Biochemical Engineering Journal 30 (2006) 260–268

Recovery of medium-chain-length polyhydroxyalkanoates (PHAs)through enzymatic digestion treatments and ultrafiltration

K. Yasotha a, M.K. Aroua a,∗, K.B. Ramachandran b, I.K.P. Tan c

a Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysiab Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600036, India

c Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia

Received 29 March 2006; received in revised form 29 April 2006; accepted 19 May 2006

Abstract

Medium-chain-length (mcl) polyhydroxyalkanoates (PHAs) are biodegradable polyesters accumulated intracellularly as energy resources bybacterial species such as Pseudomonas putida. The most popular method for PHA recovery in the downstream processing is solvent extraction usingchloroform and methanol. An alternate method is bioseparation using enzymatic digestion process which eliminates the need for hazardous solvents.This research focuses on an attempt to optimize the recovery of PHAs by solubilisation of non-PHA granules through enzymatic treatments suchacoadca©

K

1

wfomarpPapemvh

1d

s; Alcalase (to digest the denatured proteins), sodium dodecyl sulfate (SDS) to assist solubilisation, ethylene diamine tetra acetic acid (EDTA) toomplex divalent cations and lysozyme to digest the peptidoglycan wall enveloping the cell. The experiment was designed through Taguchi’s designf experiment (DOE) using Qualitek-4 software. The results show that Alcalase enzyme used had the most significant effect on the treatment processnd contributed to about 71.5% in terms of process factor importance among the different factors on treatment performance for PHA recovery. It isesired to recover the PHA granules in water suspension after the enzymatic treatment by removing the solubilised non-PHA cell material throughrossflow ultrafiltration system and purified through continuous diafiltration process. Final purity of PHA in water suspension obtained using GCnalysis is 92.6%, with a nearly 90% recovery, thus concluding that this method is indeed a suitable alternative.

2006 Elsevier B.V. All rights reserved.

eywords: mcl-Polyhydroxyalkanoate; Downstream processing; Bioseparation; Enzymatic digestion; Taguchi’s method; Ultrafiltration and continuous diafiltration

. Introduction

Polyhydroxyalkanoates (PHAs) are biodegradable polyestershich are accumulated by numerous bacterial species in the

orm of intracellular granules and which serve as reservesf energy [12]. PHAs have sparked global interest due to itsany advantages such as thermoplastic properties, biodegrad-

bility, biocompatibility and its ability to be synthesized fromenewable resources. The medium-chain-length PHAs (com-rising C6 to C16 monomers) produced by the Gram-negativeseudomonas are very much in demand due to the flexiblend elastomeric applications in the global market. The mostopular PHA recovery method is carried out using the solventxtraction method, employing solvents such as chloroform andethanol [6]. This method requires large quantities of these

olatile solvents which are not only cost prohibiting but alsoazardous to the environment.

∗ Corresponding author. Tel.: +60 3 79675313; fax: +60 3 79675319.E-mail address: mk aroua@um.edu.my (M.K. Aroua).

Another popular method is the use of sodium hypochloritewhich solubilises non-PHA cellular materials and leaves PHAintact. Then, PHA can be separated from the solution by cen-trifugation. Berger et al. [2] reported a severe degradation ofthe polymer of up to 50% reduction in molecular weight duringsodium hypochlorite digestion [14]. This technology is simplea process, however, in view of the marked decrease in molec-ular weight of PHA due to sodium hypochlorite being a strongoxidant, and the appreciable amount of chlorine left behind inthe recovered PHA, this technology has been modified by manyresearchers. Such variations include using a dispersion solutionmade of sodium hypochlorite and chloroform as studied by Hahnet al. [11]. However, this also leads to a higher cost and environ-mental hazard.

One separation method that is both gentle (100% biological)and selective, is through enzymatic digestion. Various studies inrecovery process through enzymatic digestion treatments havebeen taken upon by many researchers [8–10,12].

In the enzymatic digestion process for recovery of PHAs,heat shock is employed by means of sterilizing the fermentation

369-703X/$ – see front matter © 2006 Elsevier B.V. All rights reserved.

