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Journal of Biotechnology 161 (2012) 429– 436

Contents lists available at SciVerse ScienceDirect

Journal of Biotechnology

jou rn al hom epage: www.elsev ier .com/ locate / jb io tec

he use of a radial basis neural network and genetic algorithm for improving thefficiency of laccase-mediated dye decolourization

. Schuberta,∗, A. Mufflera, S. Mouradb

Empa, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Applied Wood Materials, Lerchenfeldstrasse 5, 9014 St. Gallen, SwitzerlandHead of Process Technology Development, R+D, FISBA OPTIK AG, Rorschacherstrasse 268, 9016 St Gallen, Switzerland

r t i c l e i n f o

rticle history:eceived 6 July 2012eceived in revised form 8 August 2012ccepted 13 August 2012vailable online xxx

eywords:BF-GA methodologyaccase-mediated dye decolourization

a b s t r a c t

A radial basis function neural network (RBF) and genetic algorithm (GA) were applied to improve theefficiency of the oxidative decolourization of the recalcitrant dye Reactive Black 5 (RB 5) by a technicallaccase (Trametes spp.) and the natural mediator acetosyringone (ACS). The decolourization of RB 5 inaqueous solution was studied with a 34 factorial design including different levels of laccase (2, 100,200 U L−1), acetosyringone (5, 50, 100 �M), pH value (3, 4.5, 6) and incubation time (10, 20, 30 min).The generated RBF network was mathematically evaluated by several statistical indices and revealedbetter results than a classical quadratic response surface (RS) model. The experimental data showedthat within 10 min of incubation time a complete decolourization (>90%) was achieved by using the

−1

ost factorsioprocess optimisation

highest amount of laccase (200 U L ) and acetosyringone (100 �M) at pH 6. By applying the RBF-GAmethodology, the efficiency of the laccase-mediated decolourization was improved by minimising therequired amount of laccase and acetosyringone by 25% and 21.7% respectively. Complete decolourization(>90%) was obtained within 10 min at the GA-optimised process conditions of laccase (150 U L−1) andacetosyringone (78.3 �M) at pH 5.67. These results illustrate that the RBF-GA methodology could be apowerful technique during scale-up studies.

© 2012 Elsevier B.V. All rights reserved.

. Introduction

Textile wastewaters are a large problem for conventional treat-ent plants across the world. In general, the dying processes have

low yield and the amount of lost dye in the effluent can reach upo 50% (Couto and Toca-Herrera, 2006). Synthetic dyes are exten-ively used in the food, cosmetics, plastics and textile industriesmong others to colour their products. In these classes, the azoyes, which are designed to resist fading upon exposure to sweat,

ight, water and many chemicals, are the most important chem-

cal class of synthetic dyes (Couto and Toca-Herrera, 2006). Thezo dyes are xenobiotic compounds and are characterised by theresence of at least one azo bond ( N N ) bearing aromatic ring

Abbreviations: ABTS, 2,2-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid);CS, acetosyringone; Af , accuracy factor; ANN, artificial neural network; Bf , bias

actor; HBA, 4-hydroxybenzoic acid; LMS, laccase-mediator system; GA, geneticlgorithm; MAPE, mean absolute percentage error; MRPE, mean relative percent-ge error; pRE, proportion of the relative error; R2, coefficient of determination; RB, reactive black 5; RBF, radial basis function; RMSE, root-mean-squares error; RS,esponse surface; SEP, standard error of prediction; VA, violuric acid.∗ Corresponding author at: Empa, Laboratory for Applied Wood Materials,

erchenfeldstrasse 5, CH-9014 St. Gallen, Switzerland. Tel.: +41 58 765 76 24.E-mail address: mark.schubert@empa.ch (M. Schubert).

168-1656/$ – see front matter © 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.jbiotec.2012.08.003

(Fig. 1a). They have high photolytic stability and show resistancetowards major oxidising agents. The majority of these dyes are toxicto flora and fauna, mutagenic, or carcinogenic (Couto and Toca-Herrera, 2006; Moilanen et al., 2010). Therefore, environmentalregulations in most countries require wastewater to be decolour-ized before its discharge. This has led to the necessity of findinginnovative and environmentally friendly treatment technologies tocomplement or substitute the conventional ones (Moilanen et al.,2010). Most currently existing chemical and physical processes totreat dye wastewater are ineffective and uneconomical (Couto andToca-Herrera, 2006; Roriz et al., 2009).

