Environ. Sci. Technol. 2003, 37, 2836-2841
Effect of Salts on the Binding of Some Environmental Pollutants to Corn Protein Zein Studied by HPLC MO Ä N I K A Z A G Y I , E S T H E R F O R G AÄ C S , * MIKLO Ä S P R O D A N , A N D T I B O R C S E R H AÄ T I Institute of Chemistry, Chemical Research Centre, Hungarian Academy of Sciences, P.O. Box 17, H-1525 Budapest, Hungary Z O L T AÄ N I L L EÄ S Central European University, H-1525 Budapest, Hungary
The strength and the character of the binding of 14 organic solvents to the corn protein zein in distilled water and in various salt solutions were determined by preparing zein-coated carbon stationary phase and by measuring the retention characteristics of solvents on a high-performance liquid chromatographic column filled with this stationary phase. The relationship between the physicochemical parameters and binding characteristics of solvents was elucidated by principal component analysis. It was established that various interactive forces are involved in the binding of solvent to the protein, suggesting a mixed binding mechanism. Binding characteristics are equally influenced by the molecular hydrophobicity and by the polarity parameters of the solvent. Coordination numbers, ionization, and lattice energies of the monovalent cations significantly influenced the various aspects of the binding of organic solvents to zein.
Introduction The regular use of a wide variety of organic solvents in numerous industrial processes results in the release of considerable amounts of these compounds in the environment producing widespread pollution, public concern, and legislation challenges. The majority of industrial solvents are volatile or semivolatile and more or less miscible in water; therefore, they can equally pollute the atmosphere (1), water (2), sediment (3), and soil (4). Moreover, it has been established that some organic solvents such as alkanes, alkenes (5), benzene and benzene derivatives (6), alcohols (7), etc. possess serious health risk factors. Because of the high variation in the chemical structure, the toxicology of organic solvents has not been entirely elucidated at the level of molecular biology. It has been demonstrated that the first step includes the binding of these molecules to the different macromolecules of the target organ or organism (8). In recent decades, numerous biochemical and biophysical methods have been employed to investigate the interactions between various molecules such as peptides (9), proteins (10), and amino acids (11). The high sensitivity and separation capacity make chromatographic methods suitable for the determination of many types of molecular interactions (12). These procedures have considerable advantages: they are fast, and the compounds to be studied * Corresponding author phone: +36-1-325-7900; fax: +36-1-3257554; e-mail:
[email protected]. 2836
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need not be extremely pure because their impurities separate during the chromatographic process. Principal component analysis (PCA), a frequently used multivariate mathemathical-statistical technique, has been developed to facilitate the extraction of maximal information from data matrices consisting of a large number of columns and rows (13). PCA promotes the easy elucidation of the corelations among the columns and rows of data matrices without being one of the dependent variables. PCA is a projection technique portraying the original data in reduced dimensions. It computes the relationships (similarities and dissimilarities) between the columns of the original matrix and arranges the variables according to the coefficients of correlations taking into consideration simultaneously the magnitude and sign of the coefficients of correlation. PCA has often been employed in various fields of current research. Thus, it has been used in quantitative structure-activity relationship (QSAR) studies (14), for the elucidation of molecular structure-property relationships (15), for the assessment of molecular lipophilicity (16, 17), for theoretical organic chemistry (18), for quantitative structure-retention studies in chromatography (19), for the evaluation of structure-biodegradation relationships (20), for the clustering of amino acids (21), for the classification of solvent properties (22), and as polarity