In vitro biosorption of ochratoxin A on the yeast industry by-products: Comparison of isotherm models

Bioresource Technology 98 (2007) 1812–1821

In vitro biosorption of ochratoxin A on the yeast industry by-products: Comparison of isotherm models Diana Ringot

a,*

, Benoit Lerzy a, Kathy Chaplain a, Jean-Paul Bonhoure Eric Auclair c, Yvan Larondelle d

a,b

,

a

d

Institut Supe´rieur d’Agriculture de Beauvais, Rue Pierre Waguet, BP 30313, Beauvais Cedex 60026, France b Centre de Valorisation des Glucides, 33, Avenue Paul Claudel, 80000 Amiens, France c Lesaffre Feed Additives, 1 Rue du Haut Touquet, 59520 Marquette-Lez-Lille, France Universite´ catholique de Louvain, Unite´ de biochimie de la nutrition, Croix du Sud, 2/8, Louvain-la-Neuve 1348, Belgium Received 16 June 2004; received in revised form 24 May 2006; accepted 28 June 2006 Available online 21 August 2006

Abstract Biosorption of ochratoxin A (OA) onto yeast biomass appears to be a reasonably low cost decontamination method. In vitro adsorption of OA onto three yeast industry by-products: a vinasse containing yeast cell walls (EX16), a purified yeast beta-glucan (BETA) and a yeast cell wall fraction (LEC) was examined at 25 C. Seven classical adsorption models were tested to provide the best description of toxin adsorption. A comparison of these models was performed using the magnitude of the coefficient of determination R2 for the linear models and the value of the sum of normalised errors (SNE) for linear and non-linear models. Based on the R2 and the SNE values, Hill, Freundlich and Brunauer–Emmett–Teller equations produced the best models for OA biosorption onto respectively, EX16, BETA and LEC. For these best models, the values of isotherm constants were consistent when measured using both linear and non-linear calculations. The SNE calculation procedure presented in this paper in association with the linear equation analysis method is an appropriate approach for designing a better adsorption isothermal model.  2006 Elsevier Ltd. All rights reserved. Keywords: Ochratoxin A; Biosorption; Yeast cell wall by-products; Isotherm models

1. Introduction Ochratoxin A (OA) is a secondary metabolite of some species of storage fungi. Aspergillus species such as A. ochraceus produce OA mainly in tropical climates and Penicillium species such as P. verrucosum produce OA in temperate climates. Chemically speaking, OA is a chlorine-containing dihydroisocoumarine linked to L-phenylalanine. OA and its hydroxyl derivatives are toxic when consumed by livestock (pigs, poultry) and humans. They are found in human milk, blood serum, plasma, liver and kidney. The main route of exposure is the consumption of contaminated cereals, wine, *

Corresponding author. Tel.: +33 344 062 517; fax: +33 344 062 526. E-mail address: [email protected] (D. Ringot).

0960-8524/$ - see front matter  2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.biortech.2006.06.015

spices, coffee and cocoa products, grape juice and beer (Commission of the European Union, 2002). OA is partially absorbed by the gastrointestinal tract in monogastrics. In ruminants, OA is hydrolysed by the microbial flora into a much less toxic metabolite, ochratoxin a. OA is hydrolysed to ochratoxin a by mammalian carboxypeptidase A and by some micro-organisms within the gastrointestinal tract. In hepatic cells, it is hydroxylated mainly to 4-R-hydroxyochratoxin A (OA-OH) by mixed function oxidases (Li et al., 2000). OA is considered to be a genotoxic agent in vivo and in vitro in some mammalian species (WHO, 2002), but the mechanism of genotoxicity is unclear and there is no evidence of a direct interaction with DNA (Bakker and Pieters, 2002; Ringot et al., 2006). OA is reasonably anticipated to be a possible human carcinogen based on sufficient evidence of carcinogenicity