oi:10.1016/j.bej.2006.05.008

K. Yasotha et al. / Biochemical Engineering Journal 30 (2006) 260–268 261

broth for short period of time. The heat shock enables the cellsto be ruptured as well as the polynucleic acids solubilised anddenatured, thereby preventing a detrimental increase in mediumviscosity. Then, the solubilisation of non-PHA cell material canbe effected through the various enzymatic treatments [12]. Theprotease enzyme (Alcalase) was found to be effective in digest-ing the denatured nucleic acids and proteins [8]. The use ofanionic detergent such as sodium dodecyl sulfate (SDS) candecompose any insoluble matters such as protein and lipids andsolubilise the components by incorporation in micelles [22]. deKoning and Witholt [8] found in their research that reaction ofAlcalase and SDS simultaneously bore no synergistic effect atthe optimum pH and temperature condition of Alcalase, andas such this leads to considerable time savings since the reac-tions can be carried out simultaneously. Further treatments withethylene diamine tetra acetic acid (EDTA) enables chelation ofdivalent cations as well as degradation of the lipopolysacchar-ride layer enveloping membrane of the cell wall. Lysozyme iseffective in degrading the peptidoglycan wall surrounding thePHA granule [10]. However, releasing this wall, increases vis-cosity, and as such, a lower concentration of lysozyme shouldsuffice.

In the study carried out by de Koning et al. [9], the PHAwas produced using sodium octanoate as the carbon source aswell as different genera of the Pseudomonas putida strain fromthe one used in this study. The authors recovered the PHA bydTc%fiteavfnsatmastnrdi

trpsstfi

concentration that will minimize the total time for ultrafiltra-tion/diafiltration. This approach was adopted in this study toenable the process to be carried out at an optimum concentra-tion which minimizes time and process volume of diafiltrationbuffer needed, thus leading to more cost effectiveness.

2. Materials and methods

In this study, shake flask experiments were conducted withP. putida as the bacterial species. P. putida was cultivated innutrient-rich medium [7] for 24 h in a shaker incubator at 30 ◦Cat 240 rpm. The culture 0.5% (v/v) were then transferred intoa 1000 mL Erlenmeyer flask containing 300 mL modified-Rmedium [13] plus 10 g/L of oleic acid as the carbon source. Theflask was incubated at 30 ◦C with a shaker speed of 240 rpmfor a fermentation period of 48 h [20]. Cells were harvested bycentrifuging the broth and re-suspending the cells in water. Thissuspension was then subjected to heat treatment by autoclav-ing at 121 ◦C for 1 min prior to enzymatic treatments. The cellweight was predetermined by drying a portion of cells at 80 ◦Cuntil constant weight was achieved. The harvested suspensionwas initially subjected to digestion with Alcalase and SDS at pH8.5 and temperature of 55 ◦C at the time duration, which wasoptimized through Taguchi’s method (refer to Section 3). Thiswas followed by further treatments with EDTA and lysozymeat pH 7 and temperature of 30 ◦C for 15 min. The digestionwmbmd

2

pa5S

2

eiwp

2t

dTtoSd

igestion with excess amounts of Alcalase, SDS and EDTA.he PHA granules were then recovered through filtration pro-ess using ceramic tubular membrane. They managed to getPHA purity that exceeded 95%. The aim of this research is

ocused on incorporating the suitable treatments based on stud-es by previous researchers [8–10,12] and to optimize the bestreatment strategy by means of enzymatic digestion for recov-ry of PHA produced by P. putida PGA1 grown on oleic acids carbon source. For conventional optimization strategies, eachariable to be tested requires independent testing. Thus, to per-orm an optimization study, it is necessary to conduct a largeumber of costly and labour intensive experiments. Thus, in thistudy, Taguchi’s method [21] was employed as a tool for system-tic design of experiment (DOE) with the purpose of optimizinghe enzymatic digestion treatment. Taguchi’s approach comple-

ents two important areas; firstly, standard sets of orthogonalrrays which can be used for many experimental situation andecondly, a standard method for analysis of results based onhe signal-to-noise (S/N) ratio. This ratio provides a mecha-ism to calculate the robustness of a combination, given a set ofesponses from replicated trials [16]. This is because variationsue to noise factors (uncontrollable factors such as manufactur-ng differences, etc.) are taken into account during calculations.

Once the optimum conditions for the enzymatic digestionreatments was decided, the solubilised non-PHA cell mate-ial was removed through crossflow ultrafiltration system withurification of PHA in water suspension through diafiltrationystem in a continuous mode, by replacing the non-PHA sub-tances with water. The optimum concentration to start the con-inuous diafiltration (CD) was determined based on the stagnantlm model used by Ng et al. [15] to evaluate the bulk protein

as performed using the experimental set-up using Taguchi’sethod to determine the optimum condition. This was achieved

y centrifuging the treated medium and carrying out gas chro-atographic analysis [3] of the lyophilized solid fraction for

etermination of %PHA purity.

.1. Enzyme

The commercial protease Alcalase enzyme was kindly sup-lied by Novozymes. The optimum activity for the enzyme ischieved at the pH range of 6.5–8.5 and at temperature range of5–70 ◦C. The declared activity is 2.4 AU/g (Alcalase Productheet, Novozymes).