indicators in gas chromatography (23), etc. As in the majority of cases, the computed matrices of PC loadings and variables are also multidimensional so they cannot be evaluated by the traditional visual methods. Nonlinear mapping technique (NLMAP) has been developed for the reduction of the dimensionality of such matrices (24). Traditional NLMAP takes into consideration the positive and negative sign of the coefficient of correlation and implements the calculation acordingly. Therefore, the points highly but negatively correlated are far away on the maps in the same manner as the points not correlated. This procedure leads to correct assumptions only in the case when the positive correlations among variables are of interest. To evaluate precisely the relationships between the points without taking into consideration the positive or negative character of the correlation, it is advisable to carry out the calculations with the absolute values of PC loadings. The validity of this experimental approximation has been proven in the evaluation of the interaction of nonsteroidal antiinflammatory drugs with a model protein. The parallel application of the original PC loadings and their absolute values in the data reduction techniques has been proposed (25). This procedure has been successfully used for the study of the effect of carboxymethyl-β-cyclodextrin on the hydrophobicity parameters of steroidal drugs (26), for the assessment of the binding characteristics of environmental pollutants to the wheat protein gliadin (27), and for the elucidation of the relationship between physicochemical parameters and biodegradation rate of sulfosuccinic acid ester surfactants (28). The aims of the work were the determination of the binding of some organic solvents commonly used in industrial processes with the corn protein zein using high-performance liquid chromatography (HPLC) and the application of PCA and NLMAP for the elucidation of the impact of salts and the physicochemical parameters of solvent molecules on the strength of interaction. The study of the binding of organic solvents to zein was motivated by the fact that zein is an important source of protein in many countries, and these solutes are pollutants in possible contact with zein. The assessment of the character of the mode of binding may not only promote the better understanding of the binding forces 10.1021/es0210087 CCC: $25.00
2003 American Chemical Society Published on Web 05/07/2003
TABLE 1. Organic Solvents and Their Origin no. of solvent
name
origin
I II III IV V VI VII VIII IX X XI XII XIII XIV
acetone tetrahydrofuran acetonitrile 1,4-dioxane 2-methoxyethanol 2-ethoxyethanol 2-propoxyethanol Methyl ethyl ketone ethylene glycol propylene glycol glycerine 1-propanol methanol ethanol
Koch Light Ltd (Suffolk, UK) Riedel de Haen (Seelze, Germany) Riedel de Haen (Seelze, Germany) Romil (Cambridge, UK) Fluka (Seelze, Germany) Fluka (Seelze, Germany) Fluka (Seelze, Germany) Reanal (Budapest, Hungary) Donauchem (Budapest, Hungary) Kemobil (Tata, Hungary) Donauchem (Budapest, Hungary) Chemolab (Budapest, Hungary) Chemolab (Budapest, Hungary) Chemolab (Budapest, Hungary)
CsCl were used as mobile phases. The use of salt solutions was motivated by the assumption that the binding of organic solvent molecules to the surface of zein in natural conditions occurs in an ionic environment. Measurements were performed at room temperature (22 ( 2 °C). Each retention time was measured by three consecutive injections. Intraday and interday reproducibilities were determined by five injections of the same sample at five consecutive days. Organic solvents and their origins are listed in Table 1. The small amount of solvents injected into the column suggests that the column has ever been overloaded and that the competition between individual solvent molecules for binding sites of the zein surface does not occur. The log kw value, theoretical plate number (N), and asymmetry factor (AF) were calculated separately for each solute in each mobile phase, and they were considered as physicochemical parameters characterizing the binding of solutes to the surface of zein. Log kw values were calculated by
involved in the interaction between proteins and organic pollutants but may also facilitate the development of efficacious environmental protection techniques.