D. Ringot et al. / Bioresource Technology 98 (2007) 1812–1821

in experimental animals (group 2B) (IARC, 1993). OA is a nephrotoxic, teratogenic and immunotoxic compound (Mu¨ller et al., 1999; Prelusky et al., 1994). OA is relatively stable and is only partially degraded under normal process conditions. Different approaches have been developed to reduce the impact of mycotoxins. They can be divided into pre- and post-harvest strategies and into biological, chemical and physical methods (Huwing et al., 2001). Physical methods involve extraction with solvent, adsorption, inactivation by heat and irradiation. Among the physical decontamination methods, adsorption onto various types of compounds (hydrated sodium calcium aluminosilicate – HSCAS, kaolin, silica binding agent, bentonite, etc.) has been extensively studied in recent years (Grant and Philips, 1998; Scott, 1998). Different binding agents including activated carbons (Galvano et al., 1998), zeolites (Tomasevic-Canovic et al., 2003), diatomaceous earth (Natour and Yousef, 1998), cholestyramine and mixtures of sterilized yeast and fermentation residua from beer production (Bauer, 1994) have been reported to remove OA in vitro. In contrast, activated charcoal and HSCAS have been reported to have a limited efficacy against OA in vitro (Ramos et al., 1996). Castellari et al. (2001) examined several fining treatments to reduce OA levels in red wine. They found that potassium caseinate and activated carbon were the best agents to remove OA. The phenomenon of biosorption is defined as a metabolism independent adsorption of pollutants based on the partition process on a microbial biomass. Microbial biomass consists of small particles, with low density, poor mechanical strength and little rigidity. This phenomenon is generally based on a set of chemical and physical mechanisms (involving physico-chemical interactions such as electrostatic interactions, ion exchange, complexation, chelation and precipitation) leading to the immobilization of a solute component on the microbial cell wall components. It has been investigated by different authors (Aksu, 2003; Aksu and Do¨nmez, 2002; Aksu and Yenner, 1998; Tsezos and Bell, 1989). The complexity of the microbial structure implies that there are many ways for the pollutant to be captured by the cells. Biosorption mechanisms are therefore various (physical adsorption, chemical binding of ionic groups, ion exchange, etc.) and in some cases they are still not very well understood (Veglio and Beolchini, 1997). Cell surface sorption is a physico-chemical interaction between the toxin and functional groups of the cell surface, based on physical adsorption, ion exchange and complexation, which is not dependent on metabolism. Cell walls of microbial biomass, mainly composed of polysaccharides, proteins and lipids, offer abundant functional groups, such as carboxyl, hydroxyl, phosphate and amino groups, as well as hydrophobic adsorption sites such as aliphatic carbon chains and aromatic rings (Ringot et al., 2005). This physico-chemical phenomenon is quick and can be reversible.

1813

Yeasts produce a high quantity of biomass. They are used in a large variety of industrial fermentation processes; moreover, they may be regarded as a good source of adsorbent material, due to the presence in their cell wall of some specific macromolecules such as the mannoproteins and beta-glucans. The ability of the Saccharomyces cerevisiae cell wall to bind zeralenone has been reported recently (Yiannikouris et al., 2003). This research was aimed at examining in vitro adsorption of OA onto three yeast industry by-products at a temperature of 25 C. To this end, several theoretical adsorption models frequently used in the literature were tested for their ability to describe the equilibrium sorption data. For these models, five error functions and the normalised error sum (SNE) were examined. This enabled the determination of the best parameter set and thus provided an accurate equilibrium adsorption model. 2. Methods 2.1. Adsorbents and reagents Three adsorbent materials, namely EX16, BETA and LEC, were obtained from Bio-Springer, Maison-Alfort, France. Two of these adsorbents, EX16 (a vinasse containing 16% liquid yeast cell walls) and LEC (a dry yeast cell wall fraction) are industrial by-products of the yeast industries. BETA is the dried purified beta-glucans fraction of cell walls. The dry matter content of the by-products was 56% for EX16, 95% for BETA and 97.6% for LEC. OA was purchased from Sigma Chemical Company (St. Louis, Missouri, USA). Primary methanolic stock solution (200 mg/L) was prepared in methanol. Five OA test solutions with concentrations of 0.5, 1.0, 2.0, 5.0 and 10.0 mg/L were prepared by the dilution of the methanolic stock solution with deionised distilled water. 2.2. Experimental system Samples of 500 mg of adsorbents were placed in screw cap test tubes with aliquots (10 mL) of the OA test solutions. Two sets of controls were prepared, one by adding 10 mL of mycotoxin test solutions without the addition of adsorbent, and another by adding 500 mg adsorbent to 10 mL deionised distilled water. All experiments were carried out in triplicate. The controls and test tubes were placed in a horizontal shaker-incubator at 400 rpm and at a controlled temperature of 25.0 ± 1.0 C. Preliminary investigations showed that equilibrium uptake was attained rapidly, with practically no change observed after a period of 30 min. All adsorptions were run for 90 min to ensure complete uptake. After the incubation period, solid particles (adsorbent) were separated from the supernatant by centrifugation at 6000g for 20 min at 25 C in an ALC 4239R centrifugation system (Fisher Scientific Labosi, Elancourt, France).

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D. Ringot et al. / Bioresource Technology 98 (2007) 1812–1821

The supernatant was extracted with an equal volume of methanol:deionised distilled water (50:50). The mixture was vortexed for 5 min. This extract was diluted, filtered and analysed by HPLC.

