown by the negative (−0.052) and low (0.002) standardized effects of this factor upon rock-rose and broom, respectively. The variable substrate concentration was found as nonsignificant for broom, but it presented a negative effect in rock-rose case, considering a range of 2–12.5% (dry biomass/V). This fact could be attributed to an inefficient mass transfer when biomass increases, amplified in rock-rose by the higher lignin content. PEG concentration is a factor that presents a positive effect on rock-rose hydrolysis and non-significant effects with broom; this may be related to the different lignin contents of the shrubs. The factor temperature was one of the most important variables, its statistical significance being one of the highest for both shrubs’ hydrolysis. Buffer concentration and pH were more statistically significant variables for broom than for rock-rose. The significance of these parameters may be attributed to the fact that both the binding of charged substrates to enzyme and the movement of charged groups within the catalytic active site are influenced by the ionic composition of the reactional medium [34]. Despite some variables not being the most significant ones when considering the shrubs separately, they were chosen in such a way that they would be the most significant for both plant shrubs at the

Table 4 Standardized effects and contribution percentage of the variables tested in Plackett–Burman design. Variables

pH (X1 ) Temperature (◦ C) (X2 ) Buffer concentration (mM) (X3 ) Cellulase concentration (FPU/g dry biomass) (X4 ) ␤-Glucosidase concentration (CBU/g dry biomass) (X5 ) Reactional volume (mL) (X6 ) Incubation time (h) (X7 ) Agitation (rpm) (X8 ) Substrate concentration (% dry biomass/V) (X9 ) PEG 4000 concentration (g/g dry biomass) (X10 ) Buffer (X11 )

Rock-rose

Broom

Standardized effects

% Contribution

Standardized effects

% Contribution

−0.015 0.030 0.002 0.150 −0.052 0.020 0.008 0.001 −0.017 0.061 0.008

0.800 2.890 0.016 72.270 8.920 1.350 0.190 0.004 0.940 12.430 0.190

−0.063 0.110 −0.047 0.220 0.002 −0.013 0.035 0.045 0.001 0.021 −0.051

5.640 16.680 3.030 65.610 0.007 0.220 1.670 2.900 0.002 0.630 3.620

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Table 5 ANOVA table for the adjusted model of response from enzymatic hydrolysis of pretreated rock-rose. Source

Sum of squares

Degree of freedom

Sum of squares

F value

p-Value (Prob > F)

Model X1 : temperature X2 : pH X3 : cellulose X4 : PEG 4000 X5 : incubation time X22 X32 X52 Residual Lack of fit Pure error

0.345 0.098 2.147 × 10−3 0.082 3.317 × 10−3 0.142 1.284 × 10−2 4.678 × 10−3 3.729 × 10−3 0.036 3.238 × 10−2 3.793 × 10−3

8 1 1 1 1 1 1 1 1 49 18 31

0.043 0.098 2.147 × 10−3 0.082 3.317 × 10−3 0.142 1.284 × 10−2 4.678 × 10−3 3.729 × 10−3 7.38 × 10−4 1.799 × 10−3 1.22 × 10−4

58.366 133.512 2.908 111.429 4.493 191.816 17.400 6.338 5.051

<0.0001 <0.0001 0.0945 <0.0001 0.0391 <0.0001 0.0001 0.0151 0.0291

14.700

<0.0001

Total

0.381

57

R2 = 0.905; adjusted R2 = 0.890.

same time. This was done, as there is a purpose to evaluate the possibility of simultaneous enzymatic hydrolysis of both residues. In fact, cellulase concentration and temperature were chosen because they had a positive effect and the highest contribution to PBD. In addition, pH was chosen since it is described as a parameter with an optimal value in enzymatic reaction, referred by Tengborg et al. [19] as influencing optimal temperature and incubation time. PEG 4000 concentration was introduced in CCRD as a variable due to the presented standardized effect on PBD in rock-rose. Based on these considerations the values of the other variables were fixed: 50 mM of buffer concentration, 3 mL of reactional volume, 250 rpm of agitation, 5% dry biomass/volume, the use of citrate buffer and no supplementation with ␤-glucosidase. The number of factors to be included in the next experiment was reduced from eleven to five. 3.2. Enzymatic hydrolysis optimization with central composite rotatable design The enzymatic response, as a function of temperature, pH, cellulase concentration, poly(ethylene)glycol 4000 concentration and incubation time, of rock-rose (C. ladanifer) and broom (C. striatus) was evaluated. Due to the compatibility of buffer system and pH, the used buffer was citrate and not acetate. The range of pH and temperature was modified relatively to PBD considering the design matrix with respect to the values of axial points. Cellulase and PEG 4000 concentration values were increased considering the effects observed in Plackett–Burman design. Considering the performed central composite rotatable design in a temperature range from 30 to 50 ◦ C with an incubation time until 72 h, it is possible to see that the hydrolysis yield increases continuously for the studied biomass. The pH presented an optimal value of 4.86 and 4.53 for pretreated rock-rose and broom, respectively. The present study shows no correlation between temperature, incubation time or pH, which is different from the studies of Tengborg, on the evaluation of the influence of these parameters on steam-pretreated softwood [19]. One of the reasons for the several differences in the best conditions of enzymatic hydrolysis may be attributed to the variation in the chemical composition of the different biomass. However, the concentration of PEG 4000 was not statistically significant for pretreated broom unlike pretreated rock-rose. This fact may be explained by lignin content difference between the two substrates, since the unspecific binding of cellulase to lignin is described as diminishing with PEG addition due to its adsorption to lignin and an increase in the concentration of enzymes available for hydrolysis of cellulose [14,15].