Experimental Section Graphitized carbon support (29) with high specific surface area (30) and suitable for the preconcentration of volatile chlorinated hydrocarbons (31) and other organic compounds from water (32) and environmental samples (33) was provided by Professor Dusan Berek (Polymer Institute of the Slovak Academy of Sciences, Bratislava, Slovakia). Zein-coated carbon stationary phase was prepared by dissolving 2 g of zein in 200 mL of 7:3 v/v 2-propanol/water mixtures at 70 °C under continuous gentle stirring. After the dissolution of the protein, 20 g of carbon support was added, the mixture was stirred for 2 h at the same temperature, and then the solvents were removed under vacuum. The zein-coated stationary phase was dried in a vacuum oven at 70 °C. Columns of 150 × 4.6 mm i.d. were filled with a Shandon (Pittsburgh, PA) analytical pump using bidistilled water as the filling agent. The effect of other levels of protein coating on the binding parameters has not been studied. The HPLC system consisted of a Liquopump model 312 (Labor MIM, Budapest, Hungary) pump, a Cecil CE-212 variable wavelength UV detector (Cecil Instruments, Cambridge, UK), a Valco injector (Valco Inc., Houston, TX) with a 20-µL sample loop, and a Waters 740 integrator (Millipore Inc., Milford, MA). The flow rate was 1 mL min-1, and the detection wavelength was 200 nm. To visualize and demonstrate the separation capacity of the zein stationary phase, some measurements were performed at 0.3 mL min-1 flow rate. This flow rate allowed the real time separation of some solvents. Aqueous solutions of 0.16 M LiCl, NaCl, KCl, and
log kw ) log(tR/t0 - 1)
(1)
where tR is the retention time of the retained organic solvent molecule and t0 is the retention time of the unretained one (in our case, methanol). The theoretical plate number (N) was calculated by
N ) 16(tR/W)2
(2)
where tR and W are the retention time and peak width (also in time units) at the base line of the organic solvent retained. AF was calculated by drawing a parallel line to the base line at a height corresponding to 10% of the peak height. The widths of the first and second part of the peak were measured and divided with each other. It was supposed that a higher log kw value indicates higher affinity of the environmental pollutants to zein (34); therefore, it can be used as a quantitative indicator of the strength of solute-protein interaction (27). It has been further assumed that N value characterizes the distribution of the adsorption centers on the zein surface when the distribution is Gaussian (a higher N value signifies lower differences among the binding strengths of adsorption centers). However, it has to be emphasized that the kinetics of binding and release (sorption-desorption) may also influence the value of N regardless if one type of binding site in the protein is involved or many such sites are involved. AF can be considered as the measure of the non-Gaussian character of the distribution of the binding strengths of adsorption centers. It can also be modified by the character of the sorption isotherms. AF is equal to 1 for entirely symmetric distribution.
TABLE 2. Characteristics of the Binding of Organic Solvents to Zein Determined in Ionic Environment binding characteristics
I
II
III
IV
V
log kw(LiCl) log kw(NaCl) log kw(KCl) log kw(CsCl) NLiCl NNaCl NKCl NCsCl AFLiCl AFNaCl AFKCl AFCsCl
0.32 0.32 0.33 0.37 13.7 13.9 23.2 20.5 0.19 0.18 0.20 0.17
0.65 0.64 0.64 0.65 35.5 46.4 90.7 25.2 0.37 0.48 0.26 0.16
0.24 0.24 0.20 0.20 12.5 20.5 22.7 163.9 0.32 0.35 0.31 0.39
0.38 0.38 0.40 0.48 30.2 26.8 37.9 19.7 0.27 0.28 0.19 0.18
0.09 0.09 0.10 0.13 68.2 61.8 84.3 47.8 0.25 0.35 0.39 0.23
a
organic solventa VI VII 0.43 0.45 0.43 0.42 56.4 43.9 56.8 45.0 0.43 0.32 0.27 0.22
0.93 0.95 0.97 0.95 18.0 36.3 364.1 15.8 0.32 0.19 1.28 0.14
VIII
IX
X
XI
XII
0.80 0.79 0.82 0.86 34.3 87.5 31.0 85.9 0.26 0.49 0.46 0.55
-0.12 -0.33 -0.30 -0.35 204.1 256.0 237.2 566.7 0.41 0.55 0.50 0.52
-0.06 -0.02 -0.10 -0.15 97.7 75.3 165.3 272.4 0.48 0.27 1.43 0.91
-0.22 -0.22 -0.30 -0.14 84.6 196.6 76.0 121.2 0.79 0.64 0.45 0.48
0.40 0.37 0.33 0.36 30.9 12.5 424.3 15.7 0.47 0.12 0.31 0.11
Roman numbers refer to solvents in Table 1. For symbols, see Experimental Section.
