Skip to main content
Log in

Study on Quantitative Structure–Retention Relationships for Hydrocarbons in FCC Gasoline

Chromatographia Aims and scope Submit manuscript

Abstract

A series of hydrocarbons in FCC gasoline have been used to develop quantitative structure-retention relationships (QSRR) for their gas chromatographic retention index (RI) by molecular descriptors which were calculated by Dragon software. QSRR models were built by adopting multiple linear regression (MLR) and artificial neural network (ANN). However, the results showed more or less the same quality with the predictive correlation coefficient R of 0.9952 and 0.9953 for MLR and ANN respectively. The obtained results showed that the linear method is satisfactory to model the gas chromatographic retention index at least to the current dataset.

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

Access this article

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

References

  1. Gallegos EJ, Whittemore IM, Klaver RF (1974) Anal Chem 46:157–161. doi:10.1021/ac60337a042

    Article  CAS  Google Scholar 

  2. Jennings W, Shibamoto T (1980) Quantitative analysis of flavor and fragrance volatiles by glass capillary gas chromatography. Academic Press, San Francisco

    Google Scholar 

  3. van Asten A (2002) Trends Analyt Chem 21:698. doi:10.1016/S0165-9936(02)00807-5

    Article  Google Scholar 

  4. Kaliszan R (1987) Quantitative structure–retention relationships, Anal. Chem., 1992, 64 (11), pp 619A-631A

    Google Scholar 

  5. Kaliszan R (1997) Structure and retention in chromatography. CRC Press, Boca Raton

  6. Shahmirani S, Farahani EV, Ghasemi J (2006) Ann Chim 96:327. doi:10.1002/adic.200690034

    Article  CAS  Google Scholar 

  7. Ghasemi J, Saaidpour S, Brown SD (2007) J Mol Struct THEOCHEM 805:27. doi:10.1016/j.theochem.2006.09.026

    Article  CAS  Google Scholar 

  8. Jalali-Heravi M, Asadollahi-Baboli M, Shahbazikhah P (2008) Eur J Med Chem 43:548–556. doi:10.1016/j.ejmech.2007.04.014

    Article  CAS  Google Scholar 

  9. Ghasemi J, Asadpour S, Abdolmaleki A (2007) Anal Chim Acta 588:200–206. doi:10.1016/j.aca.2007.02.027

    Article  CAS  Google Scholar 

  10. Yang H (2005) Application of GC in petrochemical industry. Chemical Industry Press, China, pp 94–100

    Google Scholar 

  11. Zupan J, Gasteiger J (1999) Neural networks in chemistry and drug design. Wiley-VCH, Weinheim

    Google Scholar 

  12. Sharma R, Singh K, Singhal D, Ghosh R (2004) Chem Eng Process 43:841–847. doi:10.1016/S0255-2701(03)00103-X

    Article  CAS  Google Scholar 

  13. Mjalli FS (2005) Chem Eng Sci 60:239–253. doi:10.1016/j.ces.2004.07.117

    Article  CAS  Google Scholar 

  14. Uraikul V, Chan CW, Tontiwachwuthikul P (2007) Eng Appl Artif Intell 20:115–131. doi:10.1016/j.engappai.2006.07.002

    Article  Google Scholar 

  15. Srinivasan R, Wang C, Ho WK, Lim KW (2005) Chem Eng Sci 60:935–949. doi:10.1016/j.ces.2004.09.061

    Article  CAS  Google Scholar 

  16. Fatemi MH (2002) J Chromatogr A 955:273. doi:10.1016/S0021-9673(02)00169-3

    Article  CAS  Google Scholar 

  17. Zhang R, Yan A, Liu M, Hu Z (1999) Chemom Intell Lab Syst 45:113. doi:10.1016/S0169-7439(98)00095-1

    Article  CAS  Google Scholar 

  18. Loukas YL (2000) J Chromatogr A 904:119. doi:10.1016/S0021-9673(00)00923-7

    Article  CAS  Google Scholar 

  19. Dorsey JG, Dill KA (1989) Chem Rev 89:331. doi:10.1021/cr00092a005

    Article  CAS  Google Scholar 

  20. Miller JC, Miller JN (1988) Statistics for analytical chemistry, 2nd edn. Wiley, New York

    Google Scholar 

Download references

Acknowledgments

The corresponding author is grateful for the financial support from the National Natural Science Foundation of China (20476042) and Ministry of Science and Technology of the People’s Republic of China under the National Basic Research Program of China (973 Program) (2007CB216403), which made this work possible.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiaotong Zhang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, X., Ding, L., Sun, Z. et al. Study on Quantitative Structure–Retention Relationships for Hydrocarbons in FCC Gasoline. Chroma 70, 511–518 (2009). https://doi.org/10.1365/s10337-009-1174-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1365/s10337-009-1174-0

Keywords

Navigation