- Research article
- Open Access
QSPR study on the octanol/air partition coefficient of polybrominated diphenyl ethers by using molecular distance-edge vector index
- Long Jiao^{1, 2}Email author,
- Mingming Gao^{3},
- Xiaofei Wang^{1} and
- Hua Li^{2}
https://doi.org/10.1186/1752-153X-8-36
© Jiao et al.; licensee Chemistry Central Ltd. 2014
- Received: 5 March 2014
- Accepted: 4 June 2014
- Published: 10 June 2014
Abstract
Background
The quantitative structure property relationship (QSPR) for octanol/air partition coefficient (K_{OA}) of polybrominated diphenyl ethers (PBDEs) was investigated. Molecular distance-edge vector (MDEV) index was used as the structural descriptor of PBDEs. The quantitative relationship between the MDEV index and the lgK_{OA} of PBDEs was modeled by multivariate linear regression (MLR) and artificial neural network (ANN) respectively. Leave one out cross validation and external validation was carried out to assess the predictive ability of the developed models. The investigated 22 PBDEs were randomly split into two groups: Group I, which comprises 16 PBDEs, and Group II, which comprises 6 PBDEs.
Results
The MLR model and the ANN model for predicting the K_{OA} of PBDEs were established. For the MLR model, the prediction root mean square relative error (RMSRE) of leave one out cross validation and external validation is 2.82 and 2.95, respectively. For the L-ANN model, the prediction RMSRE of leave one out cross validation and external validation is 2.55 and 2.69, respectively.
Conclusion
The developed MLR and ANN model are practicable and easy-to-use for predicting the K_{OA} of PBDEs. The MDEV index of PBDEs is shown to be quantitatively related to the K_{OA} of PBDEs. MLR and ANN are both practicable for modeling the quantitative relationship between the MDEV index and the K_{OA} of PBDEs. The prediction accuracy of the ANN model is slightly higher than that of the MLR model. The obtained ANN model shoud be a more promising model for studying the octanol/air partition behavior of PBDEs.
Keywords
- QSPR
- Polybrominated diphenyl ethers
- Octanol/air partition coefficient
- Molecular distance-edge vector index
- Artificial neural network
Background
Polybrominated diphenyl ethers (PBDEs) are a series of organobromine compounds that have been widely used as flame retardant in a variety of products, such as building materials, electronics, furnishings, coatings, plastics, etc [1, 2]. Although the production of some PBDEs has been restricted under the Stockholm Convention since 2010, PBDEs have already become ubiquitous pollutants in the environment. They have been detected in many environmental compartments, such as air, water, soil, vegetations, animals and humans [3, 4]. PBDEs have gained increasing attention because of their environmental persistence, bioaccumulation through the food chain, and potential risk to the human health [1, 5, 6]. PBDEs are lipophilic and semi-volatile compounds. The octanol/air partition of PBDEs may influence their fate, transport, and transformation in atmospheres [7–9]. The octanol/air partition coefficient (K_{OA}), which is defined as the ratio of solute concentration in air versus octanol when the octanol/air system is at equilibrium, is a key parameter for describing the octanol/air partition of PBDEs between the atmosphere and organic phases such as soil, aerosol, vegetation and animals. Thus, a quantitative study on the K_{OA} of PBDEs is of great importance to understand the environmental fate of PBDEs. Many efforts have been made to determine the K_{OA} of PBDEs [7, 9–11]. However, determining the K_{OA} of PBDEs is always a hard work due to the complexity of analytical methods, lack of chemical standards and high cost of experiments [4, 12–15]. Thus, the quantitative structure-property relationship (QSPR) method, which is fast, easy-to-use and cost-effective [12, 16, 17], is always used to preliminary estimate the value of K_{OA} of PBDEs. Several QSPR models for the K_{OA} of PBDEs have been reported [12–15]. In these works, quantum chemical descriptors are used as the structural descriptor of PBDEs. However, developing a QSPR model based on quantum chemical descriptors is still a complex work, because the calculation and selection of structural descriptors are always time-consuming and complicated. It is still worthwhile to develop an easy-to-use QSPR model for the K_{OA} of PBDEs. Topological index is a kind of structural descriptor which has been widely used in the QSPR researches. It can effectively describe the structure of molecules without the detailed molecular orbital calculation and energy optimization. Topological index is useful because, despite its mathematical simplicity, it is able to differentiate molecules with different structures [18]. Therefore, the aim of our work is to investigate the QSPR model for the K_{OA} of PBDEs based on topological index. Molecular distance-edge vector (MDEV) index [19–21] was used as the structural descriptor of PBDEs. Multivariate linear regression (MLR) and artificial neural network (ANN) were employed to build the calibration model between the MDEV index and the K_{OA} of PBDEs.
