Analytical and Bioanalytical Chemistry

, Volume 409, Issue 11, pp 2777–2789 | Cite as

Gibbs energy additivity approaches to QSRR in generating gas chromatographic retention time for identification of fatty acid methyl ester

  • Siriluck Pojjanapornpun
  • Kornkanok Aryusuk
  • Supathra Lilitchan
  • Kanit Krisnangkura
Research Paper

Abstract

The Gibbs energy additivity method was used to correlate the retention time (tR) of common fatty acid methyl esters (FAMEs) to their chemical structures. The tR of 20 standard FAMEs eluted from three capillary columns of different polarities (ZB-WAXplus, BPX70, and SLB-IL111) under both isothermal gas chromatography and temperature-programmed gas chromatography (TPGC) conditions were accurately predicted. Also, the predicted tR of FAMEs prepared from flowering pak choi seed oil obtained by multistep TPGC with the BPX70 column were within 1.0% of the experimental tR. The predicted tR or mathematical tR (tR(math)) values could possibly be used as references in identification of common FAMEs. Hence, FAMEs prepared from horse mussel and fish oil capsules were chromatographed on the BPX70 and ZB-WAXplus columns in single-step and multistep TPGC. Identification was done by comparison of tR with the tR of standard FAMEs and with tR(math). Both showed correct identifications. The proposed model has six numeric constants. Five of six could be directly transferred to other columns of the same stationary phase. The first numeric constant (a), which contained the column phase ratio, could also be transferred with the adjustment of the column phase ratio to the actual phase ratio of the transferred column. Additionally, the numeric constants could be transferred across laboratories, with similar correction of the first numeric constant. The TPGC tR predicted with the transferred column constants were in good agreement with the reported experimental tR of FAMEs. Moreover, hexane was used in place of the conventional tM marker in the calculation. Hence, the experimental methods were much simplified and practically feasible. The proposed method for using tR(math) as the references would provide an alternative to the uses of real FAMEs as the references. It is simple and rapid and with good accuracy compared with the use of experimental tR as references.

Keywords

Fatty acid methyl ester Gibbs energy Identification Quantitative structure–retention relationship Retention time 

