Chromatographic Retention Times of Polychlorinated Biphenyls: from Structural Information to Property Characterization

Jäntschi, Lorentz; Bolboaca, Sorana D.; Diudea, Mircea V.

doi:10.3390/i8111125

Open AccessArticle

Chromatographic Retention Times of Polychlorinated Biphenyls: from Structural Information to Property Characterization

by

Lorentz Jäntschi

¹,

Sorana D. Bolboaca

^2,*

and

Mircea V. Diudea

³

¹

Technical University of Cluj-Napoca, 103-105 Muncii Bvd, Cluj-Napoca, 400641 Romania

²

“Iuliu Hatieganu” University of Medicine and Pharmacy Cluj-Napoca, 6 Louis Pasteur, Cluj-Napoca, 400349 Romania

³

Babes-Bolyai University, 11 Arany Janos, Cluj-Napoca, 400028, Romania

^*

Author to whom correspondence should be addressed.

Int. J. Mol. Sci. 2007, 8(11), 1125-1157; https://doi.org/10.3390/i8111125

Submission received: 16 August 2007 / Revised: 9 November 2007 / Accepted: 13 November 2007 / Published: 22 November 2007

(This article belongs to the Special Issue The Chemical Bond and Bonding)

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Abstract

:

The paper presents a unitary approach of the use of a Molecular Descriptors Family in structure-property/activity relationships, particularly in modelling the chromatographic retention times of polychlorinated biphenyls. Starting from molecular structure, viewed as a graph, and considering the bonds and bond types, atom types and often the 3D geometry of the molecule, a huge family of molecular descriptors called MDF was calculated. A preliminary selection of MDF members was done by simple linear regression (LR) against the measured property. The best fitted MDF subset is then submitted to multivariate linear regression (MLR) analysis in order to find the best pairs of MDF members that produce a reliable QSPR (Quantitative Structure-Property Relationship) model. The predictive capability was finally tested by randomly splitting of data into training and test sets. The best obtained models are presented and the results are discussed.

Keywords:

Quantitative Structure-Property Relationship (QSPR); Molecular Descriptors Family (MDF); Polychlorinated Biphenyls (PCBs); Chromatographic Retention Time

1. Introduction

Polychlorinated biphenyls (PCBs), organic compounds with 1 to 10 chlorine atoms attached to biphenyl, have the general chemical formula C₁₂H_10-xCl_x. First manufactured by Monsanto in 1929, the PCBs production was banned in the 1970^th due to the high toxicity of most PCBs (209) and mixtures [1]. PCBs were used as insulating fluids for industrial transformers and capacitors, and are known as persistent organic pollutants. Even if the production of the PCBs was stopped, they still have an influence on the human [2–4] and animal [5] health due to their accumulation in the environment. Moreover, the toxicity and carcinogenicity of PCBs could be related to mechanistic studies of their truncated analogue vynil chloride [6]. Ecological and toxicological aspects of polychlorinated biphenyls (PCBs) in the environment are under investigation due to their worldwide distribution [7–10].

Starting with the 20^th century, several mathematical approaches, that link chemical structure and property/activity in a quantitative manner, have been introduced [11]. Nowadays, quantitative structure-property/activity relationships (QSPRs/QSARs) are currently used in pharmaceutical chemistry, toxicology and other related fields [12].

A series of properties and activities of PCBs have been investigated by QSPR/QSAR modelling: aqueous solubility [13], gas/particle partitioning in the atmosphere [14], photo degradation half-life in n-hexane solution under UV irradiation [15], n-octanol/water partition coefficients [16,17], vaporization [18,19], and sublimation enthalpy [20]. The retention time of PCB congeners has also been previously investigated and reported [21–25]. Some of the reported results are: • Hasan and Jurs [22] - five-variable regression equation with R² = 0.997 and standard deviation of 0.017; • Liu et. all [24] - five-variable regression equation with the correlation coefficient of 0.9964 (R² = 0.9928); • Ren et. all [25] - four descriptors regression model with a correlation coefficient of 0.988 (R² = 0.9761) for the test set and an average absolute relative deviation of 3.08%.

The family of molecular descriptors MDF, designed by treating the interactions among fragments of a molecular structure with the formalism of electrostatic fields and potentials, and molecular topology as well, was developed and tested in QSPR/QSAR studies [26–29].

The aim of the present study was to investigate the ability of our MDF in modelling the retention times of 209 polychlorinated biphenyls.

2. Materials and Methods

2.1 Polychlorinated Biphenyls (PCBs)

The relative response times of all PCBs obtained by using temperature-programmed, highresolution gas chromatography on a capillary column of SE-54, reported by Mullin et al. [30] served as experimental data in this study.

Molecular structure of PCBs was drawn by using HyperChem software [31] and their 3D geometry optimised at the Extended Hückel level of theory. These calculations also provided partial charges of atoms inside the molecules. The output files *.hin files, which store the information about topology, geometry and charge distribution of the PCBs, represented the primary data for the generation of the molecular descriptors family.

2.2 Methodology of using Molecular Descriptors Family in QSPR/QSAR

Our MDF implements three criteria of fragmentation, related to pairs of atoms, in order to generate molecular fragments. Let i and j be the atoms forming a pair. The criteria are as follows:

(a): A minimal fragment is that one containing only the atom i, while a maximal fragment will contain all the atoms connected to i, excluding the atom j.
(b): A Szeged fragment is the set of vertices located closer to i than j (a distance-based criterion), the distance d(i, k) being lesser than d(k, j), and
(c): A Cluj fragment is generated by excluding the path from i to j (except its terminal points) and then applying the above Szeged criterion.

Every MDF member is named with seven ordered case sensitive letters: lMfOIpd, every letter encoding an operator, as follows.

The 7^th letter (d) encodes the distance metric and is either ‘g’ (geometric) or ‘t’ (topological). The 6^th letter (p) encodes the atomic property and can be ‘M’ (mass), ‘Q’ (charge), ‘C’ (cardinality), ‘E’ (electronegativity), ‘G’ (group electronegativity), or ‘H’ (number of attached hydrogens). The 5^th letter (I) encodes the interaction descriptor (involving two participants): ‘D(d)’, ‘d(1/d)’, ‘O(p₁)’, ‘o(1/p₁)’, ‘P(p₁p₂)’, ‘p(1/p₁p₂)’, ‘Q(√p₁p₂)’, ‘q(1/√p₁p₂)’, ‘J(p₁d)’, ‘j(1/p₁d)’, ‘K(p₁p₂d)’, ‘k(1/p₁p₂d)’, ‘L(d√p₁p₂)’, ‘l(1/d√p₁p₂)’, ‘V(p₁/d)’, ‘E(p₁/d₂)’, ‘W(p₁²/d)’, ‘w(p₁p₂/d)’, ‘F(p₁²/d₂)’, ‘f(p₁p₂/d₂)’, ‘S(p₁²/d₃), ‘s(p₁p₂/d₃)’, ‘T(p₁²/d₄)’, ‘t(p₁p₂/d₄)’. The 4^th letter (O) encodes the type of overlapping interactions, which are either scalar (‘R’, ‘r’, ‘M’, ‘m’) or vectorial (‘D’, ‘d’). The 3^rd letter (f) encodes the fragmentation algorithm and can be: ‘m’ (minimal), ‘M’ (maximal), ‘D’ (Szeged, distance based), and ‘P’ (Cluj, shortest paths based). The 2^nd letter (M) encodes overlapping fragmental descriptors, which are of the type: sized group ( ‘m’, smallest; ‘M’, largest; ‘n’, smallest absolute; ‘N’, largest absolute); averaged group ( ‘S’, sum; ‘A’, average over all values; ‘a’, S divided by the number of all fragments; ‘B’, average first by atom group and then by the whole molecule; ‘b’, by bond); geometric group (‘P’, multiplication; ‘G’, geometric mean, by fragments; ‘g’, adjusted G; ‘F’, geometric mean by atom group and then by the whole molecule; ‘f’, by bond); harmonic group (‘s’, harmonic mean, ‘H’ harmonic mean, by fragments, and similarly to above ‘h’, ‘I’, and ‘i’).