With regard to their potential in degrading recalcitrant textiledyes of diverse chemical structures, laccases (benzenediol/oxygenoxidoreductases; EC 1.10.3.2.) are of particular interest for wastewater treatment (Couto and Herrera, 2006). These enzymes areincreasingly recognised as environmentally friendly oxidative cat-alysts for a wide range of applications (Camarero et al., 2007;Couto and Herrera, 2006; Riva, 2006) because of their ability tooxidise not only phenols and aromatic or aliphatic amines butalso inorganic compounds forming only oxygen as a co-substrate

and forming water as the only by-product (Riva, 2006). Laccasesfrom wood decay fungi are most widely used because of theirhigh specific activity, stability and strongly positive redox poten-tial (Baldrian, 2006). High redox potential laccases are capable of

430 M. Schubert et al. / Journal of Biotechnology 161 (2012) 429– 436

. (b) A

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ttsrbocttfeietae2rdnecbaitetsptacf(e(gl

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Fig. 1. (a) Chemical structure of the dye Reactive Black 5

xidising dyes directly; however, conversion rates are considerablyncreased by the use of redox mediators, which shuttle electronsetween the substrate and the active site (Bourbonnais and Paice,990; Camarero et al., 2005).

If laccases and mediators, which are still major cost factors, wereo be applied as technical compounds in bulk quantities in theextile industries, the required amount of such laccase-mediatorystem (LMS) for dye decolourization should be significantlyeduced. To improve the efficiency of the laccase-mediated reactiony reducing the required quantity the analysis and optimisationf important process parameters is essential. However, the mostommonly used conventional optimisation study is the ‘one at aime’ method, where only one parameter was varied at any oneime, resulting in excluding interactive effects among differentactors and in only an ‘apparent’ set of optimal conditions (Raot al., 2008). Analysing and modelling of both conventional andnteractive effects of important process parameters are thereforessential to obtain high laccase-mediated decolourization effec-iveness. Response surface (RS) methodology has been widely useds an optimisation technique in many biotechnological areas (Het al., 2008; Kalavathy et al., 2009; Roriz et al., 2009; Schubert et al.,009). As an alternative to the traditional methods of statisticalegression, artificial neural networks (ANNs) have recently beeneveloped and found widespread acceptance as modelling tech-iques because of their remarkable ability to depict the combinedffect of factors in complicated bioprocesses and to model highlyomplex and nonlinear problems in many fields of engineering andiological processing areas (Basheer and Hajmeer, 2000; Izadifarnd Jahromi, 2007). The genetic algorithm (GA) is a method for solv-ng optimisation problems based on natural selection, the processhat drives biological evolution (Goldberg, 1989). The GA repeat-dly modifies a population of individual solutions. On the basis ofhe three rules (selection, crossover, and mutation), GA randomlyelects from the current population those individuals that act asarents, and uses them to produce children for the next genera-ion. Over successive generations, the population “evolves” towardsn optimal solution (Goldberg, 1989; Singh et al., 2009). ANN, inombination with the genetic algorithm, has been used success-ully in various studies to solve a variety of optimisation problemsHe et al., 2008; Izadifar and Jahromi, 2007; Rao et al., 2008; Singht al., 2009). To the best of our knowledge, a radial basis functionRBF) neural network, a variant of ANNs, in combination with theenetic algorithm has not been used for improving the efficiency ofaccase-mediated dye decolourization.

Therefore, the objective of the present work was (1) to construct

RBF neural network for modelling the decolourization of the dyeeactive Black 5, one of the most used reactive dyes for textile fin-

shing, (2) to compare the accuracy of the RBF network approachith RS methodology and to determine the relative importance of

rchitecture of the radial basis function neural network.

the process parameters, and (3) to evaluate the application of com-bined RBF-GA for improving efficiency by minimising the requiredamount of the cost factors (i.e., laccase, mediator and time) forcomplete dye decolourization.

2. Materials and methods

2.1. Experiments

All chemical compounds used were purchased fromSigma–Aldrich (Buchs SG, Switzerland). The Laccase C (Trametesspp.) was supplied by ASA Spezialenzyme GmbH (Wolfenbüttel,Germany).

