A statistical approach for optimization of R-phycoerythrinextraction from the red algae Gracilaria verrucosaby enzymatic hydrolysis using central composite designand desirability function

Fethi Mensi & Jamel Ksouri & Emma Seale &

Mohamed Salah Romdhane & Joêl Fleurence

Received: 25 January 2011 /Revised and accepted: 9 August 2011 /Published online: 26 August 2011# Springer Science+Business Media B.V. 2011

Abstract The aim of this research is to statisticallyoptimize enzymatic hydrolysis parameters for the produc-tion of R-phycoerythrin (RPE) from red algae Gracilariaverrucosa. Six independent variables, incubation tempera-ture, incubation time, ratio of buffer to raw material,cellulase loading, xylanase loading, and pH, were selectedfor response surface methodology studies. A centralcomposite design was employed to maximize RPE produc-tion. A mathematical model with high determinationcoefficient (R2=0.86) was developed and could beemployed to optimize RPE extraction. The optimal extrac-tion conditions of RPE were determined as follows:incubation temperature (48°C), incubation time (6 h), ratioof buffer to raw material (20 w/v), cellulase loading (15%),xylanase loading (5%), and pH (6.5). Under this optimalcondition, the experimental yield of RPE was 6.25 mg g−1.

Based on the result of response surface methodology anddesirability function approach study, total sugar, the mainby-product in RPE extraction was considered as anotherresponse. A new optimal condition was predicted asfollows: incubation temperature (30°C), incubation time(12 h), ratio of buffer to raw material (20, w/v), cellulaseloading (15%), xylanase loading (5%), and pH (6). Underthis condition, similar RPE levels were obtained while theconcentration of total sugar decreased by 40%.

Keywords Gracilaria verrucosa . Enzymatic hydrolysisextraction . Optimization . Response surface methodology .

Desirability function

Introduction

Phycoerythrin is a red protein from the light-harvestingphycobiliprotein family, present in cyanobacteria, red algae,and cryptomonads. Phycobilins are soluble in water andfluorescent and are largely responsible for the distinctivecolors, including blue-green, yellow, and red (Abalde et al.1998). Phycobilins are assembled into particles namedphycobilisomes except in cryptomonads, which are attachedin regular arrays to the external surfaces of the thylakoidmembrane; they act as major light-harvesting pigments insome algal groups and cyanobacteria (Glazer and Sttryer1984). Some characteristics of phycobiliproteins make themwell suited for fluorescence applications in clinical andimmunological analysis. They are also used as a colorant,and their therapeutic value has also been categoricallydemonstrated (Sekar and Chandramohan 2008).

Species of the genus Gracilaria are a rich andinexpensive source of phycobiliproteins (Wang 2002). Over

F. Mensi (*) : J. KsouriInstitut National des Sciences et Technologies de la Mer,Laboratoire de biodiversité et biotechnologies marines,Centre Kheireddine 29, Rue General Kheireddine 2015,Le Kram, Tunisiae-mail: mensi.fethi@instm.rnrt.tn

E. SealeDaithi O’Murchu Marine Research Station, Gearhies, Bantry, Co.,Cork, Ireland

M. S. RomdhaneInstitut National Agronomique de Tunisie 43 Rue Charles Nicolle,Cité El Mahrajene, Tunis, Tunisia

J. FleurenceMer, Molécules, Santé EA 2160 UFR Sciences et Techniques,Pôle Mer Littoral 2 Rue de la Houssinière,BP 92208 44 322, Nantes cedex 03, France

J Appl Phycol (2012) 24:915–926DOI 10.1007/s10811-011-9712-1

100 species of red algae, including Gracilaria spp. arecultured worldwide for the agar industry and represent arich source of protein. In general, the key to maximalrecovery of raw phycobilproteins from algae is theextraction method (Niu et al. 2007). The extraction ofphycobiliproteins involves rupturing the cell and thesubsequent release of proteins from within the cell. To dothis, variations in the osmotic pressure, abrasive conditions,chemical treatment, freezing–thawing and sonication,among others, are necessary methods of extraction(Siegelman and Kycia 1978; Kula and Schutte 1987;Fleurence and Guyader 1995). Extraction and purificationof phycobiliproteins from algae were complicated andlengthy procedures (Tchernov et al. 1993); however,enzymatic hydrolysis methods are currently preferred(Joubert and Fleurence 2008). The latter process involvesenzymatic disintegration of the cell wall in order to liberatethe protein fraction from the cell. It is a gentle processwhich avoids depolymerization of the agarophytes andpreserves the molecular proprieties of R-phycoerythrin(RPE) (Abdeladhim 2005; Denis et al. 2009). Severalfactors within the process influence RPE extraction, themost important of which are the methods of celldisruption, incubation temperatures, extraction time,buffer, ratio of buffer to raw material, and pH. However,it is important to study the factors affecting extractionas a whole in order to optimize the process, minimizingcosts, and maximizing yield.

The production cost of any biotechnological processcan be considerably reduced by optimizing the process(Sangkharak and Prasertsan 2007). Optimization typicallyinvolves changing one factor or varying several factors atthe same time. The method of “one variable at a time”approach allows one to resolve RPE yield question bysystematically adding or deleting factors with minimalcomplicated interactions. Statistical experimental designsprovide an efficient approach to process optimization, asthey specifically account for interactions and identify themost significant factors affecting the process. A combina-tion of factors generating a particular optimal response canbe elucidated by a factorial design and response surfacemethodology (RSM). RSM is a statistical technique, basedon the fundamental principles of statistics, randomization,replication, and duplication, which simplifies the optimiza-tion by examining the mutual interactions among thevariables, over a range of values, in a statistically validmanner. Central composite design (CCD) is the mostaccepted design among several classes of RSM; it offersthe most information and reveals overall experiment errorin a least number of runs (Montgomery 1997).