MDF values enter in QSPR/QSAR modelling after a transformation (linearization procedure), one of: ‘I’ (identity), ‘i’ (inverse), ‘A’ (absolute), ‘a’ (inverse of absolute), ‘L’ (logarithm of absolute), ‘l’ (logarithm), which are encoded by the 1^st letter.

MDF use a genetic algorithm for QSPR/QSAR modelling (genetic algorithms are a particular class of evolutionary algorithms, being categorized as global search heuristics [32]). The peculiarities of the genetic algorithm used are:

– Step 1 (implies inheritance and mutation). To the solution domain (2×6×24×6×4×19 MDF members) having the genetic representation with six letters words) are applied the linearization procedure from above, when every descendent is obtained from a parent (inheritance) through a transformation (mutation). Six times more (than parents) descendants are obtained. In this step, the fitness function is defined as “have real and distinct values”. A number of 490030 descendants dye due to mutation on PCB data set (remaining 297938 descendants, having genetic representation with seven letters words now).
– Step 2 (implies selection). To the solution domain (MDF descendants from Step 1) a bias procedure (selection) is applied. In this step, the fitness function is defined as “have distinct first nine digits of determination coefficient with measured property”. For PCBs data set, only 99806 members pass selection. From this solution domain another selection is made: best descriptor (which correlates the best with measured property (for PCBs result being presented in Eq(1)).
– Step 3 (implies crossover). Pairs of MDF members are crossover in order to obtain models with two descriptors. Two fitness functions are used here: “have better determination coefficient” and “have better cross-validation leave-one-out score”. The result for PCBs data set is given in Eq(2).

2.3 Computational Details

The MDF is calculated by a set of original programs written in PHP (Pre Hypertext Processor, [33]) and stored into a MySQL database [34] under a FreeBSD server [35]. This set of programs completes the MDF generation task. The programs create tables, insert, drop, delete, and select grants on ‘MDF’ database (Figure 1). All programs run in a directory with the name of the set of selected compounds (actually, PCB).

The first program, a_mdf_prepare.php, orders the molecules, contained as *.hin files in a ‘data’ subdirectory, in the same ordering as the measured property, contained in a ‘property.txt’ file. The names of *.hin files and corresponding property are used to create a temporary ‘PCB_tmpx’ table and finally the ‘PCB_data’ table. The second program, b_mdf_generate.php (the most time consuming procedure) it stores thousands records into the ‘PCB_tmpx’ table.

The third program, c_mdf_linearize.php, completes the ‘PCB_data’, ‘PCB_xval’, and ‘PCB_yval’ tables with linearized MDF members and statistical parameters. Note that, only real and distinct values are stored into the database. The fourth program, d_mdf_bias.php applied a bias procedure for data reduction. Finally, the fifth program, e_mdf_order.php, re-arrange the data from the ‘PCB_xval’, and ‘PCB_yval’ tables in descending order of the squared correlation coefficient. When the task is complete, the fifth program writes in the ‘ready’ table a record with the set name (Figure 2).

The QSPR/QSAR finding procedure is made by a client programs built in Delphi programming language [36]. Bivariate correlations are performed, one with any other MDF members.

A client program (Figure 3) connects the ‘MDF’ database, query the ready tables all together, for the ready set (now PCB set is ready), and runs for finding the best QSAR/QSPR model. Every new better QSPR/QSAR is stored into a table called ‘qspr_qsar’, within the same ‘MDF’ database.

This program, called i_mdf_query.php, provides complete statistical analysis of models. The user of MDF can modify, by means of this i_mdf_query.php program, the criteria for the best QSPR/QSAR models.

3. Results and Discussion

The above described procedure was used for finding the best QSPR model of the PCBs relative chromatographic retention times.

In monovariate correlation, the best MDF QSPR model was provided by the iIDRwHg MDF member, Eq(1):

\begin{array}{l} {\hat{Y}}_{1 d} = - 0.16 + 0.09 \cdot iIDRwHg \\ R^{2} = 0.9840; 95 % {CI}_{R} [0.9894 - 0.9939]; StErr = 0.02; F = 12806; p < 0.0001 \\ {Q^{2}}_{cv - loo} = 0.9838 \end{array}

(1)

where Ŷ_1d = estimated retention time by MDF-SAR equation with one descriptor; iIDRwHg = molecular descriptor; R² = square correlation coefficient; 95%CI_R = 95% confidence interval for correlation coefficient; Q²_cv-loo= cross-validation leave-one-out score.

The quality of statistics is given by R² (the square correlation coefficient), StErr (standard error of estimate), F (Fisher parameter) and p (type I error, or α error). The cross-validation leave-one-out score is given as Q². Clearly, the model shows a good predictability. The type I error of the model from Eq(1) is very small, showing a very small error of rejecting the null hypothesis when it is actually true.

About ninety-eight percents of variation in PCBs chromatographic retention time can be explained by its linear relation with a single MDF member, iIDRwHg, which accounts for the actual geometry (by the geometric distance operator (‘g’)) and the number of directly bonded hydrogen atoms (‘H’).

The best model with two descriptors was:

\begin{array}{l} {\hat{Y}}_{2 d} = - 5.96 + 0.024 \cdot ISDmsHt - 1.02 \cdot lADrtHg \\ R^{2} = 0.9967; 95 % CI [0.9977 - 0.9987]; StdErr = 0.01; F = 30752; p < 0.0001 \\ {Q^{2}}_{cv - loo} = 0.9962 \end{array}

(2)

where Ŷ_2d = estimated retention time by MDF-SAR equation with two descriptors.

The multi-colinearity analysis shown that the two descriptors used by Eq(2) rather inter-related (R²(ISDmsHt, lADrtHg) = 0.944) and each of them (R²(Y, ISDmsHt) = 0.907; rank = 12614; R²(Y, lADrtHg) = 0.973; rank = 277) are not the best descriptor in monovariate regression model (see Eq(1)). The ISDmsHt descriptor is built by a topological distance operator (‘t’) while lADrtHg takes into account the genuine distance (‘g’). Both of them consider the directly bonded hydrogen atom (‘H’). The topological description explains more than 90% of the variance, the remaining 9.7% being completed by the information on molecular geometry.