2.1.1. Enzymatic assaysLaccase activity was determined at room temperature

(22–25 ◦C) and pH 4.5 (citrate buffer) with 3 mM 2,2-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)(ABTS) as substrate(Herpoël et al., 2000; Lomascolo et al., 2003). Reactions wereperformed in transparent polymethyl methacrylate (PMMA)cuvettes. Change in absorbance (�A) at 420 nm was recordedspectrophotometrically (GENESYS 10S UV–Vis, Sysmex DigitanaAG, Switzerland) in intervals of 10 s for 3 min. Samples withvery high activities were diluted 10-fold with 100 mM citratebuffer before using them in the assay. Volumetric activities werecalculated using an extinction coefficient (ε) of 0.036 �mol−1 cm−1

(36000 mol−1 cm−1) (Lomascolo et al., 2003), according to thefollowing equation (�t in min, V in �l):

Laccase activity (U L−1) = �A

�t · ε· Vtotal,assay

Vsample· dilution factorsample

(1)

2.1.2. pH optimum and stabilitypH activity dependence of the technical laccase C was

tested at pHs ranging from 1 to 10 with acetosyringone(0.1 mM ACS, ε390 = 18 M−1 cm−1), guaiacol (10 mM 2-methoxyphenol, ε456 = 12.1 M−1cm−1) and syringol (0.625 mM2,6-dimethoxyphenol, ε468 = 27.5 M−1 cm−1) as substrates(Heinfling et al., 1998b). The pH stability of the enzyme wasdetermined to range from 2 to 8 after one week (7 d) by measuringthe residual laccase activity using the ABTS standard assay.

2.1.3. Mediator screeningScreening for natural mediators was based on the decolouriza-

tion of Reactive Black (RB 5) monitored at 598 nm by laccase in thepresence of the compounds violuric acid (VA), 4-hydroxybenzoicacid (HBA) and acetosyringone (ACS). The reactions were per-formed at room temperature (22–25 ◦C) at pH 4.5 in the presence of

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M. Schubert et al. / Journal of B

accase (0.05 U ml−1), RB 5 (80 �M) and of each compound (30 �M)espectively. Decolourization of RB 5 by laccase alone served as aontrol.

.1.4. Dye decolourizationThe decolourization of RB 5 (80 �M) in aqueous solution at

oom temperature (22–25 ◦C) was studied with a 34 factorial designncluding different levels of laccase (2, 100, 200 U L−1), acetosy-ingone (5, 50, 100 �M), pH value (3, 4.5, 6) and incubation time10, 20, 30 min). All experiments were performed in triplicate andepeated at least once. Decolourization (%) was calculated based onhe difference in absorbance at 598 nm and defined as:

ecolourization (%) = (Ao − Aobs)Ao

· 100 (2)

here Ao is the absorbance measured with heat-denatured laccasend Aobs is the absorbance at a particular incubation time. A value90% was defined as complete decolourization of RB 5 (Camarerot al., 2005). The experimental data (full data set) was divided into

training set (81%) and a testing (validation) set (19%). The train-ng set was used to construct the models and was selected withreat care to ensure that the training set adequately representedhe experimental data of RB 5 decolourization.

.2. Modelling and optimisation

Because parameters can have different units and ranges in thexperimental domain, the following equation was used for normal-sing and coding the parameters (Bas and Boyaci, 2007):

= x − [xmax + xmin]/2[xmax − xmin]/2

(3)

here X is the coded variable, x is the natural variable, and xmax andmin are the maximum and minimum values of the natural variable.

.2.1. Response surface methodologyRS-modelling was done by multiple regression analysis, and a

econd-order polynomial model was defined to fit the response:

= ˇ0 +k∑

i=1

ˇixi +k∑

i=1

ˇiixi2 +

k∑i=1

k∑j=i+1

ˇijxixj + ε (4)

here Y is response (Decolourization %), ˇ0 coefficient is off-seterm called intercept, xi is independent variables related to theactors, ˇi is linear coefficients, ˇij is second-order interactionoefficients, ˇii is quadratic coefficients, and ε is error of the model.

.2.2. Radial basis function neural networkThe following brief introduction to RBF neural networks is based

n the detailed description and explanation by Panagou et al.2007). As illustrated in Fig. 1b, the RBFs are embedded in a two-ayer feed-forward neural network, with a single hidden layer in

hich the nodes are Gaussian kernels and a linear output layer.he RBF is represented by the following equation:

(x) =nr∑

i=1

wi�(∥∥x − ci

∥∥) (5)

here x is input vector, wi is weight of the output layer, � (·) isaussian kernel, ‖·‖ is the Euclidean norm, ci is centre of the ith ker-el, and nr is total number of kernels. The Gaussian kernel � (·) isefined as:

(∥∥x − c

∥∥) = e(−‖x−ci‖2/2�i2) (6)

i

here � is width of the kernel. In Eq. (6), let is exp (−0.8326

∥∥x − ci

∥∥/SPREAD2). If SPREAD is∥∥x − ci

∥∥,

nology 161 (2012) 429– 436 431

then � is 0.5. Spread determines the width of the area inputspace to which each neuron in the first layer responds. The keyto successful implementation of the network is to find suitablecentres for the Gaussian functions, so the appropriate selection ofthe training data set (supervised) and training algorithm are crucialfor the success of the RBF model. In the following simulations andexperiments, training of the RBF neural network was performed bythe NEWRB function in Matlab® Software (ver. 7.10.0 MathWorks,R2010a). The final RBF network contained 65 neurons and onespread parameter for all input variables (� = 1).

