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Support Vector Machine Applied to Study on Quantitative Structure–Retention Relationships of Polybrominated Diphenyl Ether Congeners

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Abstract

Quantitative structure–retention relationships (QSRR) models were constructed for the GC relative retention times (RRTs) of 126 polybrominated diphenyl ether (PBDE) congeners. First, a number of topological and connectivity indices descriptors were derived from E-dragon software. In a further step, six molecular descriptors were extracted by genetic algorithm (GA) coupled with multiple linear regression (MLR) method. The QSRR model was established using a support vector machine (SVM) algorithm as regression tool. High training sets correlation coefficients R 2 indicated that >99.6 % (except for stationary phase CP-Sil 19) of the total variation in the predicted RRTs is explained by the fitted models. It showed that we provided a more accurate model that was subsequently used to predict the RRTs of validation sets. The excellent statistical parameters Q 2 loo (correlation coefficient of leave-one-out cross validation) and validation sets correlation coefficients R 2 > 99.0 % reveal that the models are robust and have high internal and external predictive capability. According to sum of ranking differences (SRD) validation values, we concluded that DB-1 and DB-5 are the best two models.

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References

  1. Renner R (2000) Environ Sci Technol 34:452–453

    Article  Google Scholar 

  2. Hale RC, La Guardia MJ, Harvey E, Gaylor MO, Mainor TM (2006) Chemosphere 64:181–186

    Article  CAS  Google Scholar 

  3. Hazrati S, Harrad S (2006) Environ Sci Technol 40:7584–7589

    Article  CAS  Google Scholar 

  4. Lundstedt-Enkel K, Karlsson D, Darnerud PO (2010) J Chemometrics 24:710–718

    Article  CAS  Google Scholar 

  5. Cunha S, Kalachova K, Pulkrabova J, Fernandes J, Oliveira M, Alves A, Hajslova J (2010) Chemosphere 78:1263–1271

    Article  CAS  Google Scholar 

  6. Meerts IA, Van Zanden JJ, Luijks EA, van Leeuwen-Bol I, Marsh G, Jakobsson E, Bergman Å, Brouwer A (2000) Toxicol Sci 56:95–104