The plot corresponding to Eq(2) is illustrated in Figure 4.

The values of the best descriptors in uni and bivariate regressions (Eq(1)&(2)), the experimental and estimated chromatographic retention time, and residuals for the PCBs set are listed in Table 1.

The accuracy of description is extremely high, even as the set of molecules is quite large. The excellent model (Eq(2)), derived for such a large set, is by itself a test of predictive ability. Indeed, if various ratios training/testing selections were considered, the quality of statistics remained very high (Table 2).

As it can be observed from Table 2, the lowest R² is about 0.996 in both training and test sets, which demonstrates the ability of (ISDmsHt, lADrtHg) MDF pair to described the PCBs relative retention time (Eq(2)). Note that, R² exceeds the upper bond of the confidence interval of Eq(2) in almost 20% of cases and is less then the lower bond in other 20% of cases. In the test set, in four cases the values of Q² were greater than the upper confidence boundary.

By analysing of the obtained models (Eq(1) and Eq(2)) in the light of the previously reported models, it can be observed that with a single exception ([25], p = 0.3528) out of three the model with one descriptor - Eq(1) - did not obtains a greater squared correlation coefficient compared with models reported in the references [22] and [24] (the differences are of −0.0064 [22], and of −0.0043 [24] respectively).

Analyzing the model with two molecular descriptors it was identified a statistical significant differences between correlation coefficient of this model and of the model reported by [24] (p < 0.0001) or by [25] (p < 0.0001). There was not identified a statistical difference between the Eq(2) and the model reported by [22] (p = 0.7263). The following remarks can be revealed by summarizing the above results:

○ The MDF model obtained by Eq(1) is a better model comparing with previously reported ones [22, 24,25] in terms of number of variables used (one descriptor for the model from Eq(1), five descriptors for the model reported in [22] and [24], four descriptors for the model reported in [25]).
○ The MDF model obtained by Eq(2) is significantly better models comparing with models reported in [24] and [25] in terms of correlation coefficients. Moreover, it is a better model comparing with model reported in [22] in terms of number of variables used (two descriptors used by the Eq(2), and five descriptors used by the model reported in [22]).

4. Conclusions

The MDF methodology provides excellent QSPR models, with good stability and predictive ability. It has the disadvantage to be time consuming (it calculates a huge pool of molecular descriptors and provides exhaustive mono- and bivariate regressions) but this is compensated by the high quality of the QSPR models.

Thus, the variance of chromatographic retention time of PCBs is 99.7% explained by two molecular descriptors, showing us that the property is related with geometry and topology, as well as with directly bounded hydrogen’s of PCBs.

The selection of the MDF members from a huge family offers not only a QSPR model, but also a strong instrument to investigate the structural causality of a measured property. Thus, the chromatographic property of PCBs is determined by the molecular topology, geometry and the nonchlorinated (i.e., the remained hydrogenated) positions on the PCB structure.

Notes

Virtual library of QSPR/QSAR models:

○ http://l.academicdirect.org/Chemistry/SARs/MDF_SARs/sar/ Training and test analysis:
○ http://l.academicdirect.org/Chemistry/SARs/MDF_SARs/qsar_qspr_s/

Figure 1. ‘MDF’ database for PCBs.

Figure 2. Preparing data for Multiple Linear Regression analysis.

Figure 3. MLR MDF QSPR client-server.

Figure 4. The plot of experimental vs chromatographic retention time (CRT) by Eq(2).

Table 1. PCBs MDF descriptors, estimated and residuals obtained by Eq(1)&Eq(2).