The HPLC chromatographic system consisted of a SpectraSYSTEM P1500 isocratic pump (Thermo Separation Product, Fremont, California, USA) equipped with a AS 3000 model injection valve (100 ll) (Thermo Separation Product, Fremont, California), a Spectroflow 980 fluorescence spectrophotometric detector (Applied Biosystems, Ramsey, New Jersey, USA) equipped with a 150 W xenon lamp (kexcitation = 333 nm and kemission = 470 nm) and PC1000 integration software (Thermo Separation Product, Fremont, California, USA). The analytical column was an Alltima reversed-phase column C18 (25 cm · 4.6 mm i.d., 5 lm particles) preceded by an Alltima C18 precolumn (7.5 mm · 4.6 mm i.d., 5 lm particles) (Alltech, Templemars, France). The columns were left at room temperature. The mobile phase was a mixture of HPLC grade acetonitrile:water:acetic acid (45:54:1), filtered through a 0.22 lm filter membrane, degassed and used at a flow-rate of 1.0 ml/min. Retention time was 7.5 min. The quantity of OA adsorbed was determined by the following equation:

Qeq C0 Ceq V m

ðC 0  C eq Þ  V m

0.16

Qeq, mg/g

2.3. Chromatographic analysis

Qeq ¼

0.20

ð1Þ

quantity of OA adsorbed per gram of adsorbent (mg/g) initial concentration of OA in solution (mg/L) residual toxin concentration at equilibrium (mg/L) volume of solution (L) mass of adsorbent (g)

The maximum error calculated for the OA concentration in the supernatant was 0.259 mg/L for EX16 experiments, 0.315 mg/L for BETA and 0.031 mg/L for LEC.

0.12

0.08 EX16 BETA LEC

0.04

0.00 0

2

4

6

8

Ceq, mg/L

Fig. 1. Experimental OA adsorption on yeast by-products.

sorbed amounts. In this study, seven adsorption models often reported in the literature were tested to provide the best description of toxin adsorption, namely Freundlich (F), Langmuir (L), Brunauer–Emmett–Teller (BET), Hill (H), Redlich–Peterson (RP), Radke–Prausnitz (RkP) and Toth (T). These models are presented below (Eqs. (2)– (17)). It should be noted that for Freundlich, Langmuir, BET and Hill models, the set of isotherm parameters was calculated not only by linearization but also in non-linear forms, while the other models were only evaluated in their non-linear form. For the linear models, the coefficient of determination (R2) is widely used in assessment of isotherm accuracies and is commonly determined by the least squares method (Dagnelie, 1998). For the non-linear forms, the various constants of these models were calculated by optimising the sum of the squares of the errors (see Eq. (18)) using the solver add-in from Microsoft’s spreadsheet, Excel (Microsoft, 2001). The sum of the squares of the errors in the non-linear models was used as an error indicator analogous to the coefficient of determination in the linear models. For all models, the isotherm set parameter values are presented in Tables 1–3. Their corresponding linear and non-linear isotherms are illustrated in Figs. 2–4 and 5–7 respectively.

3. Results and discussion 3.1. Equilibrium studies Adsorption equilibrium is established when the quantity of the toxin being adsorbed (Qeq) is equal to the quantity being desorbed. Then, the equilibrium concentration in solution (Ceq) remains constant. The plotting of Qeq = f(Ceq) for all adsorption experiments is presented in Fig. 1. Adsorbent EX16 was able to bind 32–43% of the initial OA, BETA adsorbed 37–51% and LEC was a very good adsorbent, able to bind 95– 100% of the initial OA. Many equations have been published to describe the equilibrium relationship between adsorbed and unad-

3.1.1. Freundlich empirical model The empirical Freundlich (Freundlich, 1906) equation based on sorption onto a heterogeneous surface is given by Eq. (2). F Qeq ¼ K F  C 1=n eq

ð2Þ

where KF and nF are the Freundlich constants characteristic of the system. KF and nF are indicators of adsorption capacity and adsorption intensity, respectively. The linearized form of the Freundlich equation is given in Eq. (3). ln Qeq ¼ ln K F þ

1 ln C eq nF

ð3Þ

Ceq KF nF Qeq

residual toxin concentration at equilibrium (mg/L) Freundlich adsorption constant (mg/g)/(mg/L)n Freundlich adsorption constant adsorbed toxin quantity per gram of biomass (mg/g)

A plot of ln(Qeq) versus ln(Ceq) should indicate a straight line of slope 1/nF and intercept ln KF. 3.1.2. Langmuir model The Langmuir (Langmuir, 1916) model is valid for monolayer sorption to a surface with a finite number of identical sites. The well known expression of the Langmuir model is given by the following Eq. (4), which can be linearized as in Eq. (5). Q K L C eq Qeq ¼ max ð4Þ 1 þ K L C eq where Qeq (mg/g) and Ceq (mg/L) are the amount of adsorbent per unit weight of biomass and the unadsorbed toxin concentration in solution at equilibrium, respectively. Qmax is the maximum amount of toxin per unit weight of biomass to form a complete monolayer and KL is a constant related to the affinity of the binding sites. C eq 1 C eq ¼ þ Qeq K L  Qmax Qmax KL Ceq Qeq Qmax

For the signification of isotherm parameters see Eqs. (2)–(17).