    Article  CAS  Google Scholar 

  7. Vonderheide AP, Mueller KE, Meija J, Welsh GL (2008) Sci Total Environ 400:425–436

    Article  CAS  Google Scholar 

  8. Wei H, Yang R, Li A, Christensen ER, Rockne KJ (2010) J Chromatogr A 1217:2964–2972

    Article  CAS  Google Scholar 

  9. Vonderheide AP (2009) Microchem J 92:49–57

    Article  CAS  Google Scholar 

  10. Wang Y, Li A, Liu H, Zhang Q, Ma W, Song W, Jiang G (2006) J Chromatogr A 1103:314–328

    Article  CAS  Google Scholar 

  11. Zhang X, Ding L, Sun Z, Song L, Sun T (2009) Chromatographia 70:511–518

    Article  CAS  Google Scholar 

  12. Zhang XT, Ren C, Guo JJ, Song LJ, Sun T (2012) Adv Mater Res 347:1769–1773

    Article  Google Scholar 

  13. Fatemi MH, Ghorbanzad’e M, Baher E (2010) Anal Lett 43:823–835

    Article  CAS  Google Scholar 

  14. Fatemi MH, Malekzadeh H (2012) J Sep Sci 35:2088–2094

    Article  CAS  Google Scholar 

  15. Rayne S, Ikonomou MG (2003) J Chromatogr A 1016:235–248

    Article  CAS  Google Scholar 

  16. Korytár P, Covaci A, de Boer J, Gelbin A, Brinkman UAT (2005) J Chromatogr A 1065:239–249

    Article  Google Scholar 

  17. Yi Z, Li L, Zhang A, Wang L (2011) Chin J Chem 29:2495–2504

    Article  CAS  Google Scholar 

  18. D’Archivio AA, Giannitto A, Maggi MA (2013) J Chromatogr A 1298:118–131

    Article  Google Scholar 

  19. Zhang YH, Liu SS, Liu HY (2007) Chromatographia 65:319–324

    Article  CAS  Google Scholar 

  20. Goodarzi M, Jensen R, Vander Heyden Y (2012) J Chromatogr B 910:84–94

    Article  CAS  Google Scholar 

  21. Cai W, Xia B, Shao X, Guo Q, Maigret B, Pan Z (2001) J Mol Struct: THEOCHEM 535:115–119

    Article  CAS  Google Scholar 

  22. Noorizadeh H, Farmany A (2010) Chromatographia 72:563–569

    Article  CAS  Google Scholar 

  23. Whitley D (1994) Stat Comput 4:65–85

    Article  Google Scholar 

  24. Jalali Heravi M, Kyani A (2007) Eur J Med Chem 42:649–659

    Article  CAS  Google Scholar 

  25. Vapnik V (1998) Statistical learning theory. Wiley, New York

    Google Scholar 

  26. Luan F, Xue C, Zhang R, Zhao C, Liu M, Hu Z, Fan B (2005) Anal Chim Acta 537:101–110

    Article  CAS  Google Scholar 

  27. Zhang J, Wang B, Zhang X, Huang DS, Zhang X, Reyes García C, Zhang L (2010) Advanced intelligent computing theories and applications. Springer, Berlin, pp 83–90

    Google Scholar 

  28. Cong Y, Li BK, Yang XG, Xue Y, Chen YZ, Zeng Y (2013) Chemom Intell Lab Syst

  29. Vladimir VN, Vapnik V (1995) The nature of statistical learning theory. Springer, Heidelberg

    Google Scholar 

  30. Scholkopf B, Sung KK, Burges CJ, Girosi F, Niyogi P, Poggio T, Vapnik V (1997) Signal Proces, IEEE T 45:2758–2765

    Article  Google Scholar 

  31. Smola AJ, Schölkopf B (2004) Stat Comput 14:199–222

    Article  Google Scholar 

  32. Tetko IV, Gasteiger J, Todeschini R, Mauri A, Livingstone D, Ertl P, Palyulin VA, Radchenko EV, Zefirov NS, Makarenko AS (2005) J Comput Aid Mol Des 19:453–463

    Article  CAS  Google Scholar 

  33. Riahi S, Pourbasheer E, Ganjali MR, Norouzi P (2009) J Hazard Mater 166:853–859

    Article  CAS  Google Scholar 

  34. Pourbasheer E, Riahi S, Ganjali MR, Norouzi P (2009) Eur J Med Chem 44:5023–5028

    Article  CAS  Google Scholar 

  35. Pourbasheer E, Riahi S, Ganjali MR, Norouzi P (2010) Eur J Med Chem 45:1087–1093

    Article  CAS  Google Scholar 

  36. Randić M (2001) J Mol Graph Model 20:19–35

    Article  Google Scholar 

  37. Moskovkina M, Bangov I, Patleeva AZ (2013) Bulg Chem Commun 45:9–23

    CAS  Google Scholar 

  38. Bangov I, Moskovkina M, Patleeva A (2010) Bulg Chem Commun 42:338–342

    CAS  Google Scholar 

  39. Dobrynin AA, Entringer R, Gutman I (2001) Acta Appl Math 66:211–249

    Article  Google Scholar 

  40. Wang Y, Yao X, Zhang X, Zhang R, Liu M, Hu Z, Fan B (2002) Talanta 57:641–652

    Article  CAS  Google Scholar 

  41. Frame GM (1997) Fresen J Anal Chem 357:714–722

    Article  CAS  Google Scholar 

  42. Héberger K, Kollár-Hunek K (2011) J Chemometrics 25:151–158

    Article  Google Scholar 

Download references

Acknowledgments

The corresponding author is grateful for the financial support from the National Natural Science Foundation of China (21376114), and Department of Education of Liaoning Province, which made this work possible.

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Authors and Affiliations

  1. College of Sciences, Northeastern University, Shenyang, Liaoning, China

    Xiaotong Zhang & Ting Sun

  2. Liaoning Key Laboratory of Petrochemical Engineering, Liaoning Shihua University, Fushun, Liaoning, China

    Xiaotong Zhang, Xin Zhang, Qiang Li, Zhaolin Sun & Lijuan Song

  3. College of Chemical Engineering, Beijing University of Chemical Technology, Beijing, China

    Qiang Li

Authors
  1. Xiaotong Zhang
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  2. Xin Zhang
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  3. Qiang Li
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  4. Zhaolin Sun
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  5. Lijuan Song
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  6. Ting Sun
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Corresponding authors

Correspondence to Lijuan Song or Ting Sun.

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Zhang, X., Zhang, X., Li, Q. et al. Support Vector Machine Applied to Study on Quantitative Structure–Retention Relationships of Polybrominated Diphenyl Ether Congeners. Chromatographia 77, 1387–1398 (2014). https://doi.org/10.1007/s10337-014-2735-4

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  • DOI: https://doi.org/10.1007/s10337-014-2735-4

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