Results and discussion
MDEV index of the investigated PBDEs
No. | PBDE congeners | μ _{1} | μ _{2} |
---|---|---|---|
1 | 2 -monobro | 0 | 1.1111 |
2* | 3 -monobro | 0 | 1.0625 |
3 | 2,4 -dibro | 0.0625 | 2.1511 |
4 | 2,4′ -dibro | 0.0204 | 2.1511 |
5 | 2,6 -dibro | 0.0625 | 2.2222 |
6* | 3,4 -dibro | 0.1111 | 2.1025 |
7 | 3,4′ -dibro | 0.0156 | 2.1025 |
8 | 4,4′ -dibro | 0.0123 | 2.0800 |
9 | 2,3,4 -tribro | 0.2847 | 3.2136 |
10* | 2,4,6 -tribro | 0.1875 | 3.2622 |
11 | 2,4′,6 -tribro | 0.1033 | 3.2622 |
12 | 3,3′,4 -tribro | 0.1471 | 3.1650 |
13 | 3,4,4′ -tribro | 0.1391 | 3.1425 |
14* | 2,2′,4,4′ -tetrabro | 0.2182 | 4.3022 |
15 | 2,3′,4,4′ -tetrabro | 0.2498 | 4.2536 |
16 | 2,3′,4,6 -tetrabro | 0.2587 | 4.3247 |
17 | 2,4,4′,6 -tetrabro | 0.2407 | 4.3022 |
18* | 3,3′,4,4′ -tetrabro | 0.2862 | 4.2050 |
19 | 2,2′,3,3′,4 -pentabro | 0.5478 | 5.3872 |
20 | 2,2′,4,4′,5 -pentabro | 0.4127 | 5.3647 |
21 | 2,3′,4,4′,6 -pentabro | 0.4230 | 5.3647 |
22* | 2,2′,4,4′,5,5′ -hexabro | 0.6276 | 6.4272 |
Experimental and predicted lg K _{ OA } of the investigated PBDEs
No. | Experimental lgK_{OA} | Predicted lgK_{OA} | Relative error (%) | ||
---|---|---|---|---|---|
MLR | ANN | MLR | ANN | ||
1 | 7.24 | 7.56 | 7.45 | 4.42 | 3.59 |
2* | 7.36 | 7.38 | 7.40 | 0.27 | 0.54 |
3 | 8.37 | 8.43 | 8.43 | 0.72 | 0.36 |
4 | 8.47 | 8.46 | 8.45 | −0.12 | −0.12 |
5 | 8.12 | 8.54 | 8.50 | 5.17 | 5.05 |
6* | 8.55 | 8.40 | 8.35 | −1.75 | −2.34 |
7 | 8.57 | 8.39 | 8.41 | −2.10 | −1.63 |
8 | 8.64 | 8.35 | 8.39 | −3.36 | −3.01 |
9 | 9.49 | 9.22 | 9.33 | −2.85 | −2.42 |
10* | 9.02 | 9.53 | 9.44 | 5.65 | 4.66 |
11 | 9.28 | 9.54 | 9.49 | 2.80 | 2.26 |
12 | 9.61 | 9.34 | 9.37 | −2.81 | −2.81 |
13 | 9.68 | 9.32 | 9.35 | −3.72 | −3.82 |
14* | 10.34 | 10.41 | 10.44 | 0.68 | 0.97 |
15 | 10.49 | 10.34 | 10.37 | −1.43 | −1.05 |
16 | 10.23 | 10.45 | 10.43 | 2.15 | 1.96 |
17 | 10.13 | 10.47 | 10.42 | 3.36 | 2.96 |
18* | 10.7 | 10.27 | 10.30 | −4.02 | −3.74 |
19 | 11.14 | 11.38 | 11.29 | 2.15 | 2.15 |
20 | 11.28 | 11.35 | 11.36 | 0.62 | 0.27 |
21 | 11.52 | 11.28 | 11.35 | −2.08 | −1.39 |
22* | 12.15 | 12.23 | 12.26 | 0.66 | 0.91 |
MLR model
Generally, a simple model should always be chosen in preference to a complex model, if the latter does not fit the data better. Thus, we firstly investigate whether MLR can model the quantitative relationship between the MDEV index and the lgK_{OA} of these PBDEs. The MDEV index was used as independent variable and the lgK_{OA} was used as dependent variable to develop the model.