References

  1. 1.
    Schomburg G, Dielmann G. Identification by means of retention parameters. J Chromatogr Sci. 1973;11(3):151–9.CrossRefGoogle Scholar
  2. 2.
    Christie WW. Equivalent chain-lengths of methyl ester derivatives of fatty acids on gas chromatography a reappraisal. J Chromatogr A. 1988;447:305–14.CrossRefGoogle Scholar
  3. 3.
    Dose EV. Simulation of gas chromatographic retention and peak width using thermodynamic retention indexes. 1987;59(19):2414-9.Google Scholar
  4. 4.
    Snijders H, Janssen H-G, Cramers C. Optimization of temperature-programmed gas chromatographic separations I. Prediction of retention times and peak widths from retention indices. J Chromatogr A. 1995;718(2):339–55.CrossRefGoogle Scholar
  5. 5.
    Claumann CA, Wüst Zibetti A, Bolzan A, Machado RAF, Pinto LT. Fast and accurate numerical method for predicting gas chromatography retention time. J Chromatogr A. 2015;1406:258–65.CrossRefGoogle Scholar
  6. 6.
    Boswell PG, Carr PW, Cohen JD, Hegeman AD. Easy and accurate calculation of programmed temperature gas chromatographic retention times by back-calculation of temperature and hold-up time profiles. J Chromatogr A. 2012;1263:179–88.CrossRefGoogle Scholar
  7. 7.
    Wu L, Chen Y, Caccamise SAL, Li QX. Difference equation model for isothermal gas chromatography expresses retention behavior of homologues of n-alkanes excluding the influence of holdup time. J Chromatogr A. 2012;1260:215–23.CrossRefGoogle Scholar
  8. 8.
    Karolat B, Harynuk J. Prediction of gas chromatographic retention time via an additive thermodynamic model. J Chromatogr A. 2010;1217(29):4862–7.CrossRefGoogle Scholar
  9. 9.
    Aldaeus F, Thewalim Y, Colmsjö A. Prediction of retention times and peak widths in temperature-programmed gas chromatography using the finite element method. J Chromatogr A. 2009;1216(1):134–9.CrossRefGoogle Scholar
  10. 10.
    Farkas O, Zenkevich IG, Stout F, Kalivas JH, Héberger K. Prediction of retention indices for identification of fatty acid methyl esters. J Chromatogr A. 2008;1198–1199:188–95.CrossRefGoogle Scholar
  11. 11.
    Cavalli EJ, Guinchard C. Forecasting retention times in temperature-programmed gas chromatography. J Chromatogr Sci. 1995;33(7):370–6.CrossRefGoogle Scholar
  12. 12.
    Vezzani S, Moretti P, Castello G. Automatic prediction of retention times in multi-linear programmed temperature analyses. J Chromatogr A. 1997;767(1–2):115–25.CrossRefGoogle Scholar
  13. 13.
    Chen JP, Liang XM, Zhang Q, Zhang LF. Prediction of GC retention values under various column temperature conditions from temperature programmed data. Chromatographia. 2001;53(9):539–47.CrossRefGoogle Scholar
  14. 14.
    Ebrahimi-Najafabadi H, Mginitie TM, Harynuk JJ. Quantitative structure–retention relationship modeling of gas chromatographic retention times based on thermodynamic data. J Chromatogr A. 2014;1358:225–31.CrossRefGoogle Scholar
  15. 15.
    Martin AJP, editor. Some theoretical aspects of partition chromatography. Biochem Soc Symp; 1950.Google Scholar
  16. 16.
    Gerbino TC, Castello G. Prediction of programmed temperature retention indices on capillary columns of different polarities. J Chromatogr A. 1995;699(1–2):161–71.CrossRefGoogle Scholar
  17. 17.
    Kavanagh PE, Balder D, Franklin G. Estimation of retention times of homologous series in temperature programmed gas chromatography. Chromatographia. 1999;49(9):509–12.CrossRefGoogle Scholar
  18. 18.
    Said AS. Theory and mathematics of chromatography. Huthig; 1981.Google Scholar
  19. 19.
    Castells RC, Arancibia EL, Miguel NA. Regression against temperature of gas chromatographic retention data. J Chromatogr A. 1990;504:45–53.CrossRefGoogle Scholar
  20. 20.
    Zenkevich IG, Makarov AA, Schrader S, Moeder M. A new version of an additive scheme for the prediction of gas chromatographic retention indices of the 211 structural isomers of 4-nonylphenol. J Chromatogr A. 2009;1216(18):4097–106.CrossRefGoogle Scholar
  21. 21.
    Peng CT, Hua RL, Maltby D. Prediction of retention indexes: IV. Chain branching in alkylbenzene isomers with C10–13 alkyl chains identified in a scintillator solvent. J Chromatogr A. 1992;589(1–2):231–9.CrossRefGoogle Scholar
  22. 22.
    Peng CT. Prediction of retention indices. VI: isothermal and temperature-programmed retention indices, methylene value, functionality constant, electronic and steric effects. J Chromatogr A. 2010;1217(23):3683–94.CrossRefGoogle Scholar
  23. 23.
    Katritzky AR, Karelson M, Lobanov VS. QSPR as a means of predicting and understanding chemical and physical properties in terms of structure. Pure Appl Chem. 1997;69(2):245–8.CrossRefGoogle Scholar
  24. 24.
    Krisnangkura K, Tancharoon A, Konkao C, Jeyashoke N. An alternative method for the calculation of equivalent chain length or carbon number of fatty acid methyl esters in gas chromatography. J Chromatogr Sci. 1997;35(7):329–32.CrossRefGoogle Scholar
  25. 25.