In the extraction process, high temperature can result ingelification and molecular denaturizing and at lower levelsinvolves partial hydrolysis. High concentration of total

sugar will increase the cost of RPE yield. Thus, aneconomically optimal extraction condition should achievea good balance between a high RPE yield and a lowconcentration of total sugar. Desirability function approach(DFA) was used to find the best compromise between thetwo responses (the RPE yield and total sugar) based on themathematical models constructed in RSM. Unfortunately,statistical experiments are not widely used in the optimiza-tion of RPE production. In this study, the main objectivewas to optimize enzymatic hydrolysis conditions for theextraction of RPE from Gracilaria verrucosa using enzymemixtures consisting of purified endocellulase and β-xylanase. RSM was designed to systemic analyze theeffects of extraction parameters on the yields of RPE fromG. verrucosa and their interactions.

Materials and methods

Clean healthy G. verrucosa were selected and collected, in2009, from Bizerte Lagoon located on the North of Tunisia(37°8′–37°14′ N, 9°48′–9°56′ E) and transported to thelaboratory in 25-L plastic containers. The site is a shelteredestuary (1.0-m water depth), with a salinity range of 30–35‰, a temperature range of 17–20°C, and low waterclarity. Algae were cleaned of epiphytes and washed infiltered seawater. To obtain homogenous thalli, stockcultures were maintained in 14 L of medium in aerated a20-L glass aquaria in a “walk in” culture room understandard conditions of 20°C, 36‰, 12:12-h light/dark (L/D)cycle provided by cool-white fluorescent light (50 μmolphotons m−2 s−1). After 3 weeks, 500 g of the fresh sampleswere taken and placed in plastic container covered withaluminum foils. Inert nitrogen gas was passed into thecontainers and samples were kept at −20°C for use inextraction protocols.

Enzymes and substrate

A two commercial polysacharidases, xylanase (E.C.3.2.1.32) and cellulases (E.C. 3.2.1.4) were provided byFulka and used without further purification in this work.The source of the xylanase and cellulases were Tricho-derma viride and Aspergillus niger, respectively. Thexylanase was assayed as hydrolytic activities, remazolbrilliant blue R-xylan and Fulka N° 66960; as substrate,3.51 UI mg−1 at pH 6.0 and 40°C; and cellulases1.44 UI mg−1 at pH 5.0 and 37°C with carboxymethylcel-lulose as substrate. All tests were performed twice.Enzymatic hydrolysis of G. verrucosa was carried out in0.5 L Erlenmeyer flasks equipped with a magnetic stirrer asa reactor. The apparatus is schematically shown in Fig. 1.The volume of the reaction solution was 0.3 L. The

916 J Appl Phycol (2012) 24:915–926

substrate, suspended in buffer solution, was stirred to formwell dispersed slurry before starting the hydrolysis byadding a given amount of concentrated cellulase andxylanase solution. Then, temperature was kept stable inthermostated water bath. Two phases were obtained bycentrifugation (6,000×g for 20 min at 20°C). The superna-tant was removed and stored at 4°C and the pellet wasfrozen at −20°C.

The spectral analysis of the supernatants was performedwith a spectrophotometer at wavelengths between 400 and600. Using Beer–Lambert’s law, the phycoerythrin extinc-tion coefficient of 2.106 M−1cm−1at 565 nm and thephycoerythrin molecular weight of 24.000 Da, the amountof phycoerythrin in the supernatant were estimated (Deniset al. 2009). Total sugars, from the hydrolyzed polysac-charides, were determined using the phenol-sulphuric acidreagent as described by Dubois et al. (1956) with D-glucoseas the calibration standard.

Experimental design

The potential factors that influence the bioprocess wereidentified by the “one variable at a time” approach, in apreliminary experiment (data not shown). CCD wasselected to resolve their optimal combinations (Montgomery1997). The selected variables were: incubation temperature(°C), incubation time (h), ratio of buffer to raw material(w/v), cellulase loading (%), xylanase loading (%), and pH.In order to optimize the extraction of RPE from G.verrucosa, a central composite rotatable design was used,to examine the influence of different factors on RPEextraction. The effects of these factors on responsesconcentration were investigated at five levels (−2, −1, 0, 1,and 2). This design was composed of 26–1 fractional factorialdesign (runs 1–32), 14 star points (runs 33–44), and tworeplicates (runs 45–46). Thus, 46 experiments were neededin total. Each of the six variables examined were selectedaccording to the results obtained from preliminary tests,whilst taking into consideration the required experimentalconditions and knowledge for previous literature. Table 1indicates the coded and actual values.