The R, Standard error of the estimate and F value of the regression model is 0.9844, 0.2340 and 202.46, respectively. Then, the lgK_{OA} of the six PBDEs in Group II was predicted by Equation 1. The prediction result is shown in Table 2 also. As shown in the table, the predicted lgK_{OA} are still in good agreement with the experimental lgK_{OA}. The prediction RMSRE of the 6 PBDEs in Group II (marked by asterisk in Table 2) is 2.95. The plot of the predicted lgK_{OA} versus experimental lgK_{OA} is presented in Figure 1. As shown in Figure 1, there is a linear relationship (lgK_{OA,pred} = 0.9721 lgK_{OA,exp} + 0.2867 with R = 0.9836) between the predicted and experimental lgK_{OA}.
The results of leave one out cross validation and external validation demonstrates that the MDEV index is quantitatively related to the K_{OA} of PBDEs. The established MLR model can describe the quantitative relationship between the MDEV index and K_{OA} of PBDEs. Compared with the QSPR models reported in the references [12–15], the obtained MLR model shows comparative prediction accuracy. MDEV index can be generated easier than quantum chemical descriptors. Thus, the developed MLR model is a reliable and easy-to-use QSPR model for predicting the K_{OA} of PBDEs.
L-ANN model
L-ANN is an efficient and commonly used multivariate calibration method. Thus, we investigated whether a better model can be developed by using L-ANN appraoch. A 2-1 RBF-ANN (i.e. there are 2 nodes in the input layer and 1 node in the output layer) was used to model the quantitative relationship between the MDEV index and the lgK_{OA}. The MDEV index was used as the input variable and the lgK_{OA} was used as the output variable.
Subsequently, the external validation was carried out by using all the 22 PBDEs. An L-ANN model was developed from the 16 PBDEs in Group II. In the training procedure, the verification set comprises three randomly selected samples and the rest 13 samples were used as the training set. The lgK_{OA} of the six PBDEs in Group I was then predicted with the obtained L-ANN model. The prediction result is presented in Table 2 also. The prediction RMSRE of the 6 PBDEs in Group II (marked by asterisk in Table 2) is 2.68. The plot of the predicted lgK_{OA} versus the experimental lgK_{OA} is shown in Figure 2. There is a linear relationship (lgK_{OA, pred} = 0.9854 lgK_{OA, exp} + 0.1535 with R =0.9864) between the predicted and experimental lgK_{OA}. Obviously, the predicted lgK_{OA} is in good agreement with the experimental lgK_{OA}. It is demonstrated that the quantitative relationship between the MDEV index and lgK_{OA} of PBDEs has been modeled well by L-ANN. Compared with the QSPR models reported in the references [12–15], the obtained L-ANN model shows comparative accuracy in predicting the lgK_{OA} of PBDEs. Obviously, it is a reliable and easy-to-use QSPR model for predicting the lgK_{OA} of PBDEs. In addition, the prediction result of the L-ANN model is slightly better than the result of the MLR model. Therefore, the established L-ANN model should be a more promising model for studying the octanol/air partition behavior of PBDEs.
Experimental
Data set
The MDEV index was calculated according to the approach presented in section “Methods: MDEV index”. The calculated MDEV index is listed in Table 1. The experimental lgK_{OA} of the 22 PBDEs listed in Table 2 is taken from references [12].
where RE_{ i } is the relative error of the ith sample, and n is the number of samples.
Software
All the calculations were done with the subroutines developed under Matlab (Ver. 7.0). The computation was performed on a personal computer equipped with an i5-2450M processor. The used activation function of L-ANN is a linear function shown in Equation 5.