**Table 1.** PCBs MDF descriptors, estimated and residuals obtained by Eq(1)&Eq(2).
Mol	Y	iIDRwHg	Ŷ_1d	Y-Ŷ_1d	ISDmsHt	lADrtHg	Ŷ_2d	Y-Ŷ_2d
PCB₀₀₁	0.0997	10.02	−0.0122	−0.5363	133.20	−3.42	0.1119	−0.0122
PCB₀₀₂	0.1544	10.60	0.0041	0.1800	134.27	−3.47	0.1503	0.0041
PCB₀₀₃	0.1937	9.96	0.0376	1.6541	135.23	−3.40	0.1561	0.0376
PCB₀₀₄	0.2245	10.14	0.0054	0.2377	134.89	−3.42	0.2191	0.0054
PCB₀₀₅	0.2785	9.75	0.0251	1.1035	133.36	−3.41	0.2534	0.0251
PCB₀₀₆	0.2709	10.15	−0.0193	−0.8496	136.72	−3.38	0.2902	−0.0193
PCB₀₀₇	0.2566	10.72	0.0028	0.1234	134.60	−3.48	0.2538	0.0028
PCB₀₀₈	0.2783	10.27	−0.0048	−0.2094	133.35	−3.43	0.2831	−0.0048
PCB₀₀₉	0.2570	11.12	0.0348	1.5315	134.95	−3.55	0.2222	0.0348
PCB₀₁₀	0.2243	11.75	0.0333	1.4623	133.57	−3.57	0.1910	0.0333
PCB₀₁₁	0.3238	11.26	0.0168	0.7378	133.12	−3.52	0.3070	0.0168
PCB₀₁₂	0.3298	11.52	0.0442	1.9425	132.24	−3.57	0.2856	0.0442
PCB₀₁₃	0.3315	11.09	0.0065	0.2857	134.24	−3.49	0.3250	0.0065
PCB₀₁₄	0.2373	10.98	−0.0393	−1.7268	133.10	−3.50	0.2766	−0.0393
PCB₀₁₅	0.3387	10.98	0.0036	0.1567	131.58	−3.51	0.3351	0.0036
PCB₀₁₆	0.3625	10.45	0.0193	0.8481	132.74	−3.45	0.3432	0.0193
PCB₀₁₇	0.3398	10.97	−0.0184	−0.8086	133.06	−3.51	0.3582	−0.0184
PCB₀₁₈	0.3378	10.72	−0.0125	−0.5510	131.64	−3.50	0.3503	−0.0125
PCB₀₁₉	0.3045	10.16	0.0042	0.1849	132.62	−3.44	0.3003	0.0042
PCB₀₂₀	0.4170	11.56	0.0015	0.0644	132.66	−3.57	0.4155	0.0015
PCB₀₂₁	0.4135	11.09	−0.0179	−0.7855	133.32	−3.50	0.4314	−0.0179
PCB₀₂₂	0.4267	11.05	0.0005	0.0212	131.66	−3.52	0.4262	0.0005
PCB₀₂₃	0.3770	11.05	−0.0239	−1.0517	132.19	−3.51	0.4009	−0.0239
PCB₀₂₄	0.3508	10.52	0.0042	0.1838	131.49	−3.46	0.3466	0.0042
PCB₀₂₅	0.3937	10.80	−0.0283	−1.2445	133.28	−3.48	0.4220	−0.0283
PCB₀₂₆	0.3911	10.24	−0.0015	−0.0653	133.94	−3.42	0.3926	−0.0015
PCB₀₂₇	0.3521	10.75	0.0056	0.2482	132.13	−3.50	0.3465	0.0056
PCB₀₂₈	0.4031	10.23	−0.0294	−1.2916	131.25	−3.45	0.4325	−0.0294
PCB₀₂₉	0.3820	12.03	−0.0161	−0.7060	132.78	−3.65	0.3981	−0.0161
PCB₀₃₀	0.3165	11.48	−0.0323	−1.4195	133.66	−3.57	0.3488	−0.0323
PCB₀₃₁	0.4094	11.55	0.0086	0.3793	132.00	−3.60	0.4008	0.0086
PCB₀₃₂	0.3636	11.22	0.0089	0.3932	131.75	−3.57	0.3547	0.0089
PCB₀₃₃	0.4163	11.25	0.0057	0.2490	132.12	−3.58	0.4106	0.0057
PCB₀₃₄	0.3782	12.89	−0.0103	−0.4521	130.80	−3.73	0.3885	−0.0103
PCB₀₃₅	0.4738	12.32	0.0138	0.6063	131.52	−3.67	0.4600	0.0138
PCB₀₃₆	0.4375	12.42	0.0027	0.1167	130.24	−3.68	0.4348	0.0027
PCB₀₃₇	0.4858	11.87	0.0184	0.8096	129.97	−3.61	0.4674	0.0184
PCB₀₃₈	0.5102	12.09	0.0635	2.7897	130.07	−3.66	0.4467	0.0635
PCB₀₃₉	0.4488	11.53	0.0041	0.1782	131.14	−3.59	0.4447	0.0041
PCB₀₄₀	0.5102	11.58	0.0012	0.0545	129.81	−3.60	0.5090	0.0012
PCB₀₄₁	0.4990	12.15	−0.0127	−0.5568	130.26	−3.67	0.5117	−0.0127
PCB₀₄₂	0.4870	11.30	−0.0324	−1.4222	129.77	−3.59	0.5194	−0.0324
PCB₀₄₃	0.4587	12.11	−0.0267	−1.1744	130.59	−3.66	0.4854	−0.0267
PCB₀₄₄	0.4832	11.60	−0.0088	−0.3869	129.80	−3.61	0.4920	−0.0088
PCB₀₄₅	0.4334	12.57	0.0004	0.0168	130.50	−3.74	0.4330	0.0004
PCB₀₄₆	0.4450	13.43	0.0088	0.3881	128.67	−3.82	0.4362	0.0088
PCB₀₄₇	0.4639	12.87	−0.0562	−2.4723	128.32	−3.76	0.5201	−0.0562
PCB₀₄₈	0.4651	12.62	−0.0098	−0.4320	128.24	−3.75	0.4749	−0.0098
PCB₀₄₉	0.4610	14.04	−0.0314	−1.3821	126.70	−3.90	0.4924	−0.0314
PCB₀₅₀	0.4007	10.02	−0.0122	−0.5363	133.20	−3.42	0.1119	−0.0122
PCB₀₅₁	0.4242	10.60	0.0041	0.1800	134.27	−3.47	0.1503	0.0041
PCB₀₅₂	0.4557	9.96	0.0376	1.6541	135.23	−3.40	0.1561	0.0376
PCB₀₅₃	0.4187	10.14	0.0054	0.2377	134.89	−3.42	0.2191	0.0054
PCB₀₅₄	0.3800	9.75	0.0251	1.1035	133.36	−3.41	0.2534	0.0251
PCB₀₅₅	0.5562	10.15	−0.0193	−0.8496	136.72	−3.38	0.2902	−0.0193
PCB₀₅₆	0.5676	10.72	0.0028	0.1234	134.60	−3.48	0.2538	0.0028
PCB₀₅₇	0.5515	10.27	−0.0048	−0.2094	133.35	−3.43	0.2831	−0.0048
PCB₀₅₈	0.5267	11.12	0.0348	1.5315	134.95	−3.55	0.2222	0.0348
PCB₀₅₉	0.4860	11.75	0.0333	1.4623	133.57	−3.57	0.1910	0.0333
PCB₀₆₀	0.5676	11.26	0.0168	0.7378	133.12	−3.52	0.3070	0.0168
PCB₀₆₁	0.5331	11.52	0.0442	1.9425	132.24	−3.57	0.2856	0.0442
PCB₀₆₂	0.4685	11.09	0.0065	0.2857	134.24	−3.49	0.3250	0.0065
PCB₀₆₃	0.5290	10.98	−0.0393	−1.7268	133.10	−3.50	0.2766	−0.0393
PCB₀₆₄	0.4999	10.98	0.0036	0.1567	131.58	−3.51	0.3351	0.0036
PCB₀₆₅	0.4671	10.45	0.0193	0.8481	132.74	−3.45	0.3432	0.0193
PCB₀₆₆	0.5447	10.97	−0.0184	−0.8086	133.06	−3.51	0.3582	−0.0184
PCB₀₆₇	0.5214	10.72	−0.0125	−0.5510	131.64	−3.50	0.3503	−0.0125
PCB₀₆₈	0.5040	10.16	0.0042	0.1849	132.62	−3.44	0.3003	0.0042