An analysis of variance (ANOVA) was performed for the evaluation of the effects of the variables and their possible interactions. Coefficients of a full model were evaluated by regression analysis and tested for their significance. The insignificant coefficients were excluded from the model by a backward elimination. The analysis of variance performed on the reduced models (Tables 5 and 6) demonstrates that the models are statistically valid with p-values lower than 0.0001. ANOVA (Tables 5 and 6) for model terms and its significance (pvalues lower than 0.05 indicated that model terms were significant) showed that linear effect of temperature, cellulase concentration and incubation time were considerably higher than other effects (p < 0.0001) demonstrating that these are the most significant factors affecting enzymatic hydrolysis of pretreated rock-rose. To broom beside the factors referred to rock-rose, the linear effect of pH also showed a very high effect (p < 0.0001). The absence of interactions between factors (p > 0.05) may lead to the assumption that factors have an additive effect on the response. Eq. (1) describes the correlation between the significant variables and the glucose releasing rate for pretreated rock-rose in terms of decoded values when using the reduced model. Response = −0.631 + 4.5 × 10−3 · Temperature + 0.148 · pH + 0.0041 · Cellulase + 0.064 · PEG 4000 + 3.8 × 10−3 · Incubation time − 0.015 · pH2 − 2.5 × 10−5 ·Cellulase2 − 1.5 × 10−5 · Incubation time2

(1)

After Backward Elimination Regression, pH has been added as a Hierarchical Term. And to pretreated broom, by Eq. (2): Response = −1.048 + 6.7 × 10−3 · Temperature + 0.290 · pH + 7.1 × 10−3 · Cellulase + 5.8 × 10−3 Incubation time − 0.032 · pH2 − 4.9 × 10−5 · Cellulase2 − 3.4 × 10−5 ·Incubation time2

(2)

The relationship between the response and variables is visualized by the response surface or contour plot to see the influence of the parameters. The quadratic polynomial equations to experimental data (Eqs. (1) and (2)) can be described by the response surface plots for released glucose by enzymatic hydrolysis of pretreated rock-rose (Fig. 2) and of pretreated broom (Fig. 3) as a function of two factors at a time, maintaining all other factors fixed at level zero.

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Table 6 ANOVA table for the adjusted model of response from enzymatic hydrolysis of pretreated broom. Source

Sum of squares

Degree of freedom

Sum of squares

F value

p-Value (Prob > F)

Model X1 : temperature X2 : pH X3 : cellulase X5 : incubation time X22 X32 X52 Residual Lack of fit Pure error

0.674 0.214 0.048 0.174 0.164 0.056 0.018 0.019 0.115 0.088 0.028

7 1 1 1 1 1 1 1 50 19 31

0.096 0.214 0.048 0.174 0.164 0.056 0.018 0.019 2.307 × 10−3 4.62 × 10−3 8.89 × 10−4

41.764 92.976 20.783 75.585 71.057 24.100 8.052 8.380

<0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0065 0.0056

5.194

<0.0001

Total

0.790

57

R2 = 0.854; adjusted R2 = 0.834.

The proportion of total variation attributed to each fit can be evaluated by the value of R-squared (a value of R-square > 0.75 indicate the aptness of the model) [32]. For pretreated rock-rose, the regression equation obtained after ANOVA indicating a R-squared value of 0.905 was in good agreement with the adjusted R-squared

of 0.890. For pretreated broom the R-squared value was 0.854 and adjusted R-squared was 0.834. This ensured a satisfactory adjustment of the theoretical values to the experimental data by this model. The lack of fit is significant, however, R-squared value is high (0.905 for rock-rose and 0.854 for broom) indicating that the

Fig. 2. Response surface plots of the central composite design for the optimization of the enzymatic hydrolysis of pretreated rock-rose. Effect of (A) cellulase concentration and temperature; (B) cellulase concentration and pH; (C) cellulase concentration and incubation time; (D) cellulase concentration and PEG 4000 concentration. Other factors were constant at zero levels.