.2. Design of the experimental set-up

Taguchi’s design of experiment was used in implementing thexperimental set-up of the enzymatic treatments. The enzymat-cally treated samples were centrifuged and the pellets obtainedere determined of the %PHA purity (which was the responsearameter measured through gas chromatography analysis).

.2.1. Optimization of enzymatic digestion treatmentshrough Taguchi’s method

The optimum concentrations of the treating agents as well asigestion period for the enzymatic treatment is achieved throughaguchi’s method as described earlier. As such there are five con-

rol factors to be taken into account, namely the concentrationsf Alcalase (referring to Alcalase activity per gram biomass),DS, EDTA and lysozyme as well as time (which refers to theigestion period of simultaneous reaction of Alcalase and SDS).

262 K. Yasotha et al. / Biochemical Engineering Journal 30 (2006) 260–268

Table 1Factor-level settings

Factors Level 1 Level 2 Level 3 Level 4

A Alcalase (AU/g CDW) 0.1 0.2 0.3 0.4B SDS (g/g CDW) 0.04 0.06 0.08 0.10C EDTA (g/g CDW) 0.20 0.40 0.60 0.80D Lysozyme (g/g CDW) 0.0025 0.0050 0.0075 0.0100E Time (min) 20 40 60 Nil

AU, Anson units; CDW, cell dry weight.

In this study, the noise factor chosen is the biomass concentra-tion or cell density of the fermentation broth, which varies dueto certain uncontrollable conditions during fermentation and isespecially true in larger scale fermentation. This is not so appar-ent when it comes to shake flasks fermentation due to the lowvolume and less intrusive factors. However, this factor is crucialto ascertain the robustness of the enzymatic treatment to deter-mine the effectiveness of the treatment process for various celldensities of the produced fermentation broth.

The noise factor is divided into two levels, the first levelbeing cell density of broth obtained from fermentation in shakeflasks, which is around 3.6 g/L, and the second level being celldensity of broth obtained from fermentation in a 5 L bioreac-tor, which is around 44 g/L. This stark contrast in cell densi-ties is to ensure the suitability of this treatment against vary-ing nature of the fermentation broths, aggravated further atlarge scale conditions. The PHA content of cells produced inshake flasks is generally 18% cell dry weight (CDW), whereasthe PHA content of cells produced from the 5 L bioreactor isaround 34% CDW. The PHA produced in the 5 L bioreactor,was under more controlled conditions and fermentation wasconducted in fed-batch mode, thus giving a better cell densityas well as PHA content (wt%), compared to the PHA pro-duced in shake flasks. Gas chromatography analysis revealedthat the monomer composition of the PHA produced in thisresearch is typically as follows: 36 mol% of 3-hydroxyoctanoate(h2t5

cda7T

2

ta

2

t

Table 2Orthogonal array table

Runs A B C D E

1 1 1 1 1 12 1 2 2 2 23 1 3 3 3 34 1 4 4 4 45 2 1 2 3 46 2 2 1 4 37 2 3 4 1 28 2 4 3 2 19 3 1 3 4 2

10 3 2 4 3 111 3 3 1 2 412 3 4 2 1 313 4 1 4 2 314 4 2 3 1 415 4 3 2 4 116 4 4 1 3 2

of freedom (DOF) is 14. Since the DOF of an OA should begreater or at least equal to those of the experiment [19], there-fore, the appropriate orthogonal array chosen is the modified L16array (which accounts for 16 experimental runs) as depicted inTable 2. The noise factor is included as an outer array, whichallows the transformation of the results into a signal-to-noiseratio. As the 16 experiments of the modified L16 (M-16) arraywere performed at two noise levels (N1 and N2), and each exper-iments were performed with a replicate, a total of 64 runs werecarried out.

2.2.4. Experimental preparation for enzymatic digestionThe enzymatic digestion treatment was conducted according

to the layout of the modified L16 orthogonal array and at thedifferent noise level settings. The response parameter data whichrefers to the %PHA purity (obtained through GC analysis) wasrecorded for data analysis. The %PHA purity is defined as thepercentage of the ratio of the amount of PHA to the total drymatter after recovery [5].

2.2.5. Analysis of data and performance predictionThe obtained experimental data is processed in the Qualitek-

4 software. As higher %PHA purity is desirable, the optimizedcharacteristic for the response parameter is larger-the-better(LTB). The S/N ratio equation most appropriately used is asf

wuc

fipc

3-HD); 34 mol% of 3-hydroxydecanoate; 13 mol% of 3-ydroxydodecanoate; 15 mol% of 3-hydroxytetradecanoate;mol% of 3-hydroxyhexadecanoate. The monomer composi-

ion is the same in cells produced from shake flasks or from theL bioreactor.