2.2.3. Genetic algorithmIn this work, GA optimisation of the decolourization process was

performed using ‘ga’ function of Matlab and with the trained RBFnetwork as the fitness function. The upper and lower bounds of theinput variables (genes) were determined (xi

L < xi < xiU). x denotes

the experimental conditions (laccase, mediator, pH, time) and xiL

and xiU represent the lower and upper bound on xi (Table 1). The

input parameters of ‘ga’ function were as follows: population type:double; crossover fraction: 1; elite count: 2; population size: 250;migration direction: forward; migration interval: 20; migrationfraction: 0.2; generations: 50; stall gen limit: 20; fitness scalingFcn: rank wise.

2.2.4. Model accuracy and sensitivity analysisFor an appropriate comparison of the robustness and reliability

of the RBF neural network and the RS model, both were developedand validated with the same training, testing and full data sets. Toassess the fitting and predictive accuracy of the models, the datasetswere mathematically evaluated by calculating the following evalu-ation criteria (Schubert et al., 2010): coefficient of determination(R2) (Box and Draper, 1987), root-mean-squares error (RMSE),standard error of prediction (SEP) (García-Gimeno et al., 2005;Zurera-Cosano et al., 2006), mean relative percentage error (MRPE),mean absolute percentage error (MAPE) (Jeyamkondan et al., 2001),bias factor (Bf), accuracy factor (Af) (Ross, 1996), and the proportionof the relative error (pRE) (Oscar, 2005). In addition, a graphicalcomparison was performed to illustrate the accuracy of the pro-posed models.

The determination of the relative significance of model inputparameters, ranking the process parameters (laccase, mediator,pH, time) in order of importance, was performed by sensitivityanalysis on the trained RBF neural network. The sensitivity of anoutput parameter, Outj = 1,2,. . .j, to an input parameter, Ini = 1,2,. . .ni,was defined as the normalised ratio between variations causedin Outj by variations introduced in Ini (Noble et al., 2000) and isrepresented by the following equation:

NS =(

dOutj

dIni

) (Ini

Outj

)(7)

2.3. Statistical analysis

The data decolourization in per cent was arcsine-transformedprior to analysis (ANOVA) and back-transformed to numerical val-ues for visualisation. Means were separated using Tukey’s-HSD

P < 0.05 and P < 0.001. The Statistical Package for the Social Sciences(SPSS®) was used for the analyses (Version 17.0, SPSS Inc., Chicago,IL, USA).

432 M. Schubert et al. / Journal of Biotechnology 161 (2012) 429– 436

Table 1Experimental (±SD) and predicted values of laccase-mediated dye decolourization (%) obtained by applying radial basis neural network (RBF) and response surface model(RSM).

pH 6d pH 4.5 pH 3

U L−1a �Mb Minc Exp. RBF RSM Exp. RBF RSM Exp. RBF RSM

200

10010 92.11 ± 0.4 92.10 94.33 72.69 ± 4.5 72.69 82.30 67.07 ± 0.2 67.07 72.5420 92.43 ± 0.4 92.43 98.95 80.20 ± 0.6 78.69 87.09 67.74 ± 0.1 67.74 77.4830 92.68 ± 0.4 92.68 100.00 80.78 ± 0.5 80.79 89.23 67.90 ± 0.1 67.90 79.78

5010 82.19 ± 1.3 82.20 73.27 59.92 ± 4.7 59.90 64.18 65.14 ± 0.4 65.14 57.3520 85.06 ± 0.4 85.05 76.98 74.11 ± 0.8 74.14 68.05 66.87 ± 0.3 66.86 61.3730 85.89 ± 0.4 85.90 78.05 76.75 ± 0.5 76.74 69.27 67.16 ± 0.3 67.16 62.76

510 16.87 ± 2.4 16.86 18.99 14.47 ± 1.1 14.49 12.54 16.67 ± 2.3 16.66 8.3520 17.79 ± 2.3 16.49 21.87 15.59 ± 0.9 15.54 15.58 20.14 ± 1.2 20.16 11.5530 18.26 ± 2.2 18.26 22.12 15.84 ± 0.9 15.86 15.99 20.66 ± 2.3 19.03 12.11