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TABLE 3. Similarities and Dissimilarities between Physicochemical Parameters of Organic Solvents and Their Binding Characteristics to Zein in the Presence of Salts
FIGURE 1. Typical chromatograms of organic solvents on zeincoated carbon stationary phase. 1, acetone; 2, tetrahydrofuran. Flow rate, 0.3 mL min-1. For other details, see Experimental Section. PCA has been applied for the elucidation of the relationship between the binding characteristics of solutes and their physicochemical parameters. The variables were the binding parameters (log kw, N, and AF values measured in the four mobile phases and in distilled water) and the following calculated physicochemical characteristics of solvent molecules: π is the Hansch-Fujita’s substituent constants characterizing hydrophobicity; H-Ac and H-Do are the indicator variables for proton acceptor and proton donor properties, respectively; M-RE is the molar refractivity; F and R are the Swain and Luton’s electronic parameters characterizing the inductive and resonance effects; σ-ME and σ-PA are the Hammett’s constants characterizing the electronwithdrawing power of the substituents at meta and para positions; Es is the Taft’s constant characterizing the steric effects of substituents; B1 and B4 are the Sterimol width parameters determined by distance of substituents at their maximum point perpendicular to attachment; ETOTAL is the total energy of the molecule; EHOMO is the energy of the highest occupied molecular orbital; ELUMO is the energy of the lowest unoccupied molecular orbital, v is the molar volume of the molecule; µ is the dipole moment, V is the topological index (altogether 32 variables). Observations were the solvents. These sets of physicochemical parameters equally include apolar (hydrophobicity), hydrogen bonding, electrostatic, and sterical parameters describing many aspects of the molecules. It has been frequently used in both quantitative structureactivity and quantitative structure-retention relationship studies. The parameters measured in distilled water were taken from ref 35. The ratio of variance explained was set to 90%. The dimensionality of the multidimensional matrices of principal component loadings of the absolute values of PC loadings and the principal component scores was reduced by NLMAP. Iterations for NLMAP have been carried out in each instance to the point where the difference between the last two iterations was lower than 10-8. The effect of the various physicochemical parameters of monovalent cations on the binding characteristics of solvents to zein was elucidated by stepwise regression analysis (SRA) (36). Calculation was performed three times with the dependent variables being separate from the log kw, N, and AF values determined in mobile phases containing salt. They were normalized by dividing them with the corresponding values determined in distilled water as the mobile phase. Independent variables were the following parameters of monovalent cations: first and second coordination numbers; ion radii belonging to the first and second coordination numbers; enthalpy of hydration; ionization potency; enthalpies of first, second, and third ionization energies; electron affinity; hydrated ion radii; and ion radii and lattice energy in chloride salt. The acceptance limit was set to 95% significance; the number of accepted variables was not limited. 2838
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no. of principal component
Eigenvalue
variance explained (%)
total variance explained (%)
1 2 3 4 5 6 7
13.33 5.97 3.21 2.47 1.98 1.45 1.20
41.66 18.67 10.04 7.74 6.19 4.54 3.76
41.66 60.33 70.37 78.11 84.30 88.84 92.59
Principal Component Loadings no. of principal component parameter
1
2
3
4
5
6
7
log kw(H2O) log kw(LiCl) log kw(NaCl) log kw(KCl) log kw(CsCl) NH2O NLiCl NNaCl NKCl NCsCl AFH2O AFLiCl AFNaCl AFKCl AFCsCl π H-Ac H-Do M-RE F R σ-ME σ-PA Es B1 B4 ETOTAL EHOMO ELUMO v µ V