The advantage of Taguchi’s method is that, optimal workingonditions, determined from the laboratory study, can be repro-uced in real applications as exemplified by many researches thatpply this method for their DOE. Qualitek-4 software Version.0 (Nutek Inc., MI, USA) for automatic design and analysis ofaguchi experiments was used in the present study for the DOE.

.2.2. Determination of the control factors and their levelsEvaluation of the factors that could influence on the optimiza-

ion of the enzymatic treatments was first carried out. The factornd level settings are depicted in Table 1.

.2.3. Orthogonal array and noise factor assignmentAs there are four factors with four levels each and one fac-

or with three levels each in this matrix experiment, the degree

ollows [19]:

S

N= −10 log

[1

n

n∑i=1

(1

Y2i

)](1)

here n is the number of repetitions of each trial (n = 2). Thenit of S/N ratio is decibel (db), which is frequently used inommunication engineering.

The data processing consists of determination of optimumactor-level settings for enzymatic treatments, identification ofndividual factors influence on recovery process and to predicterformance at the optimum conditions. The relative signifi-ance of the individual factors on the average S/N ratio was

K. Yasotha et al. / Biochemical Engineering Journal 30 (2006) 260–268 263

Table 3Summary of data processed by Qualitek-4 software

Run Experimental result (%PHA purity)

Noise 1, C = 44 g/L,first trial

Noise 1, C = 44 g/L,second trial

Noise 2, C = 3.6 g/L,first trial

Noise 2, C = 3.6 g/L,second trial

Averageresult

Average S/Nratio, η

#1 21.3 19.8 19.0 20.0 20.0 26.0#2 33.5 34.5 33.7 33.3 33.7 30.6#3 37.8 38.5 37.7 35.7 37.4 31.5#4 34.5 33.6 32.2 32.5 33.2 30.4#5 48.1 47.6 47.8 46.4 47.5 33.5#6 51.5 50.8 50.1 50.6 50.7 34.1#7 58.0 56.0 58.5 57.4 57.5 35.2#8 63.3 62.3 62.4 62.8 62.7 35.9#9 48.9 46.8 47.0 47.8 47.6 33.6#10 53.4 54.2 54.3 53.3 53.8 34.6#11 64.0 63.9 63.9 63.2 63.8 36.1#12 61.2 60.9 61.4 61.6 61.2 35.7#13 45.6 43.4 44.5 45.8 44.8 33.0#14 52.2 51.2 51.1 52.2 51.7 34.3#15 66.4 62.2 65.7 64.9 64.8 36.2#16 59.9 60.5 60.6 60.8 60.5 35.6

Overall average, Y 49.4Average S/N ratio, η 33.5

Table 4ANOVA of S/N ratio for %PHA purity

Factor DOF S V F-R S′′ P%

A 3 82.0 27.3 98.0 81.1 71.5B 3 25.2 8.4 30.1 24.4 21.5C 3 2.7 0.9 3.3 1.9 1.7D 3 3.0 1.0 3.6 2.2 1.9E 2 0.3 0.2 0.6 0 0Error 1 0.3 0.3 3.4

Total 15 113.4 100

quantitatively evaluated using ANOVA. The results obtained inthe data processing are shown in Tables 3–7.

2.2.6. Confirmatory test and performance validationIn order to validate the methodology, the enzymatic treat-

ments were performed in accordance to the optimized conditionand the response parameter (S/N of %PHA purity) was validatedbased on the predicted response.

Table 5ANOVA of S/N ratio for %PHA purity after pooling

Factor DOF S V F-R S′′ P%

A 3 82.0 27.3 98.0 81.1 71.5B 3 25.2 8.4 30.1 24.4 21.5C 3 2.7 0.9 3.3 1.9 1.7D 3 3.0 1.0 3.6 2.2 1.9E (2) (0.3) PooledError (3) 0.6 0.2 2.7

Total 15 113.4 100

S, sum of squares; V, variance; F-R, F-ratio; S′′, total sum of squares; P%,percentage contribution.

Table 6Noise effect on average response (%PHA purity)

Trial condition Average response of Y(%PHA purity)

Noise 1 (first trial) 49.96Noise 1 (second trial) 49.13Noise 2 (first trial) 49.35Noise 2 (second trial) 49.26

2.3. Concentration and purification process

Final recovery of PHA granules in water suspension (afterdetermination of optimized conditions) was achieved throughcrossflow ultrafiltration and diafiltration. The crossflow ultra-filtration equipment (Sartorius, Germany) consisted of a 5 Lstainless steel storage tank with a cooling jacket; a rotary lobefeed pump and a membrane holding device. A 300 kDa ultrafil-tration polyethersulfone Sartocon Slice cassette with a surfacearea of 0.1 m2 was used.