100

10010 81.56 ± 5.9 78.25 75.33 74.51 ± 1.9 74.51 65.79 64.96 ± 1.0 64.96 58.5120 91.13 ± 0.2 91.16 80.10 79.88 ± 0.1 79.88 70.72 68.30 ± 0.1 67.67 63.6030 91.76 ± 0.1 91.75 82.23 80.27 ± 0.1 80.27 73.01 68.57 ± 0.2 68.57 66.05

5010 73.37 ± 0.5 73.40 68.42 59.41 ± 1.9 59.42 61.82 58.80 ± 0.3 58.79 57.4720 81.30 ± 0.4 81.22 72.28 73.63 ± 0.1 73.62 65.83 67.11 ± 0.2 67.11 61.6430 82.28 ± 0.4 82.32 73.49 76.75 ± 0.1 73.84 67.20 68.01 ± 0.2 68.01 63.17

510 17.00 ± 0.9 16.94 26.87 13.11 ± 0.2 13.11 22.91 4.30 ± 0.6 4.30 21.2020 17.76 ± 1.0 17.93 29.90 14.14 ± 0.2 15.62 26.10 8.98 ± 0.4 8.98 24.5530 18.01 ± 0.9 17.93 30.29 14.38 ± 0.3 14.38 26.65 11.13 ± 0.7 11.12 25.26

2

10010 10.31 ± 2.2 10.31 13.52 8.06 ± 0.1 11.39 6.47 6.05 ± 1.2 6.05 1.6820 13.20 ± 4.3 13.21 18.44 10.33 ± 0.7 10.33 11.55 11.58 ± 3.0 11.58 6.9130 16.53 ± 6.6 18.57 20.71 13.29 ± 1.1 13.30 13.98 16.84 ± 4.6 16.84 9.51

5010 8.33 ± 2.4 8.34 20.76 6.97 ± 0.4 6.96 16.64 3.54 ± 1.2 3.54 14.7820 11.45 ± 3.2 11.49 24.76 8.53 ± 0.6 8.55 20.80 8.07 ± 1.7 8.06 19.1030 14.99 ± 5.0 14.99 26.12 10.75 ± 0.9 10.74 22.32 12.29 ± 2.2 15.22 20.78

510 5.42 ± 1.3 3.25 0.00 6.68 ± 1.0 6.70 0.00 0.20 ± 0.3 0.00 0.0020 6.32 ± 1.7 6.27 0.00 6.81 ± 0.6 6.80 0.00 0.42 ± 0.6 0.00 0.0030 7.42 ± 2.1 7.45 0.00 6.92 ± 0.5 6.92 0.00 0.61 ± 0.9 0.61 0.00

Experimental conditions:a Laccase concentration in U L−1.b Acetosyringone (mediator) concentration in �M.

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c Incubation time in min.d pH values.

. Results

.1. Experiments

Reactive Black 5 was not oxidised by the Trametes spp. laccasesed (Fig. 2a). Among the three compounds screened as laccaseediators, only ACS provided a high decolourization of RB 5 by

accase (Fig. 2a). VA and HBA showed no effect compared to the con-

rol (laccase without mediator) and even after the incubation timef 35 min, no promotion of dye decolourization could be observedFig. 2a).

ig. 2. (a) Screening for mediators based on decolourization of Reactive Black 5 (80 �M30 �M) monitored at 589 nm. Mediators used: violuric acid VA (©), 4-hydroxybenzoic aatalysed oxidation of acetosyringone ACS (♦), guaiacol (�) and syringol (�).

The optimum pH for the Trametes spp. laccase varied dependingon the substrate used. The optimal pH for guaiacol, syringol andACS was 2.5, 3 and 4 respectively (Fig. 2b). The stability of the lac-case at different pH values was also tested and revealed no loss ofactivity at pH > 3 during the incubation time of 7 days (data notshown). A reduction in activity with a residual activity of 23% and86% respectively could only be detected at pH 2 and pH 3.