Conclusion
Two QSPR models for the octanol/air partition of PBDEs were developed by using MLR and L-ANN respectively. The results of leave one out cross validation and external validation indicate that the obtained MLR model and L-ANN model are practicable for predicting the K_{OA} of PBDEs. It is demonstrated that the MDEV index is quantitatively related to the K_{OA} of PBDEs. MDEV index can be generated easier than quantum chemical descriptors. Thus, using MDEV index as structural descriptor is more convenient than using quantum chemical descriptor when developing the QSPR model for the K_{OA} of PBDEs. In addition, the result demonstrates MLR and L-ANN are both practicable for modeling the quantitative relationship between the MDEV index and K_{OA} of PBDEs. Compared with the established MLR model, the obtained L-ANN model shows slightly higher prediction accuracy. The obtained L-ANN model should be a more promising model for studying the octanol/air partition behavior of PBDEs.
Methods
MDEV index
(k, l =1,2 and l ≥ k)
Obviously, the M_{22} of each PBDE is equal to 1. Thus, μ_{1} and μ_{2} were used to describe the structure of PBDEs.
Artificial neural network
The theory of ANN has been elaborated in a lot of articles [21–28]. Hence, only a brief outline of ANN is presented here.
Because there are no non-linear functions and hidden neurons in the network, L-ANN is ideal for dealing with linear problems. Actually, training a linear network means finding the optimal setting for the weight matrix W to minimize the root mean squared error (RMSE) of calibration set. In order to achieve this aim, the known samples which are used as calibraion set are generally divided into two parts: a training set and a verification set. The training set was used to calculate and adjust the network weights. The verification set was used to track the network's error performance, to identify the best network, and to stop training. The training should be stopped once deterioration in the verification error is observed. The optimal network parameters were selected according to the RMSE of verification set. The over-fitting and over-learning can be effectively avoided in this way. Although the verification set is used to identify the best network, actually, training algorithms do not use the verification set to adjust network weights. Standard pseudo-inverse linear optimization algorithm [22] is usually used to train the network. This algorithm uses the singular value decomposition technique to calculate the pseudo-inverse of the matrix needed to set the weights in a linear output layer, so as to find the least mean squared solution. Essentially, it guarantees to reach the optimal setting for the weights in the linear layer.
The main difference between MLR and L-ANN is the optimization algorithm. In MLR, the aim of least square algorithm is to minimize the sum of squared residuals of the training set. As for L-ANN, the aim of training algorithm is to minimize the RMSE of verification set [22].
Leave one out cross validation
Leave one out cross validation [29] is a commonly used algorithm for estimating predictive performance of a multivariable calibration model. Usually, practical calibration experiments have to be based on a limited set of available samples. The idea behind the leave one out cross validation algorithm is to predict the property value of each sample in turn with the calibration model which is developed with the other samples. When applying the algorithm to a dataset with N samples, the calibration modeling is performed N times, each time using (N-1) samples for modeling and one sample for testing. Thus, the procedure of leave one out cross validation can be divided into N segment. In each segment i (i = 1, . . . , N), there are three steps: (1) taking sample i out as temporary ‘test set’, which is not used to develop the calibration model, (2) developing the calibration model with the remaining (N-1) samples, (3) testing the developed model with sample i, calculating and storing the prediction error of the sample.
External validation
External validation [26, 30] is a algorithm which has been generally applied to estimating predictive performance of calibration models. When utilizing the algorithm, working dataset is split into two subsets: a calibration set, which is used to establish the calibration model, and a test set, which is employed to assess the predictive ability of the established calibration model. Herein, test set is designed to give an independent assessment of the predictive performance of the assed model. It is not used in establishing the calibration mdoel at all, and hence is independent of the calibration set. Generally, the samples in calibration set and test set are randomly selected from the working dataset.
Declarations
Acknowledgments
The work was supported by the National Natural Science Foundation of China No. 21305108, the Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2014JM2039), and the Innovative Research Team of Xi’an Shiyou University.
