Predicting chromatographic retention time of C₁₀-chlorinated paraffins in gas chromatography-mass spectrometry using quantitative structure retention relationship

Abstract

Chlorinated paraffins(CPs) are potential persistent organic pollutants(POPs), which threat the safety of environment and organisms. However, the analysis of CPs is a difficult task due to their complex composition containing thousands of congeners. In the present work, quantitative structure retention relationship(QSRR) of CPs was studied. A total of 470 molecular descriptors were generated, for describing the structures of 28 CPs and 12 descriptors relevant to retention time of the CPs were selected by stepwise regression. Then, QSRR models between retention time on the one hand and the selected descriptors on the other hand were established by multiple linear regression(MLR), partial least squares(PLS) and least square support vector regression(LS-SVR). The result shows that PLS model is better than MLR and LS-SVR, obtaining a squared correlation coefficient(r ²) of 0.9996 and a root mean squared error(RMSE) of 0.015. The PLS model was then used to predict the retention time of 49 C₁₀-CPs. Three of them were investigated by gas chromatography coupled with mass spectrometry(GC-MS). A well-defined correlation was found between the measured retention time and the predicted value.

Keywords

Chlorinated paraffin / Multivariate calibration / Retention time / Quantitative structure retention relationship

References

[1] Muir D C G. The Handbook of Environmental Chemistry, 2010, Berlin: Springer-Verlag 244.

[2] Tomy G T, Fisk A T, Westmore J B, Muir D C G. Rev. Environ. Contam. Toxicol., 1998, 158: 53.

[3] Lahaniatis M R, Coelhan M, Parlar H. Organohalog. Compd., 2000, 47: 276.

[4] Wang X T, Zhang Y, Miao Y, Ma L L, Li Y C, Chang Y Y, Wu M H. Environ. Sci. Pollut. Res., 2013, 20: 4742.

[5] Sverko E, Tomy G T, Marvin C H, Muir D C G. Environ. Sci. Technol., 2012, 46: 4697.

[6] Reth M, Kypke K, Schachtele J, Oehme M. Organohalogen. Compd., 2005, 67: 1671.

[7] Thomas G O, Farrar D, Braekevelt E, Stern G, Kalantzi O I, Martin F L, Jones K C. Environ. Inter., 2006, 32: 34.

[8] Reth M, Oehme M. Anal. Bioanal. Chem., 2004, 378: 1741.

[9] Pellizzato F, Ricci M, Held A, Emons H, Bohmer W, Geiss S, Iozza S, Mais S, Petersen M, Lepom P. Trends Anal. Chem., 2009, 28: 1029.

[10] Chen L G, Huang Y M, Han S, Feng Y B, Jiang G, Tang C M, Ye Z X, Zhan W, Liu M, Zhang S K. J. Chromatogr. A, 2013, 1274: 36.

[11] Zencak Z, Reth M, Oehme M. Anal. Chem., 2004, 76: 1957.

[12] Coelhan M. Anal. Chem., 1999, 71: 4498.

[13] Castells P, Santos F J, Galceran M T. Rapid Commun. Mass Spectrom., 2004, 18: 529.

[14] Zencak Z, Borgen A, Reth M. J. Chromatogr. A, 2005, 1067: 295.

[15] Coquery M, Morin A, Becue A, Lepot B. Trends Anal. Chem., 2005, 24: 117.

[16] Kaliszan R. Quantitative Structure-Chromatographic Retention Relationships, 1987, New York: Wiley 566.

[17] Kaliszan R. Chem. Rev., 2007, 107: 3212.

[18] D’Archivio A A, Giannitto A, Maggi M A. J. Chromatogr. A, 2013, 1298: 118.

[19] Rayne S, Ikonomou M G. J. Chromatogr. A, 2003, 1016: 235.

[20] Miller T H, Musenga A, Cowan D A, Barron L P. Anal. Chem., 2013, 85: 10330.

[21] Creek D J, Jankevics A, Breitling R, Watson D G, Barrett M P, Burgess K E V. Anal. Chem., 2011, 83: 8703.

[22] Heberger K. J. Chromatogr. A, 2007, 1158: 273.

[23] Coelhan M. Fresen. Environ. Bull., 2003, 5: 442.

[24] Kennard R W, Stone L A. Technometrics, 1969, 11: 137.

[25] Chemdraw, Cambriagesoft Corporation, USA, 1985–2003

[26] Hyperchem, Hypercube, Inc., 2002, http://www.hyper.com/

[27] Todeschini R, Consonni V, Mauri A, Pavan M, Srl T. Milan, 2005, 1.

[28] Darlington R B. Regression and Linear Models, 1990, New York: McGrawHill 138.