To examine the influence of initial pH of the medium onRPE production, the pH of the medium was set at desiredlevels using phosphate buffer consisting of 0.1 M potassi-um monobasic (KH2PO4) and 0.1 M sodium hydroxide(NaOH). For every buffer solution, the pH was adjusted ifneeded with hydrochloric acid. All experimental designswere completed at least twice. Runs 45 and 46 were com-pleted twice and are averages of 4 values. Statistical andnumerical analyses were carried out by analysis of variance(ANOVA) and multiple regressions, using STATISTICA(version 8.0, StatSoft Inc.). This analysis included theFisher’s F test, its associated probability p (F) and R. R2

explains the quality of polynomial model. For eachvariable, the quadratic models were represented as contourplots (3D), and response surface curves were generated.The experimental data allowed the development of empir-ical models describing the interrelationship between oper-ational and experimental variables via equations includinglinear, interaction, and quadratic terms. The quadraticmodel for predicting the optimal point was expressed asEq. 1:

Y ¼ b0 þX

bixi þX

biix2i

þXX

bijxixj; i ¼ 1; 2; . . . k; j ¼ 1; 2; . . . ; k; i 6¼ j

ð1ÞHere, Y represents the RPE concentration, β0 is the value

of the fitted response at the center point of design; βi, βii,and βij are the linear, quadratic, and interaction terms,respectively. xixj are the input variables which influence theresponse variable Y; and βi is the ith linear coefficient.Other parameters which have no effect on RPEproduction were kept constant. The variables werecoded according to Eq. 2:

xi ¼ Xi � X0ð Þ=ΔXi; i ¼ 1; 2; :::::::; k ð2Þ

Where xi and Xi are the coded and the actual values ofthe independent variable i, X0 is the actual value of theindependent variable at the center point, and ΔXi is the stepchange of Xi corresponding to a unit variation of thedimensionless value.

Response functions may be analyzed by canonicalanalysis (Myers and Montgomery 2002). The stationarypoint, if it exists, is the solution to Eq. 3 and couldrepresent a point of maximum response, a point ofminimum response, or a saddle point.

@Y

@x1¼ @Y

@x2¼ . . . ¼ 0 ð3Þ

The algebraic signs of the eigenvalues provide an ideaabout the nature of its stationary point. If the values are allFig. 1 Experimental apparatus to study enzymatic hydrolysis

J Appl Phycol (2012) 24:915–926 917

negative, it is a maximum; if all positive, it is a minimum,and if the signs are mixed it is a saddle point.

In DFA, the multicriteria problem is reduced to a singlecriterion problem of D optimization (Eq. 6). The measuredproperties of each predicted response by are transformed to adesirability value d. The scale of the desirability function(DF) ranges between d=0, which suggests that the responseis completely unacceptable, and d=1, which suggests thatthe response is exactly the target value. The value ofd increases as the “desirability” of the correspondingresponse increases (Derringer and Suich 1980).

A one-sided transformation was used to transform theindividual response into corresponding desirability value.In this study, the extraction of RPE and total sugar are“larger-the-better” and “smaller-the-better” problems,respectively. The responses were transformed into difollowing the equations below:

Larger-the-better,

d ¼ by� L

U � L

��������a

; L � by � U ;with d ¼ 0 forby � L and d

¼ 1 forby � U ð4ÞSmaller-the-better,

d ¼ by� L

U � L

��������a

; L � by � U ;with d ¼ 0 forby� U and d ¼ 1 forby � L

ð5Þ

L and U indicated in Table 2 are selected according tothe mathematical model in RSM.

In this study, because it was thought that the desirabilityof those responses increased in a linear manner, we set α=1for both by1 and by2. All the functions were combined into asingle criterion D where each single function could beequally treated as:

D ¼ d1xd2x ::::: xdkð Þ1=k ð6Þ

Where di represents the desirability of response yi (i=1,2, … k). Evidently that D will increase when the balance ofthe properties becomes more favorable.

Results

Response surface methodology

The results from this study helped to frame a second orderpolynomial equation (Eq. 7) (in coded units) that relates theRPE, Y (mg g−1) to incubation temperature, incubationtime, ratio of buffer to raw material, cellulase loading,xylanase loading, and pH. This equation was used topredict the RPE yield in Table 3. Apart from explaining thelinear effects of factors on RPE yield, the CCD approachdescribed the quadratic and interaction effects of theparameters too. After eliminating the statistically insignif-icant terms (p>0.05) and recalculating the new coefficients,a polynomial equation was derived from regression analysisas follows:

Y1 ¼ 5:13þ 0:91x1 � 0:64x2 þ 0:40x6 � 0:43x21

� 0:52x22 � 0:54x23 � 0:60x24 � 0:88x25 � 1:00x26

� 0:44x1x2 � 0:27x2x6 ð7Þwhere Y is the response variable (RPE production) and x1,x2, x3, x4, x5, and x6 are the coded values of incubationtemperature, incubation time, ratio of buffer to rawmaterial, cellulase loading, xylanase loading, and pH,respectively.

The model adequacy was checked by an F test and thedetermination coefficient R2. The analysis of variance(Table 4) showed that this regression model was highlysignificant (p<0.01) with F value of 14.90. The value of7.39 for lack of fit implies that it is not significantcomparing to the pure error. The fitness of the model wasfurther confirmed by a satisfactory value of determinationcoefficient, which was calculated to be 0.86, indicating that

Table 2 Selection of constraint levels in DFA

L U α

R-Phycoerythrin (mg g−1) 4 5.5 1

Total sugar (%) 10 30 1

Table 1 Process variables incoded and actual units Variables Incubation temperature (°C) Incubation

time (h)Ratio(w/v)

Cellulase (%) Xylanase (%) pH

Symbol x1 x2 x3 x4 x5 x6

Range and levels

−2 10 4 10 0 0 4

−1 20 8 15 7.5 2.5 5

0 30 12 20 15 5 6

1 40 16 25 22.5 7.5 7

2 50 20 30 30 10 8

918 J Appl Phycol (2012) 24:915–926

86% of the variability in the response could be predicted bythe model.