The decolourization of RB 5 (80 �M) in aqueous solution as a

function of laccase (2, 100, 200 U L−1), mediator (5, 50, 100 �M),pH value (3, 4.5, 6) and incubation time (10, 20, 30 min) is summ-arised in Table 1. The experimental data showed that a complete

) with Trametes spp. laccase (0.05 U ml−1) in the presence of different compoundscid HBA (+), acetosyringone ACS (♦), no mediator (�). (b) Effect of pH on laccase-

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M. Schubert et al. / Journal of B

ecolourization (>90%) within 10 min of incubation time was onlychieved by using the highest quantity of laccase (200 U L−1) andCS (100 �M) at pH 6 (Table 1). The most supportive pH for laccase-ediated decolourization of RB 5 was pH 6, while at pH 4.5 and

no complete decolourization could be attained, even after anncubation time of 30 min and by using the highest amount of lac-ase and ACS. The reduction of laccase (100 U L−1) at pH 6 and theighest amount of ACS (100 �M) increased the time required foromplete decolourization from 10 min to >20 min. The reduction ofCS (50 �M) at pH 6 and the highest amount of laccase (200 U L−1)onsiderably decreased the decolourization rate and no completeecolourization could be obtained, even after an incubation timef 30 min. Overall, the decolourization rate of RB 5 significantlyecreased in acidic aqueous media and by reducing the quantity of

accase and the mediator. Almost no decolourization was observedt pH 3 and at the lowest amount of laccase (2 U L−1) and ACS5 �M).

.2. Modelling and optimisation

The predictions of the decolourization rates obtained with theBF neural network and the RS methodology for the experimentalest conditions are shown in Table 1. Results of the mathematicalvaluation of the accuracy of the RBF neural network and statisticalS method for modelling the laccase-mediated RB 5 decolouriza-ion are presented in Table 2 and Figs. 3 and 4 for the three dataets (training, testing, full). As shown in Table 2, the RBF neu-al network revealed good predictive accuracy and was able toodel the dye decolourization (%) with greater accuracy than the

olynomial model (RS), as inferred by the indices. With all dataets and the training data sets in particular, the RBF neural net-ork predictions were clearly superior to those of the RS model,

s judged by the relevant indices (Table 2). Figs. 3 and 4 show theias (observed vs. predicted decolourization), RE (relative error)nd residuals plots, for both models and for all data sets respec-ively. The bias from the RBF neural network was considerablyloser to the line of equity (y = x) compared with the RS modelFig. 3). The analysis of the RE for all data sets revealed that theBF neural network was well within the proposed range from −0.3

ail-safe to 0.15 fail-dangerous under all experimental conditionsoptimal–suboptimal), whereas the RS model showed acceptableccuracy at supportive decolourization conditions, but at subopti-al conditions (decolourization/no decolourization interface) the

alues were almost out of the range (Fig. 4). The residual plotsevealed that the spread of the residuals of the RBF neural networkas narrower to zero than for the RS model. Moreover, the residu-

ls were distributed widely symmetrically around zero, indicatingo tendency to either the positive or the negative side of the graphFig. 4).

The sensitivity analysis revealed that the most influential (sen-itive) parameter affecting the decolourization rates in all data setstraining, testing, full), was laccase followed closely by the media-or and pH to a lesser extent, whereas the parameter time revealednly marginal significance. The obtained results were confirmedy analysis of the RS-model coefficients (data not shown), whichevealed a descending order of the RS-model coefficients of laccaseˇ1) > mediator (ˇ2) > pH (ˇ3) > time (ˇ4).

Because the RBF neural network showed better approxima-ion and generalisation characteristics for laccase-mediated dyeecolourization than the RS-model, indicated by the evaluation

ndices and the low MSE of 0.00092, the trained RBF-networkas used as the fitness function. By using the combined RBF-GA

pproach, the process parameters were optimised in that the effec-iveness of the cost factors was improved and the required amountf laccase, mediator and time for complete decolourization (>90%)as considerably minimised. As shown in Fig. 5, although 25% less

nology 161 (2012) 429– 436 433

laccase (150 U L−1) and 21.7% less mediator (78.3 �M) were used, acomplete decolourization of RB 5 could be attained within 10 min atpH 5.67. The overall performance was comparable to that obtainedby using the highest quantity of laccase (200 U L−1) and acetosy-ringone (100 �M) at pH 6 as no significant difference could bedetermined (P ≥ 0.05). The performance of the GA-optimised pro-cess conditions showed, compared to the next best experimentalresults (Fig. 5), a significant (P < 0.001) higher decolourization ratewithin 10 min according to Tukey’s-HSD test.