Authors’ Affiliations
References
- Anderson HA, Imma P, Knobeloch L, Turyk M, Mathew J, Buelow C, Persky V: Polybrominated diphenyl ethers (PBDE) in serum: Findings from a US cohort of consumers of sport-caught fish. Chemosphere. 2008, 73: 187-194. 10.1016/j.chemosphere.2008.05.052.View ArticleGoogle Scholar
- EPA: America’s children and the environment. 2014, http://www.epa.gov/ace/pdfs/Biomonitoring-PBDEs.pdf,Google Scholar
- Secretariat of the Stockholm Convention: Listing of POPs in the Stockholm Convention. 2014, http://chm.pops.int/TheConvention/ThePOPs/ListingofPOPs/tabid/2509/Default.aspx,Google Scholar
- Yue CY, Li LY: Filling the gap: estimating physicochemical properties of the full array of polybrominated diphenyl ethers (PBDEs). Environ Pollut. 2013, 180: 312-323.View ArticleGoogle Scholar
- Wang YW, Zhao CY, Ma WP, Liu HX, Wang T, Jiang GB: Quantitative structure-activity relationship for prediction of the toxicity of polybrominated diphenyl ether (PBDE) congeners. Chemosphere. 2006, 64: 515-524. 10.1016/j.chemosphere.2005.11.061.View ArticleGoogle Scholar
- Watkins DJ, McClean MD, Fraser AJ, Weinberg J, Stapleton HM, Webster TF: Associations between PBDEs in office air, dust, and surface wipes. Environ Int. 2013, 59: 124-132.View ArticleGoogle Scholar
- Harner T, Shoeib M: Measurements of octanol-air partition coefficients (KOA) for polybrominated diphenyl ethers (PBDEs): predicting partitioning in the environment. J Chem Eng Data. 2002, 47: 228-232. 10.1021/je010192t.View ArticleGoogle Scholar
- Mizukawa K, Takada H, Takeuchi I, Ikemoto T, Omori K, Tsuchiya K: Bioconcentration and biomagnification of polybrominated diphenyl ethers (PBDEs) through lower-trophic-level coastal marine food web. Mar Pollut Bull. 2009, 58: 1217-1224. 10.1016/j.marpolbul.2009.03.008.View ArticleGoogle Scholar
- Wania F, Lei YD, Harner T: Estimating octanol-air partition coefficients of nonpolar semivolatile organic compounds from gas chromatographic retention times. Anal Chem. 2002, 74: 3476-3483. 10.1021/ac0256033.View ArticleGoogle Scholar
- Han SY, Liang C, Qiao JQ, Lian HZ, Ge X, Chen HY: A novel evaluation method for extrapolated retention factor in determination of n-octanol/water partition coefficient of halogenated organic pollutants by reversed-phase high performance liquid chromatography. Anal Chim Acta. 2012, 713: 130-135.View ArticleGoogle Scholar
- Cetin B, Odabasi M: Atmospheric concentrations and phase partitioning of polybrominated diphenyl ethers (PBDEs) in Izmir, Turkey. Chemosphere. 2008, 71: 1067-1078. 10.1016/j.chemosphere.2007.10.052.View ArticleGoogle Scholar
- Xu HY, Zou JW, Yu QS, Wang YH, Zhang JY, Jin HX: QSPR/QSAR models for prediction of the physicochemical properties and biological activity of polybrominated diphenyl ethers. Chemosphere. 2007, 66: 1998-2010. 10.1016/j.chemosphere.2006.07.072.View ArticleGoogle Scholar
- Wang ZY, Zeng XL, Zhai ZC: Prediction of supercooled liquid vapor pressures and n-octanol/air partition coefficients for polybrominated diphenyl ethers by means of molecular descriptors from DFT method. Sci Total Environ. 2008, 389: 296-305. 10.1016/j.scitotenv.2007.08.023.View ArticleGoogle Scholar
- Chen JW, Harner T, Yang P, Quan X, Chen S, Schramm KW, Kettrup A: Quantitative predictive models for octanol–air partition coefficients of polybrominated diphenyl ethers at different temperatures. Chemosphere. 2003, 51: 577-584. 10.1016/S0045-6535(03)00006-7.View ArticleGoogle Scholar