-0.94 -0.92 -0.94 -0.94 -0.94 0.77 0.83 0.81 0.75 0.76 0.78 0.64 0.53 0.20 0.63 -0.56 0.65 0.85 -0.35 0.75 -0.74 -0.02 -0.70 0.01 0.52 -0.12 -0.31 -0.16 0.28 -0.42 -0.29 -0.43
0.23 0.19 0.23 0.20 0.21 -0.46 0.07 0.07 0.04 -0.21 0.10 0.32 -0.15 0.52 -0.13 0.65 0.62 0.47 0.87 0.51 -0.64 -0.23 -0.65 -0.03 0.52 0.19 0.45 0.47 0.44 0.62 -0.46 0.79
0.07 0.04 0.02 0.08 0.14 0.05 0.04 0.23 -0.18 -0.07 -0.07 -0.13 0.36 -0.06 0.20 -0.29 0.27 -0.05 0.29 0.29 0.08 0.95 0.22 -0.70 0.36 -0.55 -0.08 0.33 -0.73 0.06 -0.17 0.17
-0.01 0.02 0.07 0.04 0.00 0.23 -0.22 -0.23 -0.19 0.00 0.40 -0.07 -0.40 0.54 0.36 0.20 -0.14 0.08 -0.13 0.20 -0.03 -0.10 0.04 -0.59 0.40 0.08 0.40 -0.53 -0.16 0.17 0.55 -0.30
0.21 0.29 0.21 0.24 0.15 0.07 0.33 0.30 0.48 0.48 -0.07 -0.16 0.30 0.50 0.39 0.12 -0.14 -0.04 0.07 -0.17 0.12 -0.06 0.10 0.10 -0.21 -0.38 0.34 -0.04 0.12 -0.29 0.07 0.15
0.06 0.12 0.12 0.09 0.16 0.22 -0.25 0.15 -0.34 -0.18 -0.10 0.44 0.42 -0.13 -0.18 -0.13 0.08 0.12 -0.01 0.09 -0.06 0.04 -0.05 -0.05 -0.08 0.01 0.39 -0.49 0.19 -0.39 -0.12 0.02
0.08 0.12 -0.02 0.05 0.04 -0.04 0.27 0.33 0.08 0.23 -0.36 -0.06 0.05 -0.23 -0.13 0.24 -0.22 0.06 -0.13 0.05 0.04 -0.08 0.08 -0.33 0.31 0.54 0.05 -0.04 -0.12 0.02 -0.21 -0.18
a Results of principal component analysis. Parameters having a considerable contribution to the given principal component are italic. For symbols, see Experimental Section.
Softwares for PCA and NLMAP were facilitated by Dr. Barna Borda´s, Plant Protection Institute, Hungarian Academy of Sciences (Budapest, Hungary), software for SRA was purchased from Compu-Drug Ltd (Budapest, Hungary).
Results and Discussion Methanol and ethanol were not retained on the protein column, indicating that these compounds do not interact with zein. Some typical chromatograms are shown in Figure 1. They demonstrate that the retention (binding strength) and the peak shape (width and symmetry) of organic solvents are markedly different, suggesting the different binding characteristics of solutes. Chromatograms further illustrate that zein can really differentiate between the solvents. The binding characteristics of solvent are compiled in Table 2. The data entirely support our previous qualitative conclusions that the binding characteristics equally depend on both the chemical structure of the organic solvent and the type of monovalent cation present in the environment. Both intraday and interday reproducibilities were lower than 1.8%, proving the good stability of the zein-coated stationary phase and the reliability of the HPLC system. The results of PCA are compiled in Table 3. Variables (both binding characteristic and physicochemical param-
FIGURE 2. Similarity and dissimilarity between the physicochemical parameters of organic solvents and their binding characteriztics to zein. Two-dimensional nonlinear map of principal component loadings calculated from the original loadings. No. of iterations: 133; maximum error: 6.28 × 10-2. For symbols, see Experimental Section. Interrelated physicochemical parameters and binding characteristics are encircled. eters) having high loading in the same principal component are intercorrelated; consequently, physicochemical parameters having high loading in the same principal component as the binding characteristics exert a considerable impact on the solvent-zein interaction. Seven PC components explain the overwhelming majority of the variation present in the original 32 variables, leaving only 7.41% of variance unexplained. Unfortunately, PCA does not define these background (theoretical) variables as concrete physicochemical or physical entities; it only indicates their mathematical possibility. Except for AFKCl, each binding parameter has a high loading in the first principal component, however, with both positive and negative signs. This finding indicates that width (N) and asymmetry (AF) of the distribution of adsorption sites are higly intercorrelated and are higher for solvents strongly