4. Discussion

4.1. Experiments

Aside from steric hindrances, the lack of being methyl ormethoxy ortho-substituted (Camarero et al., 2005) and the highredox potential of Reactive Black 5 are probably the reasons why thelaccase C (Trametes spp.) used and even other high redox-potentiallaccases (Camarero et al., 2005; Chivukula and Renganathan, 1995)and chemical oxidisers such as Mn3+ (Heinfling et al., 1998a) werenot able to oxidise the dye. Because of such resistance of azo dyes,the development of a successful enzymatic solution for the tex-tile industry has been hindered until recently. However, by usingsmall molecules that are able to act as electron transfer mediatorsbetween the enzyme, a part or even a complete oxidation of recalci-trant dyes (e.g. RB 5) is possible (Claus et al., 2002). This reaction wasused as a test to evaluate the mediating capabilities of the syntheticcompound violuric acid (VA) and the phenols 4-hydroxybenzoicacid (HBA) and acetosyringone ACS. The diazo dye RB 5 was oxi-dised by laccase only in the presence of ACS, whereas VA and HBAshowed no mediating effect within the reaction time of 35 min.This is remarkable, as particularly VA is mentioned in the litera-ture to be an effective mediator for dye decolourization (Couto andSanromán, 2007; Hu et al., 2009; Tavares et al., 2009). The discrep-ancy may be founded in the higher initial mediator concentration(i.e. stoichiometric amounts instead of catalytic amounts), in thelonger incubation time (i.e. several hours) and in the high ratio ofmediator to substrate used by the authors. In the present work,4-hydroxybenzoic acid (HBA) was also tested as a mediator. Thespectrophotometric monitoring revealed no oxidation of HBA bylaccase and no function as a mediator. This observation is in goodagreement with those of Ikeda et al. (1998) and Kudanga et al.(2009) who also showed that HBA is not oxidised by fungal andbacterial laccases. The natural compound ACS (S-type phenol) wasrapidly oxidised by the laccase used and was found to be highly ableto mediate the oxidation of RB 5 within several minutes (Camareroet al., 2005, 2007). Since ACS yielded the highest decolourizationrate means per minute, it was selected for the additional studies.

In the present work, the laccase-mediated decolourization ofRB 5 was studied including different levels of the process param-eters laccase, mediator (ACS), pH and time. The experimental datashowed that a complete decolourization (>90%) could be achievedwithin 10 min of incubation time by using the highest amount oflaccase (200 U L−1) and acetosyringone (100 �M) at pH 6. The lac-case C (Trametes spp.) revealed a pH optimum at acidic media (pH2.5–4) depending on the substrate used, although the dye decolour-ization was better at pH 6. This is because of the higher stability ofACS phenoxy radicals in slightly acidic aqueous media (Caldwelland Steelink, 1969; Camarero et al., 2005). As a function of time,the decolourization rate of RB 5 was rather high compared to otherstudies. Claus et al. (2002) has shown that a laccase from Trametes

versicolor with HBT (hydroxybenzotriazole) as a mediator attained adecolourization rate of 70% after 16 h. Tavares et al. (2009) achieveda decolourization rate of RB 5 of 76.1% after 10 min and >90% afterone day respectively by using a Trametes villosa laccase and VA as a

434 M. Schubert et al. / Journal of Biotechnology 161 (2012) 429– 436

Table 2Comparison of the evaluation indices of the radial basis function neural network (RBF) and the response surface model (RSM).

Evaluation index Model Decolouration (%)

Training Testing Full

Coefficient of determination (R2) RBF 1.0000 0.9984 0.9997RSM 0.9653 0.9722 0.9638

Root-mean-squares error (RMSE) RBF 0.0304 2.0263 0.8429RSM 7.7949 7.2253 8.0715

Standard error of prediction (SEP%) RBF 0.0724 6.8561 2.1168RSM 18.576 24.447 20.271

Mean relative percentage error (MRPE) RBF −0.0039 12.688 2.1897RSM −15.580 4.387 −14.919

Mean absolute percentage error (MAPE) RBF 0.0976 25.306 4.4546RSM 47.411 51.044 50.702

Bias factor (Bf) RBF 1.0000 1.0000 1.0000RSM 1.1711 1.1343 1.1464

Accuracy factor (Af) RBF 1.0001 1.1386 1.0208RSM 1.3500 1.3696 1.3884

m6Srel

drctetctetia

4

aC

F(

Portion of relative error (pRE) RBF

RSM

ediator. A decolourization rate of 65% after 10 min and 89% after0 min respectively were obtained by Dubé et al. (2008) by applyingtreptomyces coelicolor laccase and ACS. Only Camarero et al. (2005)evealed comparable results to ours. The different results may bexplained by the difference in the redox-potential and purity of theaccases, the mediator and the concentration of RB 5 used.

Even though by using the laccase-mediator system a fastecolourization of the recalcitrant dye RB 5 was possible, theequired amount of the enzyme and mediator should be signifi-antly reduced, as they are still major cost factors. Until recently,he high costs hindered the use of LMS for industrial purposes,specially if they were to be applied in bulk quantities. One wayo reduce the required amount of enzyme, and with it the pro-ess costs, is the reuse of high-redox laccases by immobilisationechniques (Ihssen et al., 2011). As our results revealed a consid-rable dependence of the outcome of the decolourization rate onhe experimental conditions used, another promising strategy is tomprove the effectiveness of such laccase-mediated reactions bynalysing and optimising important process parameters.