- Papa E, Kovarich S, Gramatica P: Development, validation and inspection of the applicability domain of QSPR models for physicochemical properties of polybrominated diphenyl ethers. QSAR Comb Sci. 2009, 28: 790-796. 10.1002/qsar.200860183.View ArticleGoogle Scholar
- Nandi S, Monesi A, Drgan V, Merzel F, Novič M: Quantitative structure-activation barrier relationship modeling for Diels-Alder ligations utilizing quantum chemical structural descriptors. Chem Cent J. 2013, 7: 171-10.1186/1752-153X-7-171.View ArticleGoogle Scholar
- Steffen A, Apostolakis J: On the ease of predicting the thermodynamic properties of beta-cyclodextrin inclusion complexes. Chem Cent J. 2007, 1: 29-10.1186/1752-153X-1-29.View ArticleGoogle Scholar
- Gutman I, Tosovic J: Testing the quality of molecular structure descriptors. Vertex–degree based topological indices. J Serb Chem Soc. 2013, 78: 805-810. 10.2298/JSC121002134G.View ArticleGoogle Scholar
- Liu HH, Xiao X, Qin J, Liu YM: Study on structural characteristics and QSPR of polychlorinated biphenyls Isomers (PCBs). J Chongqing Inst Tech (In Chinese). 2005, 19: 67-70.Google Scholar
- Liu SS, Liu HL, Xia ZN, Cao CZ, Li ZL: Molecular distance-edge vector (μ): an extension from alkanes to alcohols. J Chem Inf Comput Sci. 1999, 39: 951-957. 10.1021/ci990011f.View ArticleGoogle Scholar
- Yin CS, Guo WM, Lin T, Liu SS, Fu RQ, Pan ZX, Wang LS: Application of wavelet neural network to the prediction of gas chromatographic retention indices of alkanes. J Chin Chem Soc. 2001, 48: 739-749.View ArticleGoogle Scholar
- Statsoft: Model extremely complex functions neural networks. 2013, http://www.statsoft.com/textbook/neural-networks,Google Scholar
- Yin CS, Shen Y, Liu SS, Yin QS, Guo WM, Pan ZX: Simultaneous quantitative UV spectrophotometric determination of multicomponents of amino acids using linear neural network. Comput Chem. 2001, 25: 239-243. 10.1016/S0097-8485(00)00097-8.View ArticleGoogle Scholar
- Zhang WJ, Zhong XQ, Liu GH: Recognizing spatial distribution patterns of grassland insects: neural network approaches. Stoch Environ Res Risk Assess. 2008, 22: 207-216. 10.1007/s00477-007-0108-3.View ArticleGoogle Scholar
- Zhang YX, Li H, Hou AX, Havel J: Artificial neural networks based on principal component analysis input selection for quantification in overlapped capillary electrophoresis peaks. Chemometr Intell Lab Syst. 2006, 82: 165-175. 10.1016/j.chemolab.2005.08.012.View ArticleGoogle Scholar
- Jalali-Heravi M, Garkani-Nejad Z: Prediction of electrophoretic mobilities of alkyl- and alkenylpyridines in capillary electrophoresis using artificial neural networks. J Chromatogr A. 2002, 971: 207-215. 10.1016/S0021-9673(02)01043-9.View ArticleGoogle Scholar
- Fatemi MH, Baher E: Quantitative structure-property relationship modelling of the degradability rate constant of alkenes by OH radicals in atmosphere. SAR QSAR Environ Res. 2009, 20: 77-90. 10.1080/10629360902726700.View ArticleGoogle Scholar
- Abdollahi Y, Zakaria A, Abbasiyannejad M, Masoumi HRF, Moghaddam MG, Matori KA, Jahangirian H, Keshavarzi A: Artificial neural network modeling of p-cresol photodegradation. Chem Cent J. 2013, 7: 96-10.1186/1752-153X-7-96.View ArticleGoogle Scholar
- Martens HA, Dardenne P: Validation and verification of regression in small data sets. Chemometr Intell Lab Syst. 1998, 44: 99-121. 10.1016/S0169-7439(98)00167-1.View ArticleGoogle Scholar
- Yin CS, Shen Y, Liu SS, Yin QS, Guo WM, Pan ZX: Simultaneous quantitative UV spectrophotometric determination of multicomponents of amino acids using linear neural network. Compu Chem. 2001, 25: 239-243. 10.1016/S0097-8485(00)00097-8.View ArticleGoogle Scholar
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