The larger the magnitude of the t value and the smallerthe p value indicates how significant the corresponding

Table 3 Central compositedesigns (CCD); factors are x1,temperature incubation (°C); x2,time incubation (h); x3, ratio(w/v); x4, cellulase (%); x5,xylanase (%); x6, pH

The factors levels are 32°C and40°C for x1, 6 and 20 h for x2, 6and 20 (w/v) for x3, 5% and 20%for x4, 10% and 20% for x5, and4 and 8 for x6. Value of R-phycoerythrin equal to zeroindicate the moleculardenaturation

Run x1 x2 x3 x4 x5 x6 RPE (mg g−1) Total sugar (mg g−1)

1 −1 −1 −1 −1 −1 −1 0.9 100

2 −1 −1 −1 −1 +1 −1 0.64 100

3 −1 −1 −1 +1 −1 −1 0.37 100

4 −1 −1 −1 +1 +1 1 2.03 300

5 −1 −1 +1 −1 −1 1 1.95 100

6 −1 −1 +1 −1 +1 1 2.08 100

7 −1 −1 +1 +1 −1 −1 0.39 100

8 −1 −1 +1 +1 +1 −1 0.49 100

9 −1 +1 −1 −1 −1 −1 0.61 100

10 −1 +1 −1 −1 +1 −1 0.38 200

11 −1 +1 −1 +1 −1 −1 1.19 100

12 −1 +1 −1 +1 +1 −1 1.46 100

13 −1 +1 +1 −1 −1 1 0.75 100

14 −1 +1 +1 −1 +1 1 0.88 100

15 −1 +1 +1 +1 −1 1 0.75 100

16 −1 +1 +1 +1 +1 −1 0.55 100

17 +1 −1 −1 −1 −1 −1 3.74 200

18 +1 −1 −1 −1 +1 −1 4.70 400

19 +1 −1 −1 +1 −1 −1 3.37 200

20 +1 −1 −1 +1 +1 −1 3.12 300

21 +1 −1 +1 −1 −1 −1 2.50 100

22 +1 −1 +1 −1 +1 1 5.27 200

23 +1 −1 +1 +1 −1 1 5.07 100

24 +1 −1 +1 +1 +1 1 4.19 200

25 +1 +1 −1 −1 −1 −1 1.44 200

26 +1 +1 −1 −1 +1 −1 1.64 300

27 +1 +1 −1 +1 −1 −1 1.48 200

28 +1 +1 −1 +1 +1 −1 3.08 400

29 +1 +1 +1 −1 −1 −1 1.17 100

30 +1 +1 +1 −1 +1 −1 1.37 100

31 +1 +1 +1 +1 −1 1 0.91 100

32 +1 +1 +1 +1 +1 1 1.27 100

33 −2 0 0 0 0 0 0.10 100

34 2 0 0 0 0 0 5.20 200

35 0 −2 0 0 0 0 3.30 100

36 0 2 0 0 0 0 1.30 100

37 0 0 −2 0 0 0 0.77 200

38 0 0 2 0 0 0 3.67 100

39 0 0 0 −2 0 0 1.50 100

40 0 0 0 2 0 0 2.47 100

41 0 0 0 0 −2 0 0 100

42 0 0 0 0 2 0 1.74 200

43 0 0 0 0 0 −2 0 100

44 0 0 0 0 0 2 0.76 100

45 0 0 0 0 0 0 4.00 100

46 0 0 0 0 0 0 4.80 100

J Appl Phycol (2012) 24:915–926 919

coefficient was, and its effect on enzymatic hydrolysis of G.verrucosa (Table 5). The p values were used as a tool tocheck the significance of each of the coefficients and tounderstand the interactions between the best variables.Positive coefficients for x1 (temperature) and x6 (pH) indicatea linear effect with respect to increased RPE, whilst anegative coefficient of x2 (time) revealed the opposite effect.The quadric effect of incubation temperature, incubation time,ratio of buffer to raw material, cellulase loading, xylanaseloading, and pH also had a significant effect (p<0.05).

To determine the shape of the fitted response andthe estimated stationary point, the canonical analysisof response surface was performed with STATISTICAsoftware. Eq. 7 was transformed into its canonicalform, which is shown in Eq. 8. According to themodel, the predicted response at the stationary pointx1 ¼ 1:82; x2 ¼ �1:48; x3 ¼ 0; x4 ¼ 0; x5 ¼ 0; x6 ¼ 0:48ð Þwas 6.25 mg g−1. The coefficients of Eq. 8 are eigenvaluesbased on coded data, and Y is the RPE yield (mg g−1). Sinceall coefficients of the above equation are negative, theresponse surface is suggested to have a maximum point. If thestationary point is far outside the region of exploration forfitting the second-order model and one or more eigenvaluesare near zero, another canonical form may be helpful.

Y ¼ 6:25� 0:001w21 � 0:52w2

2 � 0:65w23 � 0:708w2

4

� 0:909w22 � 1:176w2

6 ð8ÞTo investigate the interactive effects of variables for RPE

production, the response surface and contour plots were

generated as graphical representations of the regressionequation. The three-dimensional response surface and theircorresponding contour plots for the RPE against any twoindependent variables while keeping the other independentvariable at zero levels are presented in Figs. 2, 3, 4, and 5.The shapes of the contour plots, circular or elliptical,indicate whether the mutual interactions between thevariables are significant or not. A circular contour plotindicates that the interactions between the correspondingvariables are negligible, whilst elliptical contour plotindicates that the interactions between the correspondingvariables are significant.