2.3.1. Operating conditions for concentration modeInitial feed volume of 2.2 L enzymatically treated medium

was fed to the membrane and the retentate was recycled backin a continuous concentration mode. The PHA concentrationobtained at each tested volume concentration ratio (VCR) in

Table 7Response data from confirmatory test at optimal factor and level settings

Trial condition Average response of Y(%PHA purity)

S/N ratio

First trial 70.3 36.8Second trial 68.3 36.7

264 K. Yasotha et al. / Biochemical Engineering Journal 30 (2006) 260–268

the retentate tank was determined through gas chromatographyanalysis of the freeze-dried sample.

In order to optimize the conditions of concentration modeprior to continuous diafiltration for purification of PHA inwater suspension, the effects of permeate flux on hydrodynamicparameters such as pressure drop (�P) and transmembrane pres-sure (TMP) was measured. Three levels of �P (0.5, 1.0 and1.5 bar) with four levels of TMP (1.00, 1.25, 1.50 and 1.75 bar)were tested.

Once the optimum �P and TMP had been determined, theoptimum concentration (Copt) to begin CD was determined.Accordingly, the experiment was repeated at the optimizedhydrodynamic conditions and the PHA concentration at eachtested volume concentration ratio was obtained.

Copt was determined through the modified expression derivedby Ng et al. [15] for rejection coefficient, σ close to unity asfollows:

Copt = Cg

σe(2)

where Cg refers to gel concentration of the PHA when the per-meate flux is 0 L/m2 h and e is the exponential factor.

At the identified optimum concentration, CD was applied forfurther purification of PHA in water suspension. Finally, post-concentration of about 1.1 times was performed to obtain thefinal product of PHA in water suspension.

3

3

DSisctdeccnm

AtsapstFciai

Fig. 1. Main effects plot.

was found that time has the least influence which can be seen bycomparing the S values. Thus, this factor should be pooled, as itis not considered significant. By referring to the main effects plot(Fig. 1), where factor time is concerned, there is very little dif-ference of average S/N ratio at each level of the digestion periodof Alcalase and SDS; and this further emphasizes the necessityfor this factor to be pooled. Due to this, the ANOVA terms haveto be recalculated. The recalculation of ANOVA can be doneby establishing the new values for the error term by ignoringthe presence of the time factor. Table 5 depicts the ANOVAresults after pooling. It can be seen that Alcalase concentrationexerts the most influence in the digestion process (whereby thepercentage contribution is 71.5%), followed by concentrationof SDS, EDTA and lysozyme. As the time factor, is ignored(meaning that the minimum 20 min set can be assumed suffi-cient), the optimized factors are levels 3 for Alcalase and SDS,and levels 2 for EDTA and lysozyme with a digestion period of20 min for Alcalase and SDS. Although the percentage contri-bution of EDTA and lysozyme calculated are low compared tothe influence exerted by Alcalase and SDS, their contribution tothe enzymatic treatment is needed and thus cannot be neglected.

The %PHA purity from both the first and second trials ofeach noise factor were averaged (depicted in Table 6) and theaverage results for each noise factor are as follows:

Noise 1: 49.5.

itadecttmbb

. Results and discussion

.1. Data analysis of enzymatic digestion treatments

Qualitek-4 software was used throughout the experiment forOE and computations in the analysis of variance (ANOVA).ince the results of the experiments involve multiple runs and

nclusion of a noise factor, the use of signal-to-noise ratio overtandard analysis (use of average results) is preferred. Thehange in the quality characteristics of a product under inves-igation in response to a factor introduced in the experimentalesign is the “signal” of the desired effect. However, when anxperiment is conducted, the noise factors influence the out-ome. Thus, the S/N ratio measures the sensitivity of the qualityharacteristic being investigated in a controlled manner, to theoise factor not under control [18]. Table 3 depicts the experi-ental results for each run, including the average S/N ratio.The main effects plot depicted in Fig. 1 shows that level 3 of

lcalase and SDS, level 2 of EDTA, lysozyme and time generatehe highest results. In order to determine the control factor thatignificantly affects the quality characteristic, analysis of vari-nce calculations were done through Qualitek-4 software. Theercentage contribution of each process factor in the total sum ofquares can be used to evaluate the importance of the process fac-or change on the performance characteristic. Additionally the

value can be used to determine which process factors signifi-antly affect the performance characteristic. Usually, the changen the process factor significantly affects the performance char-cteristic when F is large [19]. The ANOVA results are shownn Table 4. Based on the ANOVA results computed in Table 4, it

Noise 2: 49.3.

