Skip to main content
SpringerLink
Log in

Prediction of Infinite Dilution Activity Coefficients of Halogenated Hydrocarbons in Water Using Classification and Regression Tree Analysis and Adaptive Neuro-Fuzzy Inference Systems

  • Published:
Journal of Solution Chemistry Aims and scope Submit manuscript

Abstract

A quantitative structure-infinite dilution activity relationship was developed to predict the infinite dilution activity coefficients of halogenated hydrocarbons, γ ∞, in water at 298.15 K. A set of 1,497 zero-to three-dimentional descriptors were used for each molecule in the data set. Classification and regression tree (CART) were successfully used as a descriptor selection method. Three descriptors were selected and used as inputs for the adaptive neuro-fuzzy inference system (ANFIS). The root mean square errors for both calibration and prediction sets are 0.48. The results were compared with those obtained from other models. The results showed that CART-ANFIS can be used as a powerful model for prediction of the infinite dilution activity coefficients of halogenated hydrocarbons.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Eckert, C.A., Sherman, S.R.: Measurement and prediction of limiting activity coefficients. Fluid Phase Equilib. 116, 333–342 (1996)

    Article  CAS  Google Scholar 

  2. Sandler, S.I.: Infinite dilution activity coefficients in chemical, environmental and biochemical engineering. Fluid Phase Equilib. 116, 343–353 (1996)

    Article  CAS  Google Scholar 

  3. Krummen, M., Gmehling, J.: Measurement of activity coefficients at infinite dilution in N-methyl-2-pyrrolidone and N-formylmorpholine and their mixtures with water using the dilutor technique. Fluid Phase Equilib. 215, 283–294 (2004)

    Article  CAS  Google Scholar 

  4. Dallinga, L., Schiller, M., Gmehling, J.: Measurement of activity coefficients at infinite dilution using differential ebulliometry and non-steady-state gas–liquid chromatography. J. Chem. Eng. Data 38, 147–155 (1993)

    Article  CAS  Google Scholar 

  5. David, W., Letcher, T.M., Ramjugernath, D., Raal, J.D.: Activity coefficients of hydrocarbon solutes at infinite dilution in the ionic liquid, 1-methyl-3-octyl-imidazolium chloride from gas–liquid chromatography. J. Chem. Thermodyn. 35, 1335–1341 (2003)

    Article  Google Scholar 

  6. Morton, D.W., Young, C.L.: Henry’s law constants and infinite dilution activity coefficients of C2–C8 hydrocarbons in phenylalkanes. J. Chem. Thermodyn. 28, 895–904 (1996)

    Article  CAS  Google Scholar 

  7. Gruber, D., Langenheim, D., Gmehling, J., Moollan, W.: Measurement of activity coefficients at infinite dilution using gas–liquid chromatography. 6. Results for systems exhibiting gas–liquid interface adsorption with 1-octanol. J. Chem. Eng. Data 42, 882–885 (1997)

    Article  CAS  Google Scholar 

  8. Möllmann, C., Gmehling, J.: Measurement of activity coefficients at infinite dilution using gas–liquid chromatography. 5. Results for N-methylacetamide, N,N-dimethylacetamide, N,N-dibutylformamide, and sulfolane as stationary phases. J. Chem. Eng. Data 42, 35–40 (1997)

    Article  Google Scholar 

  9. Dohnal, V., Ondo, D.: Refined non-steady-state gas–liquid chromatography for accurate determination of limiting activity coefficients of volatile organic compounds in water: application to C1–C5 alkanols. J. Chromatogr. A 1097, 157–164 (2005)

    Article  CAS  Google Scholar 

  10. Trampe, D.M., Eckert, C.A.: Limiting activity coefficients from an improved differential boiling point technique. J. Chem. Eng. Data 35, 156–162 (1990)

    Article  CAS  Google Scholar 

  11. Put, C., Perrin, Questier, F., Coomans, D., Massart, D.L., Vander Heyden, Y.: Classification and regression tree analysis for molecular descriptor selection and retention prediction in chromatographic quantitative structure–retention relationship studies. J. Chromatogr. 988, 261–276 (2003)

    Article  CAS  Google Scholar 

  12. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth, Monterey (1984)

    Google Scholar 

  13. Lavrac, N.: Selected techniques for data mining in medicine. Artif. Intell. Med. 16, 3–23 (1999)

    Article  CAS  Google Scholar 

  14. Marshall, R.J.: The use of classification and regression trees in clinical epidemiology. J. Clin. Epidemiol. 54, 603–609 (2001)