Figure 2a shows the response surface and contours of theRPE yield as a function of incubation time and temperaturewhen the other variables were kept constant. From theresponse surface curves and contours, it is easy to interpretthe interaction effects between incubation time and temper-ature. One can see that RPE yield increases with increasingincubation temperature. RPE content follows a similar trendwhen the incubation time decreases. It can be concludedthat the shorter incubation time and higher temperaturecontribute to large RPE yield. Additionally, RPE yield wassensitive even when subjected to small time and tempera-ture alterations. Similarly, the interaction effects plotted forincubation temperature and others parameters showed thatthere are no significant interactions between these variablesthat affect RPE yield.

The effects of substrate loading and enzymes concentra-tion on the hydrolysis yield of RPE, when the other factorswere at their center points, are shown in Fig. 3. Substrateconcentration is considered to be one of the major factorsaffecting the conversion rate of enzymatic hydrolysis ofcellulose. The hydrolysis yield decreases with an increasein substrate concentration from 20 to 30 (w/v) for bothcellulase (Fig. 3a) and xylanase (Fig. 3b).

The effects of cellulase and xylanase loading on thehydrolysis yield of RPE are shown in Fig. 4a, b. Whencellulase loading was low, hydrolysis yield was low.Significant improvement in the hydrolysis yield could beobtained by increasing the amount of cellulase to someextent. When cellulase loading was low, increased xylanaseconcentration resulted in increased hydrolysis yield.

The effects of hydrolysis time and pH on RPE yield,when the other factors were at their center points, areshown in Fig. 5. As can be deduced from this figure, theinteractions of extraction time and pH had a significanteffect on the RPE yield. However, extraction time actdifferently between the low and high pH values. Yield ofRPE improved with decreasing extraction time (x2). Furtherincreasing extraction time led to a slight decrease in RPEyield. Hydrolysis time had a pronounced effect on enzymaticdigestion of G. verrucosa at central levels of pH. At highpH levels, prolonging the reaction time within the tested

Table 4 Analysis of variance of second-order polynomial model forthe optimization of R-phycoerythrin yield by the enzymatic hydrolysis

SS df MS F p

Blocks 6.319 1 6.319 14.722 0.001

x1 32.493 1 32.493 75.705 0.000

x2 16.332 1 16.332 38.051 0.000

x6 6.311 1 6.311 14.704 0.001

x12 3.063 1 3.063 7.135 0.012

x22 4.410 1 4.410 10.275 0.003

x32 4.768 1 4.768 11.108 0.002

x42 5.832 1 5.832 13.588 0.001

x52 12.454 1 12.454 29.016 0.000

x62 16.164 1 16.164 37.659 0.000

x1*x2 6.227 1 6.227 14.508 0.001

x2*x6 2.383 1 2.383 5.551 0.025

Lack of fit 13.844 32 0.433 1.352 0.604

Error residual 14.164 33 0.429

Pure error 0.320 1 0.320

Total SS 107.985 45

R2 =0.86

920 J Appl Phycol (2012) 24:915–926

range only resulted in a decrease in enzymatic hydrolysis ofG. verrucosa.

Desirability function approach

Total sugar production as generated by enzymatichydrolysis was the second response studied. Themathematical model representing the total sugar concen-tration in the experimental region studied can beexpressed by Eq. 9:

Y2 ¼ 137:85þ 38:31x1 � 41:68x3 þ 33:31x5

� 29:14x1x3 � 22:89x3x5 þ 16:28x5x6 ð9ÞTotal sugar, as the main by-product in RPE production

which accounts for the important part of production cost aswe have mentioned in the introduction, was considered inthis step. DFA was used to obtain extraction conditions ofRPE which could give a good compromise betweenmaximizing RPE yield and minimizing total sugar quantityin the medium.

By applying DFA, these two responses were optimizedsimultaneously. With the aid of the STATISTICA software,the new optimal condition (OC-2) was predicted as fellows:incubation temperature (30°C), incubation time (12 h), ratioof buffer to raw material (20, w/v), cellulase loading (15%),xylanase loading (5%), and pH (6). The maximumdesirability D was 0.94.

Validation of the model was carried out in shakeflasks under conditions predicted by the software. Agood correlation can be seen between the experimentaland the predicted values, and hence, the model wassuccessfully validated. Validation of the statistical modeland regression equation was performed by usingx1(48°C), x2 (6 h), x3 (20, w/v), x4 (15%), x5, (5%), andx6 (6.5) in the experiment. The predicted and the actual

(experimental) responses of RPE (5.9 and 6.25 mg g−1,respectively) were comparable.

Discussion

Only few studies have been undertaken on the enzymaticliquefaction of red seaweeds such as Palmaria palmata,Gracilaria sp., and Chondrus crispus (Lahaye andVigouroux 1992; Fleurence et al. 1995; Deniaud et al.2003; Fleurence 2003). They have particularly demon-strated the effect of polysaccharidases on protein extrac-tion from the red alga P. palmata (Fleurence et al. 1995;Fleurence 1999a, b) and concluded that enzymatictreatment may represent an efficient process for facilitat-ing access to RPE (Fleurence 2003). In this study, we haveconfirmed the RPE yield increase from G. verucosa usingenzymatic hydrolysis. This method led to a superior yieldof RPE recovery obtained by enzymatic hydrolysis(Abdeladhim 2005) or classical methods for Gracilaria(Wang 2002; Abdeladhim 2005) and other species (Rossanoet al. 2003; Niu et al. 2007; Denis et al. 2009).