The difference of results from the noise effects which is 0.2,s less than 0.5% off both averages at each noise conditions. Ashere was minimal difference in terms of %PHA purity obtainedt both noise conditions (which differed greatly in terms of cellensity as well as PHA content (wt%)), this shows that theffect of Alcalase concentration (which gave the highest per-entage contribution as calculated from the ANOVA) is indeedhe most significant factor compared to the other factors. Thus,his further shows that the conditions for recovery of PHA are

inimally affected by the cell concentration of the fermentationroth, which implies that this treatment process is suitable foroth small and large scale production of PHA.

K. Yasotha et al. / Biochemical Engineering Journal 30 (2006) 260–268 265

3.2. Performance prediction

From the analysis done, the factors and levels chosen are A3,B3, C2 and D2. Denoting T, as the average S/N ratio (η) of all thetrials done (value as referred to Table 3 equals to 33.5), ηoptimumis predicted from the following equation [19]:

Predicted ηopt. = T + (A3 − T ) + (B3 − T ) + (C2 − T )

+ (D2 − T ) (3)

Substituting the response dataset from Table 3, the predictedηoptimum = 37.1 dB.

To estimate the result in terms of Y = PHA purity (%), with thequality characteristic larger is better, the following calculationswere made using η = 37.1

η = −10 log10(M.S.D.) (4)

where M.S.D. is the mean squared deviation.Substituting η = 37.1 in Eq. (4), M.S.D. = 1.954 × 10−4

M.S.D. = (1/Y2i ) + · · · + (1/Y2

N )(5)

w

∴

w

3

mpciwaom

facqsYi

w

C

where F(1, n2) is the F value from the F table for factor DOFand error DOF (3) at the desired confidence level (in this case90%), Ve the variance of the error term (from ANOVA table),Nver the total trial number and Ne is the effective number of repli-cations:

Ne = Total number of results or S/N

(DOF of mean (always = 1) + DOF of all factors included in estimating the mean performance at optimum condition)(8)

∴ Ne = 16

1 + 12= 1.23

F(1, n2) = 5.5393 (from Fisher table at 90% confidence interval[17]), Ve = 0.204 and Nver = 2.

By substitution of the relevant values, ⇒C.I. = ±1.5.Thus, predicted ηoptimum = 37.1 ± 1.5 (at 90% confidence

interval). This confirms that both η values of 36.8 and 36.7which correspond to %PHA purity of 70.3 and 68.3%, respec-tively (from first and second trials, respectively) obtained duringthe confirmatory test is well within the range of the upper (38.6)and lower limits (35.6) of predicted performance at the optimumcondition at the 90% confidence level. Therefore, the optimumparameter level combination and the factor effects in this exper-iment are valid.

3.4. Discussion on overall performance of enzymatict

ow%7owmapu

3s

tkbacccmspe5t

N

here N is the number of trials, and assuming all Y’s are Yexpected;

M.S.D. = N(1/Yexpected)2

N(6)

here Yexpected ∼= 71.5% PHA purity.

.3. Confirmatory test and performance validation

The purpose of a confirmation trial is to verify that the opti-al factor-level settings actually yield an improvement. If the

redicted S/N ratio is close enough to the observed S/N ratio, wean conclude that the interactions among the factors were notmportant for the study, and that the main effects only approachas considerably successful. On the other hand, if the predicted

nd observed S/N ratio does not match, it is then the indicationf the presence of interactions and therefore further experimentsay be required to verify this [16].A confirmatory test was performed after the optimal control

actor settings were determined. The confirmatory test wasrepetition of the enzymatic digestion experiment with the

ontrol factors set at optimal settings to achieve the predicteduality characteristics. Two trials at the optimal control factorettings were made in the confirmation test. The response data,(%PHA purity) for the confirmatory test done with a replicate,

s depicted in Table 7.The confidence interval (C.I.) value of 10% confidence band

ith the test trial number is calculated as follows [16]:

.I. = ±[F (1, n2) × Ve ×

(1

Ne+ 1

Nver

)]0.5

(7)

reatment

As we can see, the Taguchi’s method proved very effective inptimizing the experimental condition, and the result obtainedas true to a 90% confidence level. However, the maximumPHA purity obtainable in the centrifuged pellet is only about

0%. This is due to limitations in the centrifugation processf which the solid fraction containing PHA do not separateell from the supernatant. This is also due to the fact that thecl-PHA granules have densities close to water and as such

re difficult to centrifuge [8]. Herein, the need of recovery andurification of PHA in water suspension through the crossflowltrafiltration and diafiltration system is demonstrated.