2.1430E + 00 1.1685E + 00 4.7147E + 00 3.2630E01 2.5394E + 00 1.5820E + 00 5.0000E + 00 5.1407E01 1.5823E + 00 1.9406E + 00 1.0413E + 00 SNE

1815

where

a

1.6513E01 3.0620E01 4.5782E01 6.9748E01 5.1636E01 5.6592E02 1.3601E01 1.9499E01 4.6883E01 3.1204E01 9.9544E02 2.0442E01 2.8970E01 5.7472E01 4.1389E01 1.0410E02 3.9973E02 7.1746E02 2.5886E01 1.3307E01 1.0000E + 00 1.0000E + 00 1.0000E + 00 1.0000E + 00 1.0000E + 00 2.2301E02 2.6107E02 1.3100E02 1.1903E01 1.4576E01 9.9920E01 9.4188E01 8.5044E01 9.3918E01 9.8396E01 1.3733E01 1.2580E01 9.8934E02 3.3458E01 3.4463E01 5.5529E01 3.0967E01 6.5092E02 3.0400E01 7.0654E01

2.0275E01 3.8454E01 6.0604E01 7.7939E01 5.6667E01

9.9544E02 2.0437E01 2.8961E01 5.7464E01 4.1388E01

1.0489E04 6.3800E03 1.0313E+00 1.7238E+00 2.0480E02 3.5948E05 2.8338E03 4.3922E01 1.1587E+00 1.2376E02 6.6124E06 8.3289E04 1.6161E01 6.3976E01 5.2780E03 6.3521E04 2.0836E02 2.2525E+00 2.4714E+00 3.9663E02 1.4166E05 5.4398E04 2.9507E02 2.9417E01 5.7813E03 6.3470E04 1.9625E02 1.9156E+00 2.3211E+00 3.9027E02 8.7231E05 2.6211E03 2.2285E01 8.2689E01 1.3669E02 ERRSQ HYBRID MPSD AER EABS Error function

1.470

3.5272E04 6.4523E03 1.4662E01 7.5132E01 2.8023E02

1.391 4.054 26.036 0.109

1.2879E04 8.0123E03 1.3651E+00 1.9262E+00 2.2476E02

6.3231E05 4.2583E03 6.5235E01 1.4202E+00 1.6416E02

6.3231E05 4.2593E03 6.5255E01 1.4204E+00 1.6416E02

0.297 1.000 1.751

0.552

3.275 0.161 3.950 4. 28.851 0.161

0.387

2.228 0.018 0.020 0.071 0.015 0.017

0.23

RP Hill BET

0.996 0.078

Langmuir Freundlich Hill BET

0.867 0.015 0.982 0.150

Langmuir Freundlich

R2 0.974 KF, Qmax, 0.013 Q0max , Qmax H, Kr, A, Kt nF, KL, CBET, 1.039 KD, ar, R, At nH, br, p, t Isotherm parametersa

Linear models

Table 1 Isotherm parameters and error functions for OA adsorption onto EX16

Non-linear models

Radske´-Prausnitz Toth

D. Ringot et al. / Bioresource Technology 98 (2007) 1812–1821

ð5Þ

Langmuir adsorption constant (L/mg) residual toxin concentration at equilibrium (mg/L) adsorbed toxin quantity per gram of biomass (mg/g) maximum specific uptake corresponding to sites saturation (mg/g)

A plot of Ceq/Qeq versus Ceq should indicate a straight line of slope 1/Qmax and intercept of 1/KLQmax. In the case of low equilibrium concentration Ceq, then 1  KLCeq and Langmuir becomes Henry’s law (Eq. (6)) Qeq ¼ K H  C eq

ð6Þ

3.1.3. Brunauer–Emmett–Teller (BET) model The BET (Bruanuer et al., 1938) isotherm is the theoretical model for multilayer adsorption. It is the most widely applied model in studies of gas–solid equilibrium. This model assumes multilayer adsorption and was developed to describe adsorption phenomena when successive molecular layers of adsorbate form after the completion of a monolayer. The extinction of this model to liquid–solid interface is described by Eq. (7), which is linearized in Eq. (8). Qeq ¼

Q0max  C BET  C eq ðC s  C eq Þ  ½1 þ ðC BET  1Þ  ðC eq =C s Þ

C eq 1 C BET  1 C eq ¼ þ  Qeq ðC s  C eq Þ Q0max  C BET Q0max  C BET C s

ð7Þ ð8Þ

1816

Table 2 Isotherm parameters and error functions for OA adsorption onto beta-glucanes Linear models

Isotherm parametersa

Error function

BET

Hill

Freundlich

Langmuir

BET

R2 0.961 KF, Qmax, 0.016 Q0max , Qmax H, Kr, A, Kt nF, KL, 1.030 CBET, KD, ar, R, At nH, br, p, t