PCB₀₆₉	0.4510	11.56	0.0015	0.0644	132.66	−3.57	0.4155	0.0015
PCB₀₇₀	0.5407	11.09	−0.0179	−0.7855	133.32	−3.50	0.4314	−0.0179
PCB₀₇₁	0.4989	11.05	0.0005	0.0212	131.66	−3.52	0.4262	0.0005
PCB₀₇₂	0.4984	11.05	−0.0239	−1.0517	132.19	−3.51	0.4009	−0.0239
PCB₀₇₃	0.4554	10.52	0.0042	0.1838	131.49	−3.46	0.3466	0.0042
PCB₀₇₄	0.5341	10.80	−0.0283	−1.2445	133.28	−3.48	0.4220	−0.0283
PCB₀₇₅	0.4643	10.24	−0.0015	−0.0653	133.94	−3.42	0.3926	−0.0015
PCB₀₇₆	0.5408	10.75	0.0056	0.2482	132.13	−3.50	0.3465	0.0056
PCB₀₇₇	0.6295	10.23	−0.0294	−1.2916	131.25	−3.45	0.4325	−0.0294
PCB₀₇₈	0.6024	12.03	−0.0161	−0.7060	132.78	−3.65	0.3981	−0.0161
PCB₀₇₉	0.5894	11.48	−0.0323	−1.4195	133.66	−3.57	0.3488	−0.0323
PCB₀₈₀	0.5464	11.55	0.0086	0.3793	132.00	−3.60	0.4008	0.0086
PCB₀₈₁	0.6149	11.22	0.0089	0.3932	131.75	−3.57	0.3547	0.0089
PCB₀₈₂	0.6453	11.25	0.0057	0.2490	132.12	−3.58	0.4106	0.0057
PCB₀₈₃	0.6029	12.89	−0.0103	−0.4521	130.80	−3.73	0.3885	−0.0103
PCB₀₈₄	0.5744	12.32	0.0138	0.6063	131.52	−3.67	0.4600	0.0138
PCB₀₈₅	0.6224	12.42	0.0027	0.1167	130.24	−3.68	0.4348	0.0027
PCB₀₈₆	0.6105	11.87	0.0184	0.8096	129.97	−3.61	0.4674	0.0184
PCB₀₈₇	0.6175	12.09	0.0635	2.7897	130.07	−3.66	0.4467	0.0635
PCB₀₈₈	0.5486	11.53	0.0041	0.1782	131.14	−3.59	0.4447	0.0041
PCB₀₈₉	0.5779	11.58	0.0012	0.0545	129.81	−3.60	0.5090	0.0012
PCB₀₉₀	0.5814	12.15	−0.0127	−0.5568	130.26	−3.67	0.5117	−0.0127
PCB₀₉₁	0.5549	11.30	−0.0324	−1.4222	129.77	−3.59	0.5194	−0.0324
PCB₀₉₂	0.5742	12.11	−0.0267	−1.1744	130.59	−3.66	0.4854	−0.0267
PCB₀₉₃	0.5437	11.60	−0.0088	−0.3869	129.80	−3.61	0.4920	−0.0088
PCB₀₉₄	0.5331	12.57	0.0004	0.0168	130.50	−3.74	0.4330	0.0004
PCB₀₉₅	0.5464	13.43	0.0088	0.3881	128.67	−3.82	0.4362	0.0088
PCB₀₉₆	0.5057	12.87	−0.0562	−2.4723	128.32	−3.76	0.5201	−0.0562
PCB₀₉₇	0.6100	12.62	−0.0098	−0.4320	128.24	−3.75	0.4749	−0.0098
PCB₀₉₈	0.5415	14.04	−0.0314	−1.3821	126.70	−3.90	0.4924	−0.0314
PCB₀₉₉	0.5880	10.02	−0.0122	−0.5363	133.20	−3.42	0.1119	−0.0122
PCB₁₀₀	0.5212	10.60	0.0041	0.1800	134.27	−3.47	0.1503	0.0041
PCB₁₀₁	0.5816	9.96	0.0376	1.6541	135.23	−3.40	0.1561	0.0376
PCB₁₀₂	0.5431	10.14	0.0054	0.2377	134.89	−3.42	0.2191	0.0054
PCB₁₀₃	0.5142	9.75	0.0251	1.1035	133.36	−3.41	0.2534	0.0251
PCB₁₀₄	0.4757	10.15	−0.0193	−0.8496	136.72	−3.38	0.2902	−0.0193
PCB₁₀₅	0.7049	10.72	0.0028	0.1234	134.60	−3.48	0.2538	0.0028
PCB₁₀₆	0.6680	10.27	−0.0048	−0.2094	133.35	−3.43	0.2831	−0.0048
PCB₁₀₇	0.6628	11.12	0.0348	1.5315	134.95	−3.55	0.2222	0.0348
PCB₁₀₈	0.6626	11.75	0.0333	1.4623	133.57	−3.57	0.1910	0.0333
PCB₁₀₉	0.6016	11.26	0.0168	0.7378	133.12	−3.52	0.3070	0.0168
PCB₁₁₀	0.6314	11.52	0.0442	1.9425	132.24	−3.57	0.2856	0.0442
PCB₁₁₁	0.6183	11.09	0.0065	0.2857	134.24	−3.49	0.3250	0.0065
PCB₁₁₂	0.5986	10.98	−0.0393	−1.7268	133.10	−3.50	0.2766	−0.0393
PCB₁₁₃	0.5862	10.98	0.0036	0.1567	131.58	−3.51	0.3351	0.0036
PCB₁₁₄	0.6828	10.45	0.0193	0.8481	132.74	−3.45	0.3432	0.0193
PCB₁₁₅	0.6171	10.97	−0.0184	−0.8086	133.06	−3.51	0.3582	−0.0184
PCB₁₁₆	0.6132	10.72	−0.0125	−0.5510	131.64	−3.50	0.3503	−0.0125
PCB₁₁₇	0.6150	10.16	0.0042	0.1849	132.62	−3.44	0.3003	0.0042
PCB₁₁₈	0.6693	11.56	0.0015	0.0644	132.66	−3.57	0.4155	0.0015
PCB₁₁₉	0.5968	11.09	−0.0179	−0.7855	133.32	−3.50	0.4314	−0.0179
PCB₁₂₀	0.6256	11.05	0.0005	0.0212	131.66	−3.52	0.4262	0.0005
PCB₁₂₁	0.5518	11.05	−0.0239	−1.0517	132.19	−3.51	0.4009	−0.0239
PCB₁₂₂	0.6871	10.52	0.0042	0.1838	131.49	−3.46	0.3466	0.0042
PCB₁₂₃	0.6658	10.80	−0.0283	−1.2445	133.28	−3.48	0.4220	−0.0283
PCB₁₂₄	0.6584	10.24	−0.0015	−0.0653	133.94	−3.42	0.3926	−0.0015
PCB₁₂₅	0.6142	10.75	0.0056	0.2482	132.13	−3.50	0.3465	0.0056
PCB₁₂₆	0.7512	10.23	−0.0294	−1.2916	131.25	−3.45	0.4325	−0.0294
PCB₁₂₇	0.7078	12.03	−0.0161	−0.7060	132.78	−3.65	0.3981	−0.0161
PCB₁₂₈	0.7761	11.48	−0.0323	−1.4195	133.66	−3.57	0.3488	−0.0323
PCB₁₂₉	0.7501	11.55	0.0086	0.3793	132.00	−3.60	0.4008	0.0086
PCB₁₃₀	0.7184	11.22	0.0089	0.3932	131.75	−3.57	0.3547	0.0089
PCB₁₃₁	0.6853	11.25	0.0057	0.2490	132.12	−3.58	0.4106	0.0057
PCB₁₃₂	0.7035	12.89	−0.0103	−0.4521	130.80	−3.73	0.3885	−0.0103
PCB₁₃₃	0.6871	12.32	0.0138	0.6063	131.52	−3.67	0.4600	0.0138
PCB₁₃₄	0.6796	12.42	0.0027	0.1167	130.24	−3.68	0.4348	0.0027
PCB₁₃₅	0.6563	11.87	0.0184	0.8096	129.97	−3.61	0.4674	0.0184
PCB₁₃₆	0.6257	12.09	0.0635	2.7897	130.07	−3.66	0.4467	0.0635
PCB₁₃₇	0.7329	11.53	0.0041	0.1782	131.14	−3.59	0.4447	0.0041
PCB₁₃₈	0.7403	11.58	0.0012	0.0545	129.81	−3.60	0.5090	0.0012
PCB₁₃₉	0.6707	12.15	−0.0127	−0.5568	130.26	−3.67	0.5117	−0.0127