In red algae, unwanted protein–polysaccharides (anionicpolysaccharides) interactions limit extraction efficiency ofwater-soluble protein (Fleurence 1999a; Barbarino andLourenço 2005). Since Gracilaria cell walls are mostlyagar in composition (Kloareg and Quatrano 1988; Craigie1990), temperature augmentation permits their solubilityand enhances the liberation of cellular content. Thesefindings are in accordance with Fleurence et al. (1995)who indicate that agar degradation of G. verrucosa canimprove protein extraction. On the other hand, 40°Crepresents the optimum temperature for cellulase andxylanase activities (Collins et al. 2005). High initialhydrolysis rate was probably due to the absence of ligninin the cell walls of the alga. In highly lignified materials,

Table 5 Estimated regressioncoefficients, t, p, and standarddeviation for R-phycoerythrinyield from Gracilaria verrucosaby enzymatic hydrolysis usingresponse surface

R2=0.86

Coefficient Estimated coefficient Standard coefficient t value p value

β0 5.13 0.599 8.575 0.000000

β1 0.91 0.192 −3.836 0.000000

β2 −0.64 0.103 −6.168 0.011646

β6 0.40 0.105 3.834 0.000537

β11 −0.43 0.163 −2.671 0.011646

β22 −0.52 0.163 −3.205 0.002990

β33 −0.54 0.163 −3.332 0.002130

β44 −0.60 0.163 −3.686 0.000812

β55 −0.88 0.163 −5.386 0.000006

β66 −1.05 0.163 −6.136 0.000001

β12 −0.44 0.117 −3.808 0.000577

β26 −0.27 0.118 −2.356 0.024559

J Appl Phycol (2012) 24:915–926 921

Fig. 2 Three-dimensional contour plots for the maximum RPE yield.RSM plots were generated using the data shown in Table 3. Inputswere the 46 experimental runs carried out under the conditionsestablished by the CCD design. a RPE yield (mg g−1) as a function oftemperature and time. b RPE yield (mg g−1) as a function of

temperature and ratio. c RPE yield (mg g−1) as a function oftemperature and cellulase. d RPE yield (mg g−1) as a function oftemperature and xylanase. e RPE yield (mg g−1) as a function oftemperature and PH. The value of the missing independent variable ineach plot was kept at the center point

922 J Appl Phycol (2012) 24:915–926

the hydrolysis period normally ranges from 24 to 48 h. Ourdata also show that the hydrolysis rate was directlyproportional to the enzyme loading. However, for industrialapplications it is desirable to keep the enzyme dosage lowto reduce enzyme production costs and filtration problemsdue to major macrostructural modifications. Our resultsagree with those of San Martin et al. (1988), who found thatcellulase was very effective against algal cellulose, as mosthydrolysis occurred during the first hour of reaction. Anincrease of incubation time in excess to six hours does not

appear to improve the protein extraction from G. verrucosa.This may be due to the cellulase losing activity duringhydrolysis, which was attributed to reversible and irrevers-ible adsorption. Soluble enzyme activity during hydrolysishas been investigated for substrate other than alga. In thesecases irreversible adsorption to non-degradable cellulose,thermal inactivation and shear stress caused an undesirabledeactivation of cellulase in hydrolysis (Lee and Fan 1983;Deeble and Lee 1985; Elias and Joshi 1998). In our system,deactivated cellulase due to shear stress and thermal

Fig. 3 Three-dimensional contour plots for the maximum RPE yield.RSM plots were generated using the data shown in Table 3. Inputswere the 46 experimental runs carried out under the conditionsestablished by the CCD design. a RPE yield (mg g−1) as a function of

ratio and cellulase. b RPE yield (mg g−1) as a function of ratio andxylanase. The value of the missing independent variable in each plotwas kept at the center point

Fig. 4 Three-dimensional contour plots for the maximum RPE yield.RSM plots were generated using the data shown in Table 3. Inputswere the 46 experimental runs carried out under the conditionsestablished by the CCD design. a RPE yield (mg g−1) as a function of

cellulase and xylanase. b RPE yield (mg g−1) as a function of cellulaseand PH. The value of the missing independent variable in each plotwas kept at the center point

J Appl Phycol (2012) 24:915–926 923

stability could be negligible since the stir speed was lowand the temperature was maintained at optimal temperaturefor cellulase activity.

Previous studies found that high substrate concentrationresulted in low hydrolysis yield due to enzymes deactiva-tion and decrease in substrate accessibility and reactivity(Bansal et al. 2009). The contour plot shows that a lowerRPE yield was obtained at lower substrate concentrationwith addition of a high level of enzymes. Whilst anintermediate concentration of enzymes are needed to obtaina high hydrolysis yield up to a point from which any furtherincrease in enzymes concentration led to decreased hydro-lysis yield for both cellulase and xylanase. This may becaused by decreased enzymatic activity, enzyme becomestuck on the substrate surface when surrounding cellulose

chains prevent further processive action (Väljamäe et al.1998; Yang et al. 2006).