.5. Concentration and purification of PHA in wateruspension

In the research done by de Koning et al. [9], a 0.1 �m ceramicubular membrane was used in a crossflow system. It is widelynown, that ceramic membranes are the best available mem-ranes due to the high resistance to chemical changes as wells their relative inertness to pH, temperature as well as con-entration changes. However, they are also noted for their highapital cost due to their increased ruggedness. Thus, it was per-eived more desirable to carry out the recovery process in aore economically viable as well as sustaining method. As

uch, a 300 kDa ultrafiltration polyethersulfone polymeric flatlate membrane module was used to retain even the small-st PHA granule. The cost of polymeric membrane is about–10 times less than ceramic membranes and so the potential ofhis membrane to recover PHA in water suspension was tested.

266 K. Yasotha et al. / Biochemical Engineering Journal 30 (2006) 260–268

Fig. 2. Effects of pressure drop (�P) and TMP on average permeate flux.

This low cost membrane greatly reduced the acquisition costof membranes. However, a slight disadvantage is that polymericmembranes have a limited life span and if intensively used, mustbe replaced at least once in 1–2 years.

3.5.1. Analysis of optimization of parameters forconcentration mode

The experimental results in determining the optimum pres-sure drop and transmembrane pressure for the crossflow ultrafil-tration operation are summarized in Fig. 2. The curves obtainedas observed in Fig. 2 shows that concentration polarizationoccurred at all pressure drops whereby, the permeate flux levelsdecreases after certain TMP, except for �P which equals to 1 bar.For the highest value of pressure drop tested (�P = 1.5 bar), thepermeate flux was quite independent of the average applied TMPfor values greater than 1.25 bar. Therefore, the optimum condi-tion for maximal permeate flux was �P = 1.5 bar and averageTMP = 1.25 bar.

Concentration polarization is a phenomenon that happens tobe a major limiting factor in pressure-driven membrane pro-cesses such as ultrafiltration. Indeed, the solute retained by themembrane tends to accumulate at the surface of the membraneand constitutes an additional barrier to the passage of the per-meable species. The hydraulic resistance of this layer increaseswith time and reduces the effective permeability of the mem-brane [1], thus accounting for the reduction in average permeatefl

3u

dcuppbasr

Table 8PHA concentration at each tabulated VCR during filtration

VCR Ca total BM (g/L) Ca PHA (g/L) PHA purity (%)

1.00 4.30 1.16 27.01.11 4.52 1.29 28.51.24 4.77 1.44 30.21.39 5.08 1.62 31.91.57 5.40 1.82 33.71.79 5.84 2.08 35.62.08 6.38 2.41 37.82.46 7.08 2.86 40.42.93 7.85 3.40 43.33.60 8.98 4.15 46.24.44 10.45 5.13 49.15.53 12.29 6.43 52.37.26 14.66 8.34 56.9

10.63 18.79 11.60 61.7

BM, biomass.a C, concentration.

the gel concentration (Cg) is known [15]. Cg can be obtainedfrom the semilogarithmic plot of permeate flux versus PHA con-centration in the retentate at each volume concentration ratiowhich is the degree of concentration.

The VCR is calculated as follows [4]:

VCR = V0

Vf(9)

where V0 is the initial volume in the retentate vessel and Vf isthe final volume in the retentate vessel.

The concentration of macrosolute during ultrafiltration cancorrelated to the VCR and characterized from the following Eq.[4]:

Cf = C0(VCR)σ (10)

where C0 is the initial solute concentration in the retentate vessel,Cf the final solute concentration in the retentate vessel and σ isthe overall rejection coefficient of the solute.

The PHA concentration in retentate was determined throughGC analysis at each tabulated VCR as shown in Table 8. Thesedata were then used to graph the semilogarithmic plot as depictedin Fig. 4.

It was our primary goal to remove the undesirable low molec-ular weight permeable solutes; as such the %purity (P) of theretained PHA granules in the retentate (as depicted in Table 8)a

P

wCt

c

sWl

ux with increasing TMP.