0.920 0.134

0.915 0.019

0.940 0.174

0.015

3.237

0.024

0.766

1.052

0.017

22.644

0.169

9.912

8.004

0.964

0.005

9.549

51.324

67.378

1.051

48.190

1.087

0.000

0.571

0.538

ERRSQ HYBRID MPSD AER EABS

1.0550E04 4.5149E03 3.3860E01 1.1492E+00 1.8329E02

4.7852E04 9.7567E03 9.4996E01 1.4792E+00 3.2715E02

1.0284E04 5.7889E03 9.0677E01 1.5350E+00 2.0782E02

2.6054E04 7.5246E03 7.4106E01 1.3612E+00 2.6770E02

1.0011E04 4.5383E03 2.8610E01 1.0789E+00 1.9104E02

1.0593E04 4.4048E03 3.2898E01 1.1559E+00 1.9760E02

2.6720E05 5.2701E03 1.4667E+00 1.4283E+00 8.5830E03

1.1239E04 4.8674E03 2.8242E01 1.0645E+00 2.0140E02

1.0186E04 4.3496E03 3.1662E01 1.1410E+00 1.9451E02

1.9465E04 5.5218E03 4.1335E01 1.2667E+00 2.5957E02

1.1074E04 4.5287E03 3.7048E01 1.1850E+00 1.9671E02

2.2046E01 4.6275E01 2.3086E01 7.4863E01 5.6027E01

1.0000E + 00 1.0000E + 00 6.4770E01 9.6362E01 1.0000E + 00

2.1492E01 5.9333E01 6.1825E01 1.0000E + 00 6.3525E01

5.4447E01 7.7122E01 5.0527E01 8.8678E01 8.1829E01

2.0920E01 4.6514E01 1.9507E01 7.0282E01 5.8395E01

2.2137E01 4.5147E01 2.2431E01 7.5299E01 6.0400E01

5.5839E02 5.4015E01 1.0000E + 00 9.3046E01 2.6236E01

2.3488E01 4.9888E01 1.9256E01 6.9347E01 6.1561E01

2.1286E01 4.4581E01 2.1588E01 7.4330E01 5.9456E01

4.0678E01 5.6594E01 2.8183E01 8.2516E01 7.9344E01

2.3142E01 4.6416E01 2.5260E01 7.7194E01 6.0128E01

2.2354E + 00

2.2124E + 00

2.8732E + 00

2.3214E + 00

SNE a

1.040

2.2230E + 00 4.6113E + 00 3.0617E + 00 3.5260E + 00 2.1562E + 00 2.2541E + 00 2.7888E + 00

For the signification of isotherm parameters see Eqs. (2)–(17).

Hill

RP

Radske´Prausnitz

Langmuir

Toth

D. Ringot et al. / Bioresource Technology 98 (2007) 1812–1821

Freundlich

Non-linear models

Table 3 Isotherm parameters and error functions for OA adsorption onto LEC

Freundlich Isotherm parametersa

Error function

R2 0.896 KF, Qmax, Q0max , 0.202 Qmax H, Kr, A, Kt nF, KL, CBET, 2.048 KD, ar, R, At nH, br, p, t ERRSQ HYBRID MPSD AER EABS

SNE a

Non-linear models Langmuir

BET

Hill

Freundlich

Langmuir

BET

Hill

RP

Radske´Prausnitz

Toth

0.981 0.109

0.997 0.045

0.964 1.892

0.897

26.275

0.040

18.706

4.296

0.593

0.795

12.460

22.750

4.854

0.732

0.021

19.924

20.063

6.846

8.719

9.298

1.389

0.000

1.146

5.976

0.762

5.0766E03 3.6971E02 6.1672E01 1.5035E+00 1.0070E01

8.7417E03 4.9814E02 3.4450E01 8.1778E01 9.9503E02

7.4881E05 2.3850E03 9.5714E02 5.3580E01 1.3048E02

2.3796E03 1.9182E02 1.9502E01 7.9404E01 7.4544E02

1.5754E03 4.3355E02 1.9014E+00 2.6561E+00 8.1587E02

1.6555E03 2.6388E02 7.9567E01 1.8442E+00 7.8147E02

7.0877E05 2.5069E03 1.2103E01 6.2723E01 1.5304E02

1.5848E03 4.4200E02 1.9471E+00 2.6815E+00 8.1713E02

1.6470E03 2.6430E02 8.0085E01 1.8490E+00 7.8002E02

1.6436E03 2.7106E02 8.4852E01 1.8980E+00 7.8521E02

1.6471E03 2.6431E02 8.0084E01 1.8490E+00 7.8001E02

5.8073E01 7.4217E01 3.1674E01 5.6070E01 1.0000E + 00

1.0000E + 00 1.0000E + 00 1.7693E01 3.0497E01 9.8812E01

8.5660E03 4.7877E02 4.9158E02 1.9981E01 1.2958E01

2.7221E01 3.8508E01 1.0016E01 2.9612E01 7.4027E01

1.8021E01 8.7033E01 9.7652E01 9.9054E01 8.1020E01

1.8938E01 5.2973E01 4.0865E01 6.8775E01 7.7605E01

8.1079E03 5.0324E02 6.2160E02 2.3391E01 1.5198E01

1.8129E01 8.8730E01 1.0000E + 00 1.0000E + 00 8.1146E01

1.8841E01 5.3057E01 4.1131E01 6.8953E01 7.7461E01

1.8801E01 5.4413E01 4.3579E01 7.0783E01 7.7976E01

1.8842E01 5.3059E01 4.1130E01 6.8952E01 7.7460E01

3.2003E + 00

3.4700E + 00

4.3499E01 1.7938E + 00 3.8278E + 00

2.5916E + 00

5.0648E01

3.8801E + 00 2.5944E + 00 2.6555E + 00 2.5944E + 00

D. Ringot et al. / Bioresource Technology 98 (2007) 1812–1821

Linear models

For the signification of isotherm parameters see Eqs. (2)–(17).