    Nilratnisakorn S, Jeyashoke N, Krisnangkura K. Effect of column length on forecasted retention times and carbon numbers in an isothermal and temperature-programmed gas chromatography. ScienceAsia. 1999;173.Google Scholar
  26. 26.
    Kittiratanapiboon K, Jeyashoke N, Krisnangkura K. Forecasting retention times of fatty acid methyl esters in temperature-programmed gas chromatography. J Chromatogr Sci. 1998;36(11):541–6.CrossRefGoogle Scholar
  27. 27.
    Lomsugarit SD, Katsuwon J, Jeyashoke N, Krisnangkura K. An empirical approach for estimating the equivalent chain length of fatty acid methyl esters in multistep temperature-programmed gas chromatography. J Chromatogr Sci. 2001;39(11):468–72.CrossRefGoogle Scholar
  28. 28.
    Aryusuk K, Yensruang D, Krisnangkura K. An alternative approach for the estimation of equivalent temperature in gas chromatography. J Chromatogr Sci. 2004;42(7):371–7.CrossRefGoogle Scholar
  29. 29.
    Sansa-ard C, Aryusuk K, Lilitchan S, Krisnangkura K. Free energy contribution to gas chromatographic separation of petroselinate and oleate esters. Chromatogr Res Int. 2010. doi:10.4061/2011/252543.Google Scholar
  30. 30.
    Cavalli EJ, Guinchard C. Forecasting retention times in temperature-programmed gas chromatography: experimental verification of the hypothesis on compound behavior. J Chromatogr Sci. 1996;34(12):547–9.CrossRefGoogle Scholar
  31. 31.
    Kalayasiri P, Jeyashoke N, Krisnangkura K. Survey of seed oils for use as diesel fuels. J Am Oil Chem Soc. 1996;73(4):471–4.CrossRefGoogle Scholar
  32. 32.
    Mjøs SA. Identification of fatty acids in gas chromatography by application of different temperature and pressure programs on a single capillary column. J Chromatogr A. 2003;1015(1–2):151–61.CrossRefGoogle Scholar
  33. 33.
    Quintanilla-López JE, Lebrón-Aguilar R, García-Domínguez J. The hold-up time in gas chromatography II. Validation of the estimation based on the concept of a zero carbon atoms alkane. J Chromatogr A. 1997;767(1–2):127–36.CrossRefGoogle Scholar
  34. 34.
    Watanachaiyong T, Jeyashoke N, Krisnangkura K. A convenient method for routine estimation of dead time in gas chromatography. J Chromatogr Sci. 2000;38:67–71.CrossRefGoogle Scholar
  35. 35.
    Ragonese C, Sciarrone D, Tranchida PQ, Dugo P, Mondello L. Use of ionic liquids as stationary phases in hyphenated gas chromatography techniques. J Chromatogr A. 2012;1255:130–44.CrossRefGoogle Scholar
  36. 36.
    Santiuste JM, Quintanilla-López JE, Takács JM, Lebrón-Aguilar R. Behaviour of the isothermal retention indices of n-alkylbenzenes on stationary phases of different polarity. J Chromatogr A. 2012;1222:90–7.CrossRefGoogle Scholar
  37. 37.
    Aryusuk K, Krisnangkura K. Prediction of gas chromatographic retention times of capillary columns of different inside diameters. J Sep Sci. 2003;26(18):1688–92.CrossRefGoogle Scholar
  38. 38.
    Torres AG, Trugo NMF, Trugo LC. Mathematical method for the prediction of retention times of fatty acid methyl esters in temperature-programmed capillary gas chromatography. J Agric Food Chem. 2002;50(15):4156–63.CrossRefGoogle Scholar
  39. 39.
    van Den Dool H, Dec Kratz P. A generalization of the retention index system including linear temperature programmed gas–liquid partition chromatography. J Chromatogr A. 1963;11:463–71.CrossRefGoogle Scholar
  40. 40.
    Claumann CA, Wüst Zibetti A, Bolzan A, Machado RAF, Pinto LT. Robust estimation of thermodynamic parameters (ΔH, ΔS and ΔCp) for prediction of retention time in gas chromatography – part I (theoretical). J Chromatogr A. 2015;1425:249–57.CrossRefGoogle Scholar
  41. 41.
    Li A, Gao J, Freels S, Huang J, Yu G. Predicting gas chromatography relative retention times for polychlorinated biphenyls using chlorine substitution pattern contribution method. J Chromatogr A. 2016;1427:161–9.CrossRefGoogle Scholar
  42. 42.
    Mjøs SA. Two-dimensional fatty acid retention indices. J Chromatogr A. 2004;1061(2):201–9.CrossRefGoogle Scholar
  43. 43.
    Harynuk J, Wynne PM, Marriott PJ. Evaluation of new stationary phases for the separation of fatty acid methyl esters. Chromatographia. 2006;63(13):S61–6.CrossRefGoogle Scholar
  44. 44.
    Zeng AX, Chin S-T, Nolvachai Y, Kulsing C, Sidisky LM, Marriott PJ. Characterisation of capillary ionic liquid columns for gas chromatography–mass spectrometry analysis of fatty acid methyl esters. Anal Chim Acta. 2013;803:166–73.CrossRefGoogle Scholar
  45. 45.
    Dettmer K. Assessment of ionic liquid stationary phases for the GC analysis of fatty acid methyl esters. Anal Bioanal Chem. 2014;406(20):4931–9.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Siriluck Pojjanapornpun
    • 1
  • Kornkanok Aryusuk
    • 1
  • Supathra Lilitchan
    • 2
  • Kanit Krisnangkura
    • 1
  1. 1.Division of Biochemical Technology, School of Bioresources and TechnologyKing Mongkut’s University of Technology Thonburi (Bangkhuntien)BangkokThailand
  2. 2.Department of Nutrition, Faculty of Public HealthMahidol UniversityBangkokThailand

Personalised recommendations