retained on the zein surface (large log kw values). Some physicochemical characteristics also have high loadings in the first principal component. As hydrophobic, hydrophilic, and steric parameters are equally represented with high loadings, it can be assumed that the binding of organic solvents to zein depends on more than one physicochemical parameter, demonstrating the involvement of various interactive forces in the interaction (mixed binding mechanism). The two-dimensional nonlinear map of principal component loadings calculated from the original PC loadings is depicted in Figure 2. The scales of each map are dimensionless numbers indicating only the distribution of points on the two-dimensional plane. Points representing both binding characteristic and physicochemical parameters are near to each other when they are intercorrelated. The log kw values form a cluster with the hydrophobicity (π), electronic parameter characterizing resonance effect (R), and electronwithdrawing capacity (σ-PA), demonstrating that these molecular parameters influence the strength of solventzein interaction. This clustering can be tentatively explained by the supposition that pollutants bind by hydrophobic forces to the apolar side chains of amino acids and by electrostatical
forces to the polar amino acid residues on the surface of zein. The N and AF values form a common cluster with the proton donor and acceptor properties (H-Ac and H-Do), electronic parameter characterizing inductive effect (F), and sterical parameter B1. This result demonstrates that mainly electrostatic, polar interactions account for the distribution of the adsorption centers; however, sterical correspondence also influences the distribution. The inclusion of the hydrogen-donor and hydrogen-acceptor capacity of solvent molecules indicates that hydrogen bonding is also involved in the solvent-zein interaction. The complexity of the distribution pattern of binding characteristics and physicochemical parameters suggests that the solute molecules can interact with more than one amino acid residue, reflecting the nonselective character of the binding. This distribution further indicates that the individual amino acid residue responsible for the binding cannot be defined. The two-dimensional nonlinear map of principal component loadings calculated from the absolute values of PC loadings is depicted in Figure 3. Each variable having a high loading in the first PC forms a cluster independent of the positive or negative sign. The same variables presenting two clusters in the previous map are molded into one. This scattering of variables illustrates that the binding parameters are highly intercorrelated and that they are equally influenced by hydrophilic, electrostatic, and sterical interactive forces. The two-dimensional nonlinear map of PC variables is shown in Figure 4. Interestingly, solvents with one or more polar hydroxyl group form two distinct clusters (clusters A and B). This fact demonstrates the paramount importance of hydrophilic interaction in the binding of solvents to zein. SRA found significant linear relationships between the binding parameters of solvents and the physicochemical characteristics of monovalent cations. The parameters of equations are compiled in Table 4. The significance level was in each instance over 99% (compare Fcalcd values with tabulated ones); however, the ratio of variance explained (r 2 % values) differed considerably from 99.90% (dependent VOL. 37, NO. 12, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 3. Similarity and dissimilarity between the physicochemical parameters of organic solvents and their binding characteriztics to zein. Two-dimensional nonlinear map of principal component loadings calculated from the absolute values of loadings. No. of iterations: 402; maximum error: 4.36 × 10-2. For symbols, see Experimental Section. Interrelated physicochemical parameters and binding characteristics are encircled.