1817

1818

D. Ringot et al. / Bioresource Technology 98 (2007) 1812–1821 0.09

0.08

0.08

0.07

0.07

0.06 0.05

0.05

Qeq, mg/g

Qeq, mg/g

0.06

0.04 Experimental Freundlich Langmuir BET Hill

0.03 0.02 0.01

Experimental Freundlich Langmuir BET Hill Redlich-Peterson Toth RadKe-Prausnitz

0.04 0.03 0.02 0.01

0 0

2

4 Ceq, mg/L

6

0

8

0

2

4 Ceq, mg/L

Fig. 2. Linear isotherms for OA adsorption onto EX16.

6

8

Fig. 5. Non-linear isotherms for OA adsorption onto EX16. 0.10 0.09

0.09

0.08

0.08

0.07

0.07 0.06 Qeq, mg/g

Qeq, mg/g

0.06 0.05 0.04

Experimental Freundlich Langmuir BET Hill

0.03 0.02 0.01

Experimental Freundlich Langmuir BET Hill Redlich-Peterson Toth RadKe-Prausnitz

0.05 0.04 0.03 0.02 0.01

0.00 0

1

2

3 Ceq, mg/L

4

5

6

0 0

1

2

3

4

5

6

Ceq, mg/L

Fig. 3. Linear isotherms for OA adsorption onto BETA.

Fig. 6. Linear isotherms for OA adsorption onto BETA. 0.20 0.18

0.20

0.16

0.18

0.14

0.16 0.14

0.10 0.08

Experimental Freundlich Langmuir BET Hill

0.06 0.04 0.02

Qeq, mg/g

Qeq, mg/g

0.12

0.12

Experimental Freundlich Langmuir BET Hill Redlich-Peterson Toth RadKe-Prausnitz

0.10 0.08 0.06 0.04

0.00

0.02 0

0.05

0.1

0.15 0.2 Ceq, mg/L

0.25

0.3

0.35

0.00 0

Fig. 4. Linear isotherms for OA adsorption onto LEC.

0.05

0.1

0.15 0.2 Ceq, mg/L

0.25

0.3

0.35

Fig. 7. Linear isotherms for OA adsorption onto LEC.

CBET Ceq Qeq Q0max

BET adsorption constant relating to the energy of interaction with the surface (L/mg) residual toxin concentration at equilibrium (mg/L) adsorbed toxin quantity per gram of biomass (mg/ g) maximum specific uptake corresponding to monolayer saturation (mg/g)

Cs

saturation concentration of the solute corresponding to monolayer saturation (mg/L)

A plot of Ceq/[Qeq*(Cs  Ceq)] versus Ceq/Cs allows calculation of CBET and Q0max .

D. Ringot et al. / Bioresource Technology 98 (2007) 1812–1821

3.1.4. Non-ideal competitive adsorption (NICA) model At the origin of the NICA (Koopal et al., 1994, 2001) model, it was postulated that an equation like the Hill equation (Hill, 1910) could be used to describe the binding of different species onto a homogeneous substrate. This model assumes that the adsorption is a cooperative phenomenon due to the ability of ligand binding at one site on a macromolecule to influence ligand binding at a different site on the same macromolecule. The Hill equation is: Qeq ¼

Qmax H  C nH eq K D þ C nH eq

ð9Þ

This equation can be linearized through logarithmic terms (Eq. (10)) ! Qeq ln ð10Þ ¼ nH ln C eq  ln K D Qmax H  Qeq

This model has three isotherm parameters, Kr, ar and br (0 < br < 1) which characterize the isotherm. When br = 1, then the equation becomes similar to the Langmuir model (Eq. (13)). Qeq ¼

Ceq Qeq KD Kd

Qmax H nH

ð11Þ

residual toxin concentration at equilibrium (mg/L) adsorbed toxin quantity per gram of biomass (mg/ g) Hill constant dissociation constant per site (mg/L), Kd is equal to residual toxin concentration at half saturation, K d ¼ C 50 eq ; also, Kd = 1/Ka, where Ka is the association constant maximum specific uptake corresponding to sites saturation (mg/g) Hill cooperativity coefficient of the binding interaction

Thus, three possibilities can occur – nH > 1, positive cooperativity in binding, – nH = 1, non-cooperative or hyperbolic binding, – nH < 1, negative cooperativity in binding. 3.1.5. Redlich–Peterson model This model (Redlich and Peterson, 1958) approaches the Freundlich model at high adsorbate concentration (it describes equilibrium on heterogeneous surfaces and hence does not assume monolayer capacity) and Henry’s law at low concentration (Eq. (12)). K r  C eq Qeq ¼ 1 þ ar  C beqr Ceq Qeq Kr ar br