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Fig. 3. Response surface plots of the central composite design for the optimization of the enzymatic hydrolysis of pretreated broom. Effect of (A) cellulase concentration and temperature; (B) cellulase concentration and pH; (C) cellulase and incubation time. Other factors were constant at zero levels.

models are well adapted to the responses. Therefore the model is suitable to predict enzymatic hydrolysis of pretreated rock-rose and broom. The optimum values of the selected variables for rock-rose and broom were obtained by solving the regression equation, as shown in Table 7. To validate the model, the optimum values for Eqs. (1) and (2) were used in triplicate sets of experiments and the maximum response obtained for each shrub is presented in Table 7. The experimental response for pretreated rock-rose and broom was 0.313 and 0.448 g glucose/g dry biomass, respectively. These values are in good agreement with the predicted value 0.326 g glucose/g dry biomass (0.310–0.340 g glucose/g dry biomass) and 0.427 g glucose/g dry biomass (0.400–0.460 g glu-

cose/g dry biomass), considering a range of 95% confidence. This behavior shows the adaptation of the model to the experimental results, confirming the validity and adequacy of the models. Several studies have evaluated the influence of the cellulase loading in enzymatic hydrolysis. Chen et al. [35] demonstrated the requirement of higher cellulase concentrations to corncob cellulose hydrolysis, verifying an increase in hydrolysis yield from 50 to 150 FPU/g substrate, although above 100 FPU/g substrate the raise was weaker. Tengborg et al. [19] studied the hydrolysis of whole slurry of steam-pretreated softwood with a cellulases loading until 120 FPU/g cellulose do not achieve a complete hydrolysis even after 120 h. Lu et al. [23] reached to saturation with an increase in enzyme concentration from 20 to 25 FPU/g with an optimum

Table 7 Optimal values of the test variables in decoded units and predicted maximum with 95% confidence interval of released mass of glucose by dry biomass. Variables ◦

X1 : temperature ( C) X2 : pH X3 : cellulase (FPU/g dry biomass) X4 : PEG 4000 (g/g dry biomass) X5 : incubation time (h) Predicted response with 95% confidence interval (g glucose/g dry biomass) Experimental response (g glucose/g dry biomass)

Pretreated rock-rose

Pretreated broom

50 4.86 60.00 0.40 72.00 0.326 [0.310 − 0.340] 0.313 ± 0.004

50 4.53 60.00 0.27 72.00 0.427 [0.400 − 0.460] 0.448 ± 0.011

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for cellulase concentration of 22 FPU/g substrate. These studies show that different biomass and different conditions of pretreatment and enzymatic hydrolysis reaction may lead to very different results. In our study it was observed a rising model response with the increase of cellulase concentration from 21 to 60 FPU/g dry biomass for both shrubs. When applying the reduced model considering the axial points with pretreated rock-rose until 79.5 FPU/g dry biomass (axial point in CCDR) is observed a raise in hydrolysis yield, to broom it is presented a stabilization around 72 FPU/g dry biomass. By employing different substrates, similar behavior was observed when the response surface methodology was applied to pH, temperature, concentration of cellulase and time. Substituting in Eq. (1), the values of the central points to temperature, pH and incubation time and varying from 0.14 to 0.4 g PEG/g dry biomass and from 21 to 60 FPU/g dry biomass it is shown that when passing from the higher to the lower cellulase concentrations the influence of PEG concentration increase lead to a smaller enhancement of the released glucose (12.2–7.6%, respectively). So, to broom case no significant results with PEG 4000 may be due to the utilization of a high enzyme loading considering the lignin content of this substrate to be seen as a significant effect of this factor. The difference in cellulose conversion during enzymatic hydrolysis is largely dependent on the difference in lignin content [36]. 4. Conclusions With the main goal of testing as many factors as possible and selecting those that affected enzymatic hydrolysis yield most significantly, a Plackett–Burman design was used. To estimate effects of temperature, cellulase concentration, pH, PEG 4000 concentration and incubation time on the response and factor interactions, a central composite rotatable design was employed. This experimental design can convert the process factor correlations into mathematical models that predict where the response is likely to be located. From the results it can be concluded that the enzymatic hydrolysis of rock-rose and broom was clearly enhanced by temperature, cellulase concentration, and incubation time, on the range tested. On the contrary, the hydrolysis process is less markedly enhanced by poly(ethylene)glycol 4000 concentration and with different effects for both shrubs: is a statically significant variable to rockrose, but not to broom. This fact may be explained by lignin content difference between the two substrates. Regarding broom, the variable pH proved to be highly significant; a hypothesis to explain this fact is cellulase availability for the enzymatic reaction. The amount of binding cellulase to lignin may differ from broom and rock-rose, since they have different lignin contents, so the higher availability of cellulase to reaction in broom case may be translated in a higher significance of influence of pH. A cellulase concentration of 60 FPU/g dry biomass yielded 313 mg of glucose (per g of dry biomass to pretreated rock-rose) and 448 mg of glucose (per g of dry biomass to pretreated broom), at the experimental conditions: 50 ◦ C, pH 4.86, 0.4 g PEG 4000/g dry biomass at 72 h and 50 ◦ C, pH 4.50, 0.27 g PEG 4000/g dry biomass at 72 h to pretreated rock-rose and broom, respectively. The model validation proved a good agreement between the experimental results and predicted response. Acknowledgements This research was supported by Instituto de Financiamento e Apoio ao Desenvolvimento da Agricultura e das Pescas (IFADAP) Project 2006.09.001055.1. We thank Univar Iberia, SA, Portugal, for supplying the enzymes.

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