PCB₁₄₀	0.6707	11.30	−0.0324	−1.4222	129.77	−3.59	0.5194	−0.0324
PCB₁₄₁	0.7200	12.11	−0.0267	−1.1744	130.59	−3.66	0.4854	−0.0267
PCB₁₄₂	0.6848	11.60	−0.0088	−0.3869	129.80	−3.61	0.4920	−0.0088
PCB₁₄₃	0.6789	12.57	0.0004	0.0168	130.50	−3.74	0.4330	0.0004
PCB₁₄₄	0.6563	13.43	0.0088	0.3881	128.67	−3.82	0.4362	0.0088
PCB₁₄₅	0.6149	12.87	−0.0562	−2.4723	128.32	−3.76	0.5201	−0.0562
PCB₁₄₆	0.6955	12.62	−0.0098	−0.4320	128.24	−3.75	0.4749	−0.0098
PCB₁₄₇	0.6608	14.04	−0.0314	−1.3821	126.70	−3.90	0.4924	−0.0314
PCB₁₄₈	0.6243	10.02	−0.0122	−0.5363	133.20	−3.42	0.1119	−0.0122
PCB₁₄₉	0.6672	10.60	0.0041	0.1800	134.27	−3.47	0.1503	0.0041
PCB₁₅₀	0.5969	9.96	0.0376	1.6541	135.23	−3.40	0.1561	0.0376
PCB₁₅₁	0.6499	10.14	0.0054	0.2377	134.89	−3.42	0.2191	0.0054
PCB₁₅₂	0.6062	9.75	0.0251	1.1035	133.36	−3.41	0.2534	0.0251
PCB₁₅₃	0.7036	10.15	−0.0193	−0.8496	136.72	−3.38	0.2902	−0.0193
PCB₁₅₄	0.6349	10.72	0.0028	0.1234	134.60	−3.48	0.2538	0.0028
PCB₁₅₅	0.5666	10.27	−0.0048	−0.2094	133.35	−3.43	0.2831	−0.0048
PCB₁₅₆	0.8105	11.12	0.0348	1.5315	134.95	−3.55	0.2222	0.0348
PCB₁₅₇	0.8184	11.75	0.0333	1.4623	133.57	−3.57	0.1910	0.0333
PCB₁₅₈	0.7429	11.26	0.0168	0.7378	133.12	−3.52	0.3070	0.0168
PCB₁₅₉	0.7655	11.52	0.0442	1.9425	132.24	−3.57	0.2856	0.0442
PCB₁₆₀	0.7396	11.09	0.0065	0.2857	134.24	−3.49	0.3250	0.0065
PCB₁₆₁	0.6968	10.98	−0.0393	−1.7268	133.10	−3.50	0.2766	−0.0393
PCB₁₆₂	0.7737	10.98	0.0036	0.1567	131.58	−3.51	0.3351	0.0036
PCB₁₆₃	0.7396	10.45	0.0193	0.8481	132.74	−3.45	0.3432	0.0193
PCB₁₆₄	0.7399	10.97	−0.0184	−0.8086	133.06	−3.51	0.3582	−0.0184
PCB₁₆₅	0.6920	10.72	−0.0125	−0.5510	131.64	−3.50	0.3503	−0.0125
PCB₁₆₆	0.7572	10.16	0.0042	0.1849	132.62	−3.44	0.3003	0.0042
PCB₁₆₇	0.7814	11.56	0.0015	0.0644	132.66	−3.57	0.4155	0.0015
PCB₁₆₈	0.7068	11.09	−0.0179	−0.7855	133.32	−3.50	0.4314	−0.0179
PCB₁₆₉	0.8625	11.05	0.0005	0.0212	131.66	−3.52	0.4262	0.0005
PCB₁₇₀	0.8740	11.05	−0.0239	−1.0517	132.19	−3.51	0.4009	−0.0239
PCB₁₇₁	0.8089	10.52	0.0042	0.1838	131.49	−3.46	0.3466	0.0042
PCB₁₇₂	0.8278	10.80	−0.0283	−1.2445	133.28	−3.48	0.4220	−0.0283
PCB₁₇₃	0.8152	10.24	−0.0015	−0.0653	133.94	−3.42	0.3926	−0.0015
PCB₁₇₄	0.7965	10.75	0.0056	0.2482	132.13	−3.50	0.3465	0.0056
PCB₁₇₅	0.7611	10.23	−0.0294	−1.2916	131.25	−3.45	0.4325	−0.0294
PCB₁₇₆	0.7305	12.03	−0.0161	−0.7060	132.78	−3.65	0.3981	−0.0161
PCB₁₇₇	0.8031	11.48	−0.0323	−1.4195	133.66	−3.57	0.3488	−0.0323
PCB₁₇₈	0.7537	11.55	0.0086	0.3793	132.00	−3.60	0.4008	0.0086
PCB₁₇₉	0.7205	11.22	0.0089	0.3932	131.75	−3.57	0.3547	0.0089
PCB₁₈₀	0.8362	11.25	0.0057	0.2490	132.12	−3.58	0.4106	0.0057
PCB₁₈₁	0.7968	12.89	−0.0103	−0.4521	130.80	−3.73	0.3885	−0.0103
PCB₁₈₂	0.7653	12.32	0.0138	0.6063	131.52	−3.67	0.4600	0.0138
PCB₁₈₃	0.7720	12.42	0.0027	0.1167	130.24	−3.68	0.4348	0.0027
PCB₁₈₄	0.7016	11.87	0.0184	0.8096	129.97	−3.61	0.4674	0.0184
PCB₁₈₅	0.7848	12.09	0.0635	2.7897	130.07	−3.66	0.4467	0.0635
PCB₁₈₆	0.7416	11.53	0.0041	0.1782	131.14	−3.59	0.4447	0.0041
PCB₁₈₇	0.7654	11.58	0.0012	0.0545	129.81	−3.60	0.5090	0.0012
PCB₁₈₈	0.6920	12.15	−0.0127	−0.5568	130.26	−3.67	0.5117	−0.0127
PCB₁₈₉	0.9142	11.30	−0.0324	−1.4222	129.77	−3.59	0.5194	−0.0324
PCB₁₉₀	0.8740	12.11	−0.0267	−1.1744	130.59	−3.66	0.4854	−0.0267
PCB₁₉₁	0.8447	11.60	−0.0088	−0.3869	129.80	−3.61	0.4920	−0.0088
PCB₁₉₂	0.8269	12.57	0.0004	0.0168	130.50	−3.74	0.4330	0.0004
PCB₁₉₃	0.8397	13.43	0.0088	0.3881	128.67	−3.82	0.4362	0.0088
PCB₁₉₄	0.9620	12.87	−0.0562	−2.4723	128.32	−3.76	0.5201	−0.0562
PCB₁₉₅	0.9321	12.62	−0.0098	−0.4320	128.24	−3.75	0.4749	−0.0098
PCB₁₉₆	0.8938	14.04	−0.0314	−1.3821	126.70	−3.90	0.4924	−0.0314
PCB₁₉₇	0.8293	10.02	−0.0122	−0.5363	133.20	−3.42	0.1119	−0.0122
PCB₁₉₈	0.8845	10.60	0.0041	0.1800	134.27	−3.47	0.1503	0.0041
PCB₁₉₉	0.8494	9.96	0.0376	1.6541	135.23	−3.40	0.1561	0.0376
PCB₂₀₀	0.8197	10.14	0.0054	0.2377	134.89	−3.42	0.2191	0.0054
PCB₂₀₁	0.8875	9.75	0.0251	1.1035	133.36	−3.41	0.2534	0.0251
PCB₂₀₂	0.8089	10.15	−0.0193	−0.8496	136.72	−3.38	0.2902	−0.0193
PCB₂₀₃	0.8938	10.72	0.0028	0.1234	134.60	−3.48	0.2538	0.0028
PCB₂₀₄	0.8217	10.27	−0.0048	−0.2094	133.35	−3.43	0.2831	−0.0048
PCB₂₀₅	0.9678	11.12	0.0348	1.5315	134.95	−3.55	0.2222	0.0348
PCB₂₀₆	1.0103	11.75	0.0333	1.4623	133.57	−3.57	0.1910	0.0333
PCB₂₀₇	0.9423	11.26	0.0168	0.7378	133.12	−3.52	0.3070	0.0168
PCB₂₀₈	0.9320	11.52	0.0442	1.9425	132.24	−3.57	0.2856	0.0442
PCB₂₀₉	1.0496	11.09	0.0065	0.2857	134.24	−3.49	0.3250	0.0065