The effects of xylanase supplementation on the RPEyield were evaluated by subjecting the algae to differentloadings of cellulase and xylanase. Xylanase is known toexert beneficial effects on cellulose hydrolysis by degradingheterogeneous xylan polymers that shield cellulose fibers interrestrial plants (Allen et al. 2001; Ishizawa et al. 2007).Indeed, the addition of xylanase to cellulase has beenconducted in attempts to enhance proteins extraction fromred algae (Fleurence et al. 1995; Abdeladhim 2005). In ourstudy, when high or low levels of cellulase such as 0% or30% were used, the xylanase supplement exerted nonoticeable synergistic effect on RPE yield. However, higherRPE yields were achieved when additional xylanase

Fig. 5 Three-dimensional contour plots for the maximum RPE yield.RSM plots were generated using the data shown in Table 3. Inputswere the 46 experimental runs carried out under the conditionsestablished by the CCD design. a RPE yield (mg g−1) as a function of

time and ratio. b RPE yield (mg g−1) as a function of time andcellulase. c RPE yield (mg g−1) as a function of time and xylanase. dRPE yield (mg g−1) as a function of time and PH. The value of themissing independent variable in each plot was kept at the center point

924 J Appl Phycol (2012) 24:915–926

amounts were used in conjunction with cellulase only up tothe midpoint level. Furthermore, neither cellulase norxylanase alone enhance RPE yields. Firstly, there wassufficient evidence to demonstrate that xylan degradationby the supplemental xylanase enhanced the cellulosehydrolysis. Secondly, a fixed proportion of cellulases andxylanses was necessary for cellulose hydrolysis.

In biotechnological processes, the extraction processinfluences the purification one. However, high concentra-tions of total sugar would increase difficulties during RPEpurification by chromatography methods (Wang 2002).Thus, the extraction method should have a good balancebetween a high RPE yield and a low concentration of totalsugar. Alternatively, we can avoid this problem by usingaqueous two-phase system for RPE purification (Mensi,data not published). Any future method of RPE purificationshould introduce a desirability function that does or doesnot take into account the quantity of total sugar.

By applying DFA, the responses were optimized simul-taneously. In our DFA study, for the first time, the totalsugar quantity as a constrained variable was used to selectthe most reasonable weight of the response variable. Withthe aid of the STATISTICA software, the optimal extractioncondition (OC-2) with an incubation temperature (30°C),incubation time (12 h), ratio of buffer to raw material (20,w/v), cellulase loading (15%), xylanase loading (5%), andpH (6) was predicted using the desirability function. Themaximum desirability D was 0.94. The performance of OC-2 differed from OC-1. The RPE yield from RSM was6.25 mg g−1 whilst it was reduced to 5.85mg g−1 in DFA.Thus, the difference of RPE yield between RSM and DFAwas 0.4 mg, corresponding to 4.0% of the RPE yield fromRSM. In addition, the difference of total sugar concentra-tion between RSM and DFA was 85 mg g−1, correspondingto 40% of the total sugar concentration from RSM.

In conclusion and to the best of our knowledge, our work isa novel approach to enzymatic hydrolysis optimization of RPE.A response surface methodology and central composite designapproach found that enzymatic hydrolysis enhance RPE yieldfrom the red alga G. verrucosa. It can be concluded that shortincubation time and higher temperature contributes to largeRPE yield. In addition, a synergic effect of cellulase andxylanase on RPE yield was shown. In this study, combiningRSM and DFA by balancing two response variables simulta-neously effectively optimized the RPE yield. After optimiza-tion, total sugar quantity, as the main by-product, was reducedgreatly. We can conclude that the method developed in ourwork will contribute to reducing RPE costs.

Acknowledgments We are very grateful to Doctor Ktari Leila whohelped with our discussion and provide editing advice for themanuscript. The authors are extremely grateful to Mr. Rajeb Chokri,Director of “Centre Technique d’Agroalimentaire (CTAA)”, for hishelp with our analytical analysis.

References

Abalde J, Betancourt L, Torres E, Cid A, Barwell C (1998)Purification and characterization of phycocyanin from the marinecyanobacterium Synechococcus sp. IO9201. Plant Sci 136:109–120

Abdeladhim L (2005) Contribution à l’identification taxonomique et àla valorisation des protéines des Gracilaires de Tunisie (lac Nordde Tunis et la lagune de Bizerte) par les outils de biologiemoléculaire. Master’s thesis, Institut National Agronomique deTunisie, Tunisia

Allen SG, Schulman D, Lichwa J, Antal MJ, Jennings E, Elander R(2001) A comparison of aqueous and dilute-acid single-temperature pretreatment of yellow poplar sawdust. Ind EngChem Res 40:2352–2361

Bansal P, Mélanie H, Matthew JR, Jay HL, Andreas SB (2009)Modeling cellulase kinetics on lignocellulosic substrates. Bio-technol Adv 27:833–848

Barbarino E, Lourenço SO (2005) An evaluation of methods forextraction and quantification of protein from marine macro- andmicroalgae. J Appl Phycol 17:447–460

Collins T, Gerady C, Feller G (2005) Xylanases, xylanase families andextremophilic xylanases. FEMS Microbiol Rev 29:3–23

Craigie JS (1990) Cell wall. In: Cole KM, Sheath RG (eds) Biology of thered algae. Cambridge University Press, New York, pp 221–257

Deeble MF, Lee MJ (1985) Enzymatic hydrolysis of cellulosicsubstances in an attrition bioreactor. Biotechnol Bioeng Symp15:277–293