.2. Modelling and optimisation

The RBF neural network and the RS-model were constructednd fitted with the experimental results of the 34 factorial design.omparison of the two models was based on both graphical plots

ig. 3. Comparison of predicted and observed values of dye decolourization (%) accordin©) for the training (a), testing (b) and full data sets (c).

1.0000 0.7857 0.96300.6119 0.4286 0.5679

and several evaluation indices. In particular the indices RMSE, SEPand pRE confirmed that the RBF neural network possesses excel-lent prediction accuracy and generalisation ability. RMSE providesa measure of the goodness-of-fit of a model to the data used toproduce it (Box and Draper, 1987) and also provides informationabout how consistent the model would be in the long term (Lou andNakai, 2001). In all cases, the RBF neural network showed lowervalues than the RS model. The SEP index is the relative deviation ofthe mean prediction values and has the advantage of being inde-pendent of the magnitude of the measurements (García-Gimenoet al., 2005). Based on this index, the RBF neural network was farsuperior to the polynomial model for all data sets and revealed,particularly for the training data set, an exceptional SEP valueof <1%. As already discussed in detail (Dalgaard and Jørgensen,1998; Schubert et al., 2010), the indices Af, and Bf have somelimitations because they cannot be calculated for cases in which nodecolourization is predicted by the model and decolourization isobserved or vice versa (Dalgaard and Jørgensen, 1998). Therefore,the additional calculation of the pRE is recommended (Oscar,2005). For cases in which observed or predicted values are infinityor zero, the RE assigns a value of −1 for graphical presentation,

which is an important feature of the acceptable prediction zonemethod because it allows the inclusion of no-decolourization cases(Oscar, 2005). Models with pRE > 0.700 are considered to provideprediction with acceptable bias and accuracy (Oscar, 2005). For

g to the radial basis function neural network (�) and the response surface model

M. Schubert et al. / Journal of Biotechnology 161 (2012) 429– 436 435

F −0.3 (o colou(

ao<t

n

Fd

ig. 4. (a, c, e) Relative error (RE) plots with an acceptable prediction zone from REf dye decolourization (%). (b, d, f) Comparison of residuals (predicted – observed de©) for the training (a, b), testing (c, d) and full data sets (e, f).

ll data sets, the RBF neural network revealed acceptable valuesf >0.700, whereas the RS model revealed unacceptable values of

0.700. Overall, the comparison demonstrated the usefulness ofhe RBF neural network as an empirical model.

The genetic algorithm (GA) is a combinatorial optimisation tech-ique, which searches for an optimal value of a complexobjective

ig. 5. Comparison of the best experimental conditions on laccase-mediated Reactiveecolouration (>90%) of Reactive Black 5.

fail-safe) to 0.15 (fail-dangerous) for comparison of observed and predicted valuesrization). Radial basis function neural network (�) and the response surface model

function by simulation of the biological evolutionary processbased on crossover and mutation. GAs have been successfully used

in a wide variety of problem domains (Goldberg, 1989). As thesensitivity analysis and the RS-model coefficients revealed thatthe most influential (sensitive) parameter was laccase followedclosely by the mediator and pH to a lesser extent, the combined

Black 5 decolourization (%) with the results obtained by RBF-GA. — = Complete

4 iotech

RpfchRdt

teibpi

A

P

R

B

B

B

B

B

C

C

C

C

C

C

C

C

D

D

G

G

H

H

36 M. Schubert et al. / Journal of B

BF-GA approach was applied to optimise the significant processarameters to minimise the amount of the cost factors requiredor efficient decolourization. In the experiments, the fastest andomplete decolourization of RB 5 was obtained by using theighest quantity of laccase and mediator at pH 6. Validation ofBF-GA results demonstrated that at optimal pH of 5.67 a completeecolourization could be obtained in the same time (10 min), evenhough 25% less laccase and 21.7% less mediator were applied.

These results support that the interaction among parame-ers was one of the important aspects in achieving optimumffectiveness of laccase-mediated dye decolourization and thatmprovement is possible with regulation of interactive influencesetween selected parameters. The developed approach in theresent work could help in simulation of economic dye decolour-

zation, with respect to reducing the cost factors in scale-up studies.

cknowledgment

The authors express their gratitude to the Swiss CTI (Innovationromotion Agency) No. 11476.2 PFIW for its financial support.

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