iIDRwHg, ISDmsHt, and lADrtHg = the value of the descriptor - Eq(1)& Eq(2); Ŷ_{1d, 2d} = relative retention time: estimated by Eq(1) and Eq(2), respectively; Y = relative retention time: experimental [30]; Y- Ŷ_{1d, 2d} = residuals.

Table 2. Training vs Test Experiments: Results.

**Table 2.** Training vs Test Experiments: Results.
Training set						Test set
No PCBs	Coefficients			Statistics		No PCBs	Statistics
No PCBs	Intercept	ISDmsHt	lADrtHg	R²	F	No PCBs	Q²	F
9	−6.06	0.0243	−1.0294	0.999	2640†	200	0.997	32807†
19	−6.18	0.0248	−1.0442	0.998	4827†	190	0.997	28567†
29	−5.94	0.0237	−1.0195	0.996	3342†	180	0.997	32047†
39	−5.80	0.0230	−1.0040	0.998	8406†	170	0.997	27678†
49	−5.89	0.0234	−1.0172	0.998	9608†	160	0.997	25667†
59	−6.30	0.0257	−1.0448	0.996	6924†	150	0.998	27578†
69	−6.21	0.0251	−1.0440	0.996	7641†	140	0.998	29667†
79	−5.95	0.0238	−1.0170	0.996	9186†	130	0.998	29915†
89	−6.12	0.0246	−1.0348	0.997	16315†	120	0.997	19049†
99	−6.06	0.0244	−1.0278	0.997	15763†	110	0.997	20314†
109	−6.07	0.0244	−1.0304	0.996	13489†	100	0.998	26764†
119	−6.10	0.0245	−1.0361	0.997	19333†	90	0.997	14990†
129	−6.07	0.0245	−1.0284	0.997	19823†	80	0.998	17306†
139	−5.98	0.0240	−1.0219	0.997	21316†	70	0.997	11610†
149	−6.07	0.0244	−1.0297	0.997	25972†	60	0.997	10077†
159	−6.03	0.0241	−1.0287	0.997	31071†	50	0.997	5692†
169	−6.03	0.0242	−1.0258	0.997	25723†	40	0.998	12671†
179	−5.97	0.0239	−1.0203	0.997	30942†	30	0.997	4938†
189	−6.02	0.0242	−1.0247	0.997	31570†	20	0.998	3383†
199	−6.01	0.0241	−1.0243	0.997	34566†	10	0.998	1450†

^†p < 0.0001

Acknowledgements

The research was partly supported by UEFISCSU Romania through research projects.