ð12Þ

residual toxin concentration at equilibrium (mg/L) adsorbed toxin quantity per gram of biomass (mg/ g) constant of Redlich–Peterson isotherm (L/g) constant of Redlich–Peterson isotherm (L/mg)br constant of Redlich–Peterson isotherm

K r  C eq 1 þ ar  C eq

ð13Þ

When br = 0, then it becomes similar to Henry’s law (Eq. (14)). Qeq ¼

K r  C eq 1 þ ar

ð14Þ

3.1.6. The Radke–Prausnitz model The Radke–Prausnitz (Radke and Prausnitz, 1972) isotherm model is depicted in Eq. (15). Qeq ¼

where K D ¼ K nH d

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A  R  C peq A þ R  C p1 eq

ð15Þ

This equation can be converted to a linear form (Eq. (16)) ! 1 1 1 ln   ð16Þ ¼  ln R  p ln C eq Qeq A C eq Ceq Qeq A R p

residual toxin concentration at equilibrium (mg/L) adsorbed toxin quantity per gram of biomass (mg/ g) constant of Radke–Prausnitz isotherm (L/g) constant of Radke–Prausnitz isotherm, (mg/ g) * (mg/L)p constant of Radke–Prausnitz isotherm

3.1.7. Toth model The Toth isotherm (Toth, 1971) is derived from the potential theory and is applicable for heterogeneous adsorption. It assumes a quasi-Gaussian energy distribution. Most sites have an adsorption energy lower than the maximum adsorption energy (Eq. (17)) Qeq ¼

Ceq Qeq Kt At t

K t  C eq ðAt þ C teq Þ1=t

ð17Þ

residual toxin concentration at equilibrium (mg/L) adsorbed toxin quantity per gram of biomass (mg/ g) constant of Toth isotherm (mg/g) constant of Toth isotherm (L/mg) constant of Toth isotherm

3.2. Error analysis The classical method to determine isotherm parameters is to use linear regression with transformed variables. Linearization of the isotherm equation alters the error

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D. Ringot et al. / Bioresource Technology 98 (2007) 1812–1821

structure and normality assumption of standard least squares. This could explain earlier observations that Freundlich isotherms produced better results at low concentration and Langmuir isotherms tend to fit data better for high concentration (Richter et al., 1989). Recently, the utilisation of sum of normalised errors (SNE) calculation procedure in adsorption studies has been presented by several authors (Allen et al., 2003; Ho et al., 2002; Porter et al., 1999). In these publications, a non-linear complex optimisation procedure was performed for the estimation of the SNE values. In the present study, five error functions were examined to determine and evaluate the fit of isotherm models to the experimental data (Eqs. (18)–(22)) and only the sum of the squares of the errors (ERRQS) was optimised as a relevant error indicator comparable to the coefficient of determination in the linear models. 1. The sum of the squares of errors (ERRSQ) p X 2 ðQe;meas  Qe;calc Þi

ð18Þ

i¼1

2. The hybrid fractional error function (HYBRID) " # 2 p X ðQe;meas  Qe;calc Þ Qe;meas i¼1

ð19Þ

i

3. A derivative of Marquardt’s percent standard deviation (MPSD) !2 p X Qe;meas  Qe;calc ð20Þ Qe;meas i¼1 i

4. The average relative error (AER)  p  X Qe;meas  Qe;calc      Q e;meas

i¼1

ð21Þ

i

5. The sum of absolute errors (EABS) p X  Q

e;meas

  Qe;calc i

ð22Þ

i¼1

The process of minimising the respective error functions across the range of experimental concentrations permitted calculation of the isotherm constants. The values of the errors obtained for each error function for each set of linear and non-linear isotherm constants were normalised by dividing by the highest value for that error function across a range of different isotherms. These normalised errors were added for each parameter set to get the SNE. A comparison of the various SNE could thus be undertaken and allowed the identification of the best set of isotherm constants to describe the measured data. The parameter set thus providing the smallest normalised error sum was considered to be optimal. The values of all these minimum error functions and of the optimum isotherm constants are listed in Tables 1–3.