    Article  CAS  Google Scholar 

  15. De’Ath, G., Fabricius, K.E.: Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81, 3178–3192 (2000)

    Article  Google Scholar 

  16. Tittonell, P., Shepherd, K.D., Vanlauwe, B., Giller, K.E.: Unravelling the effects of soil and crop management on maize productivity in smallholder agricultural systems of western Kenya—an application of classification and regression tree analysis. Agric. Ecosyst. Environ. 123, 137–150 (2008)

    Article  Google Scholar 

  17. Questier, F., Put, R., Coomans, D., Walczak, B., Vander Heyden, Y.: The use of CART and multivariate regression trees for supervised and unsupervised feature selection. Chemom. Intell. Lab. 76, 45–54 (2005)

    Article  CAS  Google Scholar 

  18. Jalali-Heravi, M., Shahbazikhah, P.: Quantitative structure-mobility relationship study of a diverse set of organic acids using classification and regression trees and adaptive neuro-fuzzy inference systems. Electrophoresis 29, 363–374 (2008)

    Article  CAS  Google Scholar 

  19. Atabati, M., Zarei, K., Abdinasab, E.: Classification and regression tree analysis for molecular descriptor selection and binding affinities prediction of imidazobenzodiazepines in quantitative structure-activity relationship studies. Bull. Korean Chem. Soc. 30, 2717–2722 (2009)

    Article  CAS  Google Scholar 

  20. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  Google Scholar 

  21. Sugeno, M., Kang, G.T.: Structure identification of fuzzy model. Fuzzy Sets Syst. 28, 15–33 (1988)

    Article  Google Scholar 

  22. Jang, J.-S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE T. Syst. Man. Cybern. 23, 665–685 (1993)

    Article  Google Scholar 

  23. He, J., Zhong, C.: A QSPR study of infinite dilution activity coefficients of organic compounds in aqueous solutions. Fluid Phase Equilib. 205, 303–316 (2003)

    Article  CAS  Google Scholar 

  24. Li, X., Luan, F., Si, H., Hu, Z., Liu, M.: Prediction of retention times for a large set of pesticides or toxicants based on support vector machine and the heuristic method. Toxicol. Lett. 175, 136144 (2007)

    Google Scholar 

  25. Todeschini, R., Consunni, V.: Handbook of molecular descriptors. Wiley, Weinheim (2000)

    Book  Google Scholar 

  26. Estrada, E., Diaz, G.A., Delgado, E.J.: Predicting infinite dilution activity coefficients of organic compounds in water by quantum-connectivity descriptors. J. Comput. Aided Mol. Des. 20, 539–548 (2006)

    Article  CAS  Google Scholar 

  27. Xu, J., Zhang, H., Wang, L., Ye, W., Xu, W., Li, Z.: QSPR analysis of infinite dilution activity coefficients of chlorinated organic compounds in water. Fluid Phase Equilib. 291, 111–116 (2010)

    Article  CAS  Google Scholar 

  28. Delgado, E.J., Alderete, J.B.: Prediction infinite dilution activity coefficients of chlorinated organic compounds in aqueous solution from quantum-chemical descriptors. J. Comput. Chem. 22, 1851–1856 (2001)

    Article  CAS  Google Scholar 

  29. Xu, J., Wang, L., Wang, L., Zhang, H., Xu, W.: Prediction infinite dilution activity coefficients of chlorinated organic compounds in aqueous solution based on three-dimentional WHIM and GETAWAY descriptors. J. Solut. Chem. 40, 118–130 (2011)

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors acknowledge to the Research Council of Damghan University for the support of this work.

Author information

Authors and Affiliations

  1. School of Chemistry, Damghan University, Damghan, Iran

    Kobra Zarei & Morteza Atabati

Authors
  1. Kobra Zarei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Morteza Atabati
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kobra Zarei.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zarei, K., Atabati, M. Prediction of Infinite Dilution Activity Coefficients of Halogenated Hydrocarbons in Water Using Classification and Regression Tree Analysis and Adaptive Neuro-Fuzzy Inference Systems. J Solution Chem 42, 516–525 (2013). https://doi.org/10.1007/s10953-013-9972-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10953-013-9972-2

Keywords

Search

Navigation