Deniaud E, Fleurence J, Lahaye M (2003) Preparation and chemicalcharacterization of cell wall fractions enriched in structuralproteins from Palmaria palmata (Rhodophyta). Bot Mar46:366–377

Denis C, Michèle M, Pierre G, Fleurence J (2009) Effect of enzymaticdigestion on thallus degradation and extraction of hydrosolublecompounds from Grateloupia turuturu. Bot Mar 52:262–267

Derringer G, Suich R (1980) Simultaneous optimization of severalresponse variables. J Qual Technol 12:214–219

Dubois M, Gilles JK, Hamilton PA, Rebers FS (1956) Colorimetricmethod for determination of sugars and related substances. AnalChem 28:350–356

Elias CB, Joshi JB (1998) Role of hydrodynamic shear on activity andstructure of proteins. Adv Biochem Eng Biotechnol 59:47–71

Fleurence J (1999a) The enzymatic degradation of algal cell walls: auseful approach for improving protein accessibility? J ApplPhycol 11:313–314

Fleurence J (1999b) Seaweed proteins: biochemical, nutritionalaspects and potential uses. Trends Food Sci Technol 10:25–28

Fleurence J (2003) R-phycoerythrin from red macroalgae: strategiesfor extraction and potential application in biotechnology. ApplBiotech Food Sci Pol 1:63–68

Fleurence J, Guyader O (1995) Contribution of electrophoresis to theidentification of red seaweeds (Gracilaria sp.) used as foodingredients. Sci Aliments 15:43–48

Fleurence J, Massiani L, Guyader O, Mabeau S (1995) Use ofenzymatic cell wall degradation for improvement of proteinextraction from Chondrus crispus, Gracilaria verrucosa andPalmaria palmata. J Appl Phycol 7:393–397

Glazer AJ, Sttryer L (1984) Phycofluor probes. Trends Biochem Sci9:423–427

Ishizawa CI, Davis MF, Schell DF, Johnson DK (2007) Porosity andits effect on the digestibility of dilute sulfuric acid pretreated cornstover. J Agric Food Chem 55:2575–2581

Joubert Y, Fleurence J (2008) Simultaneous extraction of proteins andDNA by an enzymatic treatment of the cell wall of Palmariapalmata (Rhodophyta). J Appl Phycol 20:55–61

J Appl Phycol (2012) 24:915–926 925

Kloareg B, Quatrano RS (1988) Structure of the cell walls of marinealgae and ecophysiological functions of the matrix polysacchar-ides. Oceanogr Mar Biol Ann Rev 26:259–315

Kula MR, Schutte H (1987) Purification of proteins and the disruptionof microbial cells. Biotechnol Prog 3:31–42

Lahaye M, Vigouroux J (1992) Liquefaction of dulse (Palmariapalmata (L.) Kuntze) by a commercial enzyme preparationand a purified endo-β-1,4-D-xylanase. J Appl Phycol 4:329–337

Lee Y, Fan LT (1983) Kinetic studies of enzymatic hydrolysis ofinsoluble cellulose: (II) analysis of extended hydrolysis times.Biotechnol Bioeng 25:939–966

Montgomery DC (1997) Design and analysis of experiments, 4th edn.Wiley, New York

Myers RH, Montgomery DC (2002) Response surface methodology:process and product optimization using designed experiments,2nd edn. Wiley, New York

Niu JF, Wang GC, Zhou BC, Lin XZ, Chen CS (2007)Purification of R-phycoerythrin from Porphyra haitanensis(Bangiales, Rhodophyta) using expanded-bed absorption. JPhycol 43:1339–1347

Rossano R, Ungaro N, D’Ambrosio A, Liuzzi GM, Riccio P (2003)Extracting and purifying R-phycoerythrin from Mediterraneanred algae Corallina elongata Ellis & Solander. J Biotechnol101:289–292

San Martin R, Aguilera JM, Hohlberg AI (1988) Effect of cellulasepretreatments on red algae agar extractability. Carbohydr Polym8:33–43

Sangkharak K, Prasertsan P (2007) Optimization of polyhydroxybu-tyrate production from a wild type and two mutant strains ofRhodobacter sphaeroides using statistical method. J Biotechnol132:331–340

Sekar S, Chandramohan M (2008) Phycobiliproteins as a commoditytrends in applied research, patents and commercialization. J ApplPhycol 20:113–136

Siegelman HW, Kycia JH (1978) Algal biliproteins. In: Hellebust JA,Craisie JS (eds) Handbook of phycological methods. CambridgeUniversity Press, Cambridge, pp 71–79

Tchernov AA, Minkova KM, Georgiev DI, Houbavenska NB (1993)Method for B-phycoerythrin purification from Porphyridiumcruentum. Biotechnol Tech 7:853–858

Väljamäe P, Sild V, Pettersson G, Johansson G (1998) The initial kineticsof hydrolysis by cellobiohydrolases I and II is consistent with acellulose surface erosion model. Eur J Biochem 253:469–475

Wang G (2002) Isolation and purification of phycoerythrin from redalga Gracilaria verrucosa by expended-bed-adsorption and ion-exchange chromatography. Chromatographia 56:509–513

Yang S, Ding W, Chen H (2006) Enzymatic hydrolysis of rice straw ina tubular reactor coupled with UF membrane. Process Biochem41:721–725

926 J Appl Phycol (2012) 24:915–926