For EX16, the data presented in Table 1 show, based on the sum of normalised errors, that the Hill model produced the best goodness of fit. Furthermore, for the linear equations, the highest coefficients of determination were also obtained using the Hill model. Since the value of the Hill coefficient nH is higher than 1, the binding of OA by EX16 is a positive cooperative interaction. For this model, the values of isotherm constants are comparable when they are calculated by linear and non-linear equations. For BETA, the SNE values presented in Table 2 show that the non-linear Freundlich model produced the best fit across the range of non-linear models. Moreover, in linear models, the best value of linear R2 was also determined for the Freundlich model. Based on the R2 and on the SNE values, the Freundlich model in its linear and non-linear forms produced a reasonable model for OA biosorption onto beta-glucans. The magnitude of R2 is an indication of the relative quality of fit of the linear isotherm. The fact that the nF value (in the Freundlich model) is very close to 1 means that this model becomes Henry’s law. Then, OA adsorption onto BETA is a simple linear solid/liquid partition which follows Henry’s law. For LEC, the data presented in Table 3 show that, based on the SNE values, the BET model is the most appropriate model for the biosorption of OA on this yeast cell product. Furthermore, the highest value of R2 was also calculated for the BET model. Based on the R2 values and on the SNE value, the BET model appears to be a very good model for OA biosorption onto LEC. For this model, the values of isotherm constants calculated with the linear equation are very close to those obtained using non-linear calculation. The various profiles of toxin adsorption onto the three adsorbents studied suggest different mechanisms of adsorption according to their different compositions. In a previous study (Ringot et al., 2005), a thermodynamic approach to characterization of the binding of OA onto EX16, BETA and LEC was presented, enabling suggestion of some hypotheses regarding the mechanism of OA yeast cell wall biosorption. In order to investigate these hypotheses in future research, it is proposed to study the influence of adsorption conditions (pH and adsorbent mass) on the biosorption phenomenon. 4. Conclusions The present work allowed identification, among the most commonly described models in the literature, of the isothermal equation which best fitted the adsorption of OA onto each of the three yeast by-products studied. Linear and non-linear optimisation techniques were applied to determine isotherm parameters. For the best models identified, the non-linear calculation of isotherm parameters presented in this paper produced comparable data to those obtained using the linear method based on the least squares calculation.

D. Ringot et al. / Bioresource Technology 98 (2007) 1812–1821

Based on the error analysis, the Hill, Freundlich and BET equations, in both their linear and non-linear forms, appear to be appropriate models for OA biosorption onto EX16, BETA and LEC, respectively. For these best models, the values of isotherm constants were very close when calculated using linear and non-linear equations. These results indicate that the linear equation analysis using the R2 calculation associated with the SNE calculation procedure presented in this work is an appropriate method to use for the study of OA adsorption onto yeast by-products. Acknowledgements The authors thank Mr. Renaud Trouve from Serendi Ltd., Mr. Eric Oriol from Bio-Springer Ltd. and Mrs. Pauline Anton-Gay from ISAB for their scientific interest in this study. References Aksu, Z., 2003. Reactive dye bioaccumulation by Saccharomyces cerevisiae. Process Biochem. 38, 1437–1444. Aksu, Z., Do¨nmez, G., 2002. A comparative study on the biosorption characteristics of some yeast for Remazol Blue reactive dye. Chemosphere 50, 1075–1083. Aksu, Z., Yenner, J., 1998. Investigation of the biosorption of phenol and monochlorinated phenols on the dried activated sludges. Process Biochem. 33, 483–491. Allen, S., Gan, Q., Matthews, R., Johnson, P.A., 2003. Comparison of optimised models for basic dye adsorption by kudzu. Bioresource Technol. 88, 143–152. Bakker, M., Pieters, M.N., 2002. Risk assessment of ochratoxin A in Netherlands. RIVM Report 388802025. Inspectorate for Health Protection and Veterinary Public Health. Bilthoven, Netherlands, 24p. Bauer, J., 1994. Mo¨glichkeiten zur Entgiftung mycotoxin-haltiger, Futtermittel. Monatssh. Veterina¨rmed. 49, 175–181. Bruanuer, S., Emmett, P.H., Teller, E., 1938. Adsorption of gases in multimolecular layers. J. Am. Chem. Soc. 60, 309–316. Castellari, M., Versari, A., Fabiani, A., Parpinello, G.P., Galassi, S., 2001. Removal of ochratoxin A in red wines by means of adsorption treatments with commercial fining agents. J. Agric. Food Chem. 49, 3917–3921. Commission of the European Union, 2002. Report of experts participating in task 3.2.7. Assessment of dietary intake of ochratoxin A by the population of EU Member States. 153p. Dagnelie, P., 1998. Statistique the´orique et applique´e. vol. 1, De Boecket Larcier, Paris-Bruxelles, Belgium pp. 130–148. ¨ ber die Adsorption in Lo¨sungen. Z. Phys. Freundlich, H.M.F., 1906. U Chem. 57, 385–470. Galvano, F., Pietri, A., Bertuzzi, T., Piva, A., Chies, L., Galvano, M., 1998. Activated carbons: In vitro affinity for ochratoxin A and deoxynivalenol and relation of adsorption ability to physicochemical parameters. J. Food Prot. 61, 469–475. Grant, P.G., Philips, T.D., 1998. Isothermal adsorption of aflatoxin B1 on HSCAS clay. J. Agric. Food Chem. 46, 599–605. Hill, A.V., 1910. The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves. J. Physiol. (London) 40, iv–vii. Ho, Y.S., Porter, J.F., McKay, G., 2002. Equilibrium isotherm studies for the sorption of divalent metal ions onto peat: copper, nickel and lead single component systems. Water Air Soil Pollut. 141, 1–33. Huwing, A., Freimund, S., Ka¨ppeli, O., Dutler, H., 2001. Mycotoxin detoxication of animal feed by different adsorbent. Toxicol. Lett. 122, 179–188.

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