.5.2. Data analysis of concentration mode duringltrafiltration prior to CD

As the pressure drop, �P = 1 bar and TMP = 1.25 bar wereetermined to be the optimal conditions for concentration pro-ess, experimentation was proceeded at these conditions. Theltrafiltration step was conducted in the concentration mode,rior to the purification through CD, simply to reduce the volumerocessed so that the amount of water for the diafiltration coulde greatly reduced, and the time minimized. It was found in liter-ture that there exists an optimum macromolecule (PHA in thistudy) concentration, which is the point where CD could be car-ied out. This optimum concentration can be determined when

t any time was calculated as:

HA purity, P (%) = CPHA,R

CPHA,R + CMS,R× 100 (11)

here CPHA,R is the PHA concentration in the retentate andMS,R is the microsolute (non-PHA) concentration in the reten-

ate.The summation of CPHA,R and CMS,R gives the total biomass

oncentration in the retentate.The overall rejection coefficient was determined as a mea-

ure of the membrane’s ability to retain the PHA in the retentate.hen ln(Cf/C0) was plotted against ln(V0/Vf) (Fig. 3), a straight

ine was obtained by linear regression, the slope of which was

K. Yasotha et al. / Biochemical Engineering Journal 30 (2006) 260–268 267

Fig. 3. Determination plot of rejection coefficient of PHA during ultrafiltrationprocess.

the overall rejection coefficient, σ according to the characteris-tic equation of ultrafiltration. The value of the overall rejectioncoefficient, σ for the PHA granules was thus determined to be0.972.

From Fig. 4, it can be seen that an exponential relationship(linear on semilogarithmic plot) does exist between permeateflux and concentration of PHA in the retentate up to a PHAconcentration of 8.3 g/L. After that, it seems like permeate fluxshowed only a slight decrease, which caused a slight deviationin the linearity. From Fig. 4, the concentration of PHA (taken asthe gel concentration, Cg) when permeate flux reaches 0 L/m2 hwas 28.4 g/L. The last point which is the cause for deviationfrom linearity was not taken into consideration in finding theCg.

By substitution of the relevant data in Eq. (2);

⇒ Copt = 28.4

0.972e

⇒ Copt = 10.7 g/L

Fi

Fig. 5. Plot of PHA purity (%) and permeate flux vs. volumes diluted.

From Eq. (10), Cf = C0(VCR)σwhere Cf = Copt = 10.7 g/L,C0 = CPHA,0 = 1.2 and σ = 0.972.

By substitution of the relevant data, ⇒VCR = 9.5 times ≈ 10times of concentration.

3.5.3. Effects of continuous diafiltration on PHA purityFor further purification of PHA in water suspension, the enzy-

matically treated medium which was concentrated 10 times, wasapplied to the diafiltration system using water as the buffer. Fig. 5shows the changes in %PHA purity after diafiltration processwith the various volumes of diafiltration (VD). From an ini-tial %PHA purity of about 60%, after VD was seven times theinitial volume, the PHA purity was about 92.6% with a recov-ery of nearly 90%. However, increasing VD did not drasticallyimprove the purity of PHA. Therefore, considering the processtime and energy consumption involved, the optimum VD wasseven times the initial volume prior to diafiltration process. It wasalso found that the PESU membrane was effective in the recov-ery process, as the water flux before experiment was restoredafter experiment through cleaning process with 1 M NaOH at50 ◦C (according to manufacturers instructions).

4. Conclusions

From the results obtained, it is evident that Taguchi methodioccn2aacoi

ig. 4. Relationship between permeate flux and concentration of PHA fractionn retentate at each VCR at �P = 1.5 bar and TMP = 1.25 bar.

s an effective tool in designing the experiment for optimizationf the enzymatic recovery of mcl-PHA. From the percentageontribution of each factor indicated in the ANOVA table, itould be inferred that Alcalase concentration is the predomi-ant factor for the enzymatic treatment. It was also found that a0 min digestion period for simultaneous reaction of Alcalasend SDS was more than sufficient for this treatment. It waslso found that the predicted results from Taguchi’s data pro-essing were within 90% confidence interval from the resultsbtained experimentally. The recovery and purification of PHAn water suspension yielded 92.6% PHA purity, with a nearly

268 K. Yasotha et al. / Biochemical Engineering Journal 30 (2006) 260–268

90% recovery. For latex-like applications such as in paints (forbinder, vanish hardening and sprayable films), as well as in papercoating and impregnation [17], the PHA purity obtained in thisresearch is sufficient for direct applications without further treat-ments. However, for niche applications which require a very highPHA purity, such as for biomedical products, e.g. scaffolds fortissue engineering, wound dressing, microspheres for skin cells[17], this enzymatic treatment could be complemented with fur-ther treatment using solvent extraction, whereby only a smallfraction of solvents would be needed to increase the PHA purityto as high as 99%. However, the final product would no longerbe in latex-form.

Acknowledgments

We gratefully acknowledge Sumi Rubber Malaysia Sdn. Bhd,as well as PJP Vote F1024/2005A for the funding received.Thanks are also due to Mr. Muklishien (research assistant inUniversity Malaya) for kindly carrying out production of PHAin 5 L fermentor. We also thank Novozymes for the kind giftsof the commercial grade Alcalase enzyme as well as SouthernAcids (M) for the kind gifts of oleic acid.

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