References

National Research Council (U.S.), Committee on the Assessment of Polychlorinated Biphenyls in the Environment. In Polychlorinated biphenyls: a report; National Academy of Sciences: Washington, 1979.
Angulo Lucena, R.; Farouk Allam, M.; Serrano Jiménez, S.; Luisa, Jodral; Villarejo, M. A review of environmental exposure to persistent organochlorine residuals during the last fifty years. Curr. Drug Safety 2007, 2(2), 163–172. [Google Scholar]
Roveda, A. M.; Veronesi, L.; Zoni, R.; Colucci, M. E.; Sansebastiano, G. Exposure to polychlorinated biphenyls (PCBs) in food and cancer risk: recent advances. Igiene e sanità pubblica 2006, 62(6), 677–696. [Google Scholar]
Lundqvist, C.; Zuurbier, M.; Leijs, M.; Johansson, C.; Ceccatelli, S.; Saunders, M.; Schoeters, G.; Ten Tusscher, G.; Koppe, J. G. The effects of PCBs and dioxins on child health. Acta. Paediatr 2006, 95(453), 55–64. [Google Scholar]
Poppenga, R. H. Current environmental threats to animal health and productivity. Vet. Clin. N. Am.-Food A 2000, 16(3), 545–558. [Google Scholar]
Bren, U.; Zupan, M.; Guengerich, F. P.; Mavri, J. Chemical Reactivity as a Tool to Study Carcinogenicity: Reaction between Chloroethylene Oxide and Guanine. J. Org. Chem 2006, 71(11), 4078–4084. [Google Scholar]
Lebeuf, M.; Noël, M.; Trottier, S.; Measures, L. Temporal trends (1987–2002) of persistent, bioaccumulative and toxic (PBT) chemicals in beluga whales (Delphinapterus leucas) from the St. Lawrence Estuary, Canada. Sci. Total Environ 2007, 383(1–3), 216–231. [Google Scholar]
Tan, J.; Cheng, S. M.; Loganath, A.; Chong, Y. S.; Obbard, J. P. Selected organochlorine pesticide and polychlorinated biphenyl residues in house dust in Singapore. Chemosphere 2007, 68(9), 1675–1682. [Google Scholar]
Borrell, A.; Cantos, G.; Aguilar, A.; Androukaki, E.; Dendrinos, P. Concentrations and patterns of organochlorine pesticides and PCBs in Mediterranean monk seals (Monachus monachus) from Western Sahara and Greece. Sci. Total Environ 2007, 381(1–3), 316–325. [Google Scholar]
Klánová, J.; Kohoutek, J.; Kostrhounová, R.; Holoubek, I. Are the residents of former Yugoslavia still exposed to elevated PCB levels due to the Balkan wars?. Part 1: air sampling in Croatia, Serbia, Bosnia and Herzegovina. Environ. Int 2007, 33(6), 719–726. [Google Scholar]
Hansch, C. Quantitative approach to biochemical structure-activity relationships. Acc. Chem. Res 1969, 2(8), 232–239. [Google Scholar]
Hansch, C.; Leo, A. Substituent Constants for Correlation Analysis in Chemistry and Biology; John Wiley & Sons: New York, 1979. [Google Scholar]
Castro, E. A.; Toropov, A. A.; Nesterova, A.I.; Nabiev, O. M. QSPR modeling aqueous solubility of polychlorinated biphenyls by optimization of correlation weights of local and global graph invariants. Central European Journal of Chemistry 2004, 2(3), 500–523. [Google Scholar]
Wei, B.; Xie, S.; Yu, M.; Wu, L. QSPR-based prediction of gas/particle partitioning of polychlorinated biphenyls in the atmosphere. Chemosphere 2007, 66(10), 1807–1820. [Google Scholar]
Niu, J. F.; Yang, Z. F.; Shen, Z. Y.; Wang, L. L. QSPRs for the prediction of photodegradation half-life of PCBs in n-hexane. SAR QSAR Environ. Res 2006, 17(2), 173–182. [Google Scholar]
Padmanabhan, J.; Parthasarathi, R.; Subramanian, V.; Chattaraj, P. K. QSPR models for polychlorinated biphenyls: n-Octanol/water partition coefficient. Bioorg. Med. Chem. Lett 2006, 14(4), 1021–1028. [Google Scholar]
Jäntschi, L.; Bolboacă, S. Molecular Descriptors Family on Structure Activity Relationships 6. Octanol-Water Partition Coefficient of Polychlorinated Biphenyls. Leonardo El. J. Pract. Technol 2006, 8, 71–86. [Google Scholar]
Puri, S.; Chickos, J. S.; Welsh, W. J. Three-dimensional quantitative structure - Property relationship (3D-QSPR) models for prediction of thermodynamic properties of polychlorinated biphenyls (PCBs): Enthalpy of vaporization. J. Chem. Inf. Comp. Sci 2002, 42(2), 299–304. [Google Scholar]
Padmanabhan, J.; Parthasarathi, R.; Subramanian, V.; Chattaraj, P. K. Using QSPR models to predict the enthalpy of vaporization of 209 polychlorinated biphenyl congeners. QSAR Comb. Sci 2007, 26(2), 227–237. [Google Scholar]
Puri, S.; Chickos, J. S.; Welsh, W. J. Three-dimensional quantitative structure - Property relationship (3D-QSPR) models for prediction of thermodynamic properties of polychlorinated biphenyls (PCBs): Enthalpy of sublimation. J. Chem. Inf. Comp. Sci 2002, 42(1), 109–116. [Google Scholar]
Devillers, J. A simple method for the prediction of the GLC retention times of all the 209 PCB congeners. Fresenius Z. Anal. Chem 1988, 332(1), 61–62. [Google Scholar]
Hasan, M.N.; Jurs, P.C. Computer-assisted prediction of gas chromatographic retention times of polychlorinated biphenyls. Anal. Chem 1988, 60(10), 978–982. [Google Scholar]
Makino, M. Novel classification to predict relative gas chromatographic retention times and noctanol/water partition coefficients of polychlorinated biphenyls. Chemosphere 1999, 39(6), 893–903. [Google Scholar]
Liu, S.-S.; Liu, Y.; Yin, D.-Q.; Wang, X.-D.; Wang, L.-S. Prediction of chromatographic relative retention time of polychlorinated biphenyls from the molecular electronegativity distance vector. J. Sep. Sci 2006, 29(2), 296–301. [Google Scholar]
Ren, Y.; Liu, H.; Yao, X.; Liu, M. An accurate QSRR model for the prediction of the GC×GCTOFMS retention time of polychlorinated biphenyl (PCB) congeners. Anal. Bioanal. Chem 2007, 388(1), 165–172. [Google Scholar]
Jäntschi, L.; Katona, G.; Diudea, M. Modeling Molecular Properties by Cluj Indices. MATCH Commun. Math. Comput. Chem 2000, 41, 151–188. [Google Scholar]
Jäntschi, L. MDF - A New QSPR/QSAR Molecular Descriptors Family. Leonardo J. Sci 2004, 4, 68–85. [Google Scholar]
Jäntschi, L. Molecular Descriptors Family on Structure Activity Relationships 1. Review of the Methodology. Leonardo El. J. Pract. Technol 2005, 6, 76–98. [Google Scholar]
Jäntschi, L.; Bolboacă, S. Results from the Use of Molecular Descriptors Family on Structure Property/Activity Relationships. Int. J. Mol. Sci 2007, 8(3), 189–203. [Google Scholar]
Mullin, M. D.; Pochini, C. M.; McCrindle, S.; Romkes, M.; Safe, S. H.; Safe, L. M. High resolution PCB analysis: synthesis and chromatographic properties of all 209 PCB congeners. Environ. Sci. Technol 1984, 18, 468–476. [Google Scholar]
HyperChem, Molecular Modelling System; [software]; ©2003. Hypercube. [cited 2007 June]. Available from: URL: http://hyper.com/products/.
Chambers, D.L. The practical handbook of genetic algorithms; Chapman & Hall: Boca Raton, 2001. [Google Scholar]
The FreeBSD Project. [online]; ©1995–2007. The FreeBSD Project. [cited 2007 June]. Available from: URL: http://freebsd.org.
Borland Software Corporation. [online]; ©1994–2007. Borland Software Corporation. [cited 2007 June]. Available from: URL: http://borland.com.

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Jäntschi, L.; Bolboaca, S.D.; Diudea, M.V. Chromatographic Retention Times of Polychlorinated Biphenyls: from Structural Information to Property Characterization. Int. J. Mol. Sci. 2007, 8, 1125-1157. https://doi.org/10.3390/i8111125

AMA Style

Jäntschi L, Bolboaca SD, Diudea MV. Chromatographic Retention Times of Polychlorinated Biphenyls: from Structural Information to Property Characterization. International Journal of Molecular Sciences. 2007; 8(11):1125-1157. https://doi.org/10.3390/i8111125

Chicago/Turabian Style

Jäntschi, Lorentz, Sorana D. Bolboaca, and Mircea V. Diudea. 2007. "Chromatographic Retention Times of Polychlorinated Biphenyls: from Structural Information to Property Characterization" International Journal of Molecular Sciences 8, no. 11: 1125-1157. https://doi.org/10.3390/i8111125

APA Style

Jäntschi, L., Bolboaca, S. D., & Diudea, M. V. (2007). Chromatographic Retention Times of Polychlorinated Biphenyls: from Structural Information to Property Characterization. International Journal of Molecular Sciences, 8(11), 1125-1157. https://doi.org/10.3390/i8111125

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Chromatographic Retention Times of Polychlorinated Biphenyls: from Structural Information to Property Characterization

Abstract

1. Introduction

2. Materials and Methods

2.1 Polychlorinated Biphenyls (PCBs)

2.2 Methodology of using Molecular Descriptors Family in QSPR/QSAR

2.3 Computational Details

3. Results and Discussion

4. Conclusions

Acknowledgements

References

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