FIGURE 4. Similarity and dissimilarity between organic solvents. Two-dimensional nonlinear map of principal component variables. No. of iterations: 103; maximum error: 4.76 × 10-2. Roman numbers refer to organic solvents in Table 1. Organic solvents showing similar binding characteristics toward zein are encircled. variable log kw) to 24.15% (dependent variable AF). This fact illustrates that other physicochemical parameters of monovalent cations not included in the calculation may also exert a marked influence on the binding parameters N and AF. The data can be explained by the assumption that more than one process accounts for the effect of monovalent cations. Cations can enhance or decrease the solubility of solvents in the mobile phase (salting-in and salting-out effect) influencing in this manner their binding to zein. Moreover, 2840
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cations can bind to the same adsorption centers on the surface of zein as solvents, competitively inhibiting the solvent-zein interaction. It can be concluded from the data that the zein-coated carbon stationary phase can be successfully used for study of the binding characteristics of organic solvents to the protein using common HPLC instrumentation. The technique allows the elucidation of the effect of ionic environment on the binding parameters too. The procedure proposed is rapid,
TABLE 4. Parameters of Linear Relationships Describing the Effect of Physicochemical Characteristics of Monovalent Cations on Their Impact on the Binding of Solvents to Zeina no. of equation parameter
I
II
III
a b1 sb1 b2 sb2 b3 sb3 b4 sb4 b5 sb5 b1 % b2 % b3 % b4 % b5 % r2 % Fcalcd F99.9% F99%
4.31 × 10-3 0.14 5.40 × 10-3 -7.84 × 10-2 5.55 × 10-3 5.75 × 10-4 2.40 × 10-4 5.37 × 10-3 2.60 × 10-3 9.08 × 10-2 2.47 × 10-2 37.23 31.10 10.18 10.78 10.70 99.90 8236.06 5.13
17.64 0.74 0.14
7.90 × 10-2 0.46 0.18 -1.63 0.77
55.14 44.86
38.89 29.28 12.61
24.15 7.16 5.12
Results of stepwise regression analysis (n ) 48). Only the physicochemical parameters influencing significantly the binding characteristics are included. I, log kw ) a + b1x1 + b2x2 + b3x3 + b4x4 + b5x5. II, N ) a + b1x6. III, AF ) a + b1x1 + b2x7. x1 and x2 ) first and second coordination numbers; x3 ) enthalpy of first ionization energy; x4 and x5 ) lattice energy and ion radii in chloride salt; x6 ) hydrated ion radii; x7 ) ion radii belonging to the second coordination number. a
reliable, relatively simple, and can replace other physicochemical methods such as equilibrium dialysis, thermogravimetry, and differential scanning calorimetry. PCA followed by a nonlinear mapping technique is suitable for the elucidation of the relationship between the binding characteristics and physicochemical parameters of this set of organic solvents.
Literature Cited (1) Ockenden, W. A.; Sweetman, A. J.; Prest, H. F.; Steinnes, E.; Jones, K. C. Environ. Sci. Technol. 1998, 32, 2795-2803. (2) Reuter, J. E.; Allen, B. C.; Richards, R. C.; Pankow, J. F.; Goldman, C. R.; Scholl, R. L.; Seyfried, J. S. Environ. Sci. Technol. 1998, 32, 3666-3672. (3) Christensen, E. R. Water Sci. Technol. 1998, 37, 149-156. (4) Papaefstathiou, I.; de Castro, M. D. L. J. Chromatogr. A 1997, 779, 352-359. (5) Ichihara, G.; Saito, I.; Kamijima, M.; Yu, X.; Shibata, E.; Toida, M.; Takeuchi, Y. Int. Arch. Occup. Enivron. Health 1998, 72, 100-104.
(6) Scherer, G.; Renner, T.; Meger, M. J. Chromatogr. B 1998, 717, 179-199. (7) Lieber, C. S. Clin. Chim. Acta 1997, 257, 59-84. (8) Ogata, N.; Shibata, T. Res. Commun. Mol. Pathol. Pharmacol. 2000, 107, 167-173. (9) Garone, L.; Steiner, R. F. Arch. Biochem. Biophys. 1990, 276, 12-18. (10) Cserha´ti, T.; Szo¨gyi, M. Peptides 1995, 16, 165-173. (11) Solte´s, L.; Bree´, F.; Sebille, B. Biochem. Pharmacol. 1985, 34, 4331-4334. (12) Cserha´ti, T.; Valko´, K. Chromatographic Determination of Molecular Interactions; CRC Press: Boca Raton, FL, 1993. (13) Mardia, K. V.; Kent, J. T.; Bibby, J. M. Multivariate Analysis; Academic Press: London, 1979; pp 213-254. (14) Drew, M. G. B.; Wilden, G. R. H.; Spillane, W. J.; Walsh, R. M.; Ryder, C. A.; Simmie, J. M. J. Agric. Food Chem. 1998, 46, 30163026. (15) Seybold, P. G. SAR QSAR Environ. Res. 1999, 10, 101-115. (16) Sarbu, C.; Todor, S. J. Planar Chromatogr.-Mod. TLC 1998, 11, 123-126. (17) Mannhold, R.; Cruciani, G.; Dross, K.; Rekker, R. J. Comput.Aid. Mol. Des. 1998, 12, 573-581. (18) He´berger, K.; Lopata, A. J. Org. Chem. 1998, 63, 8646-8653. (19) He´berger, K.; Go¨rge´nyi, M. J. Chromatogr. A 1999, 845, 21-31. (20) Damborsky, J.; Berglund, A.; Kuty, M.; Ansorgova´, A.; Nagata, A.; Sjo¨stro¨m, M. Quant. Struct.-Act. Relat. 1998, 17, 450-458. (21) Zaliani, A.; Gancia, E. J. Chem. Inf. Comput. Sci. 1999, 39, 525533. (22) Katritzky, A. R.; Tamm, T.; Wang, Y.; Karelson, M. J. Chem. Inf. Comput. Sci. 1999, 39, 692-698. (23) He´berger, K. Chemom. Intell. Lab. Syst. 1999, 47, 41-49. (24) Sammon, J. W., Jr. IEEE Trans. Comput. 1969, C18, 401-407. (25) Forga´cs, E.; Ko´sa, A.; Csiktusna´di Kiss, G.; Kaliszan, R.; Haber, P.; Nasal, A. J. Liq. Chromatogr. Relat. Technol. 1998, 21, 25232534. (26) Cserha´ti, T.; Forga´cs, E. Carbohydr. Polym. 1999, 38, 171-177. (27) Cserha´ti, T.; Forga´cs, E.; Ko´sa, A.; Csiktusna´di Kiss, G.; Kaliszan, R.; Haber, P.; Nasal, A.; O ¨ rsi, F. J. Chromatogr. Sci. 1999, 159164. (28) Vrbanova´, A.; Gregorova´, D.; Cserha´ti, T.; Forga´cs, E. Int. Biodeterior. Biodegrad. 1999, 43, 207-211. (29) Berek, D.; Novak, I. Chromatographia 1990, 30, 582-590. (30) Chiantore, O.; Novak, I.; Berek, D. Anal. Chem. 1988, 60, 638642. (31) Matisova, E.; Skrabakova, S. Anal. Chim. Acta 1995, 309, 181188. (32) Skrabakova, S.; Matisova, E.; Benicka, E.; Novak, I.; Berek, D. J. Chromatogr. 1994, 665, 27-32. (33) Matisova, E.; Skrabakova, S. J. Chromatogr. A 1995, 707, 145179. (34) Cserha´ti, T.; Forga´cs, E.; Deyl, Z.; Miksik, I.; Eckhardt, A. J. Chromatogr. A 2003, 987, 403-408. (35) Zagyi, M.; Forga´cs, E.; Proda´n, M.; Cserha´ti, T.; Berek, D. Anal. Sci. (in press). (36) Mager, H. Moderne Regressionsanalyse; Salle: Sauerlander, Frankfurt, 1982; pp 135-157.
Received for review October 31, 2002. Revised manuscript received April 1, 2002. Accepted April 2, 2003. ES0210087
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