Surface fitting for calculating the second dimension retention index in comprehensive two-dimensional gas
Md Aminul Islam Prodhan1, Xinmin Yin1, Seongho Kim2
1Department of Chemistry, University of Louisville, Louisville, KY 40208, USA; University of Louisville Alcohol Research Center, Louisville, KY 40208, USA; University of Louisville Hepatobiology & Toxicology Program, Louisville, KY 40208, USA; Center for Regulatory and Environmental Analytical Metabolomics, University of Louisville, Louisville, KY 40208, USA.
Abstract:
Comprehensive two-dimensional gas chromatography mass spectrometry (GC × GC-MS) has been widely used for analysis of volatile compounds. However, the second dimension retention index (I) of each compound is not widely used to aid compound identification owing to the limited accuracy of I calculation. We report a surface fitting approach to the calculation of I using n-alkanes (C7-C30) as references, where the second dimension retention time (2tR) and the second dimension column temperature (2Te) formed the X-Y plane and the I was the Z-axis to form the I surface. Compared to the conventional approach for calculating I using isovolatility curves, the surface fitting approach eliminated the construction of isovolatility curves for the reference compounds and gives better reproducibility. The goodness of the proposed surface fitting achieved R2 = 0.9999 and RMSE = 6.1 retention index units (iu). Ten-fold cross validation demonstrated the surface fitting approach had a good predictability with average R2 = 0.9999 and RMSE = 6.6 iu. The developed method was also applied to calculate the second dimension retention indices of compound standards in two commercial mixtures MegaMix A and MegaMix B. The mean standard deviation of the calculated I was only 1.6 iu for compounds in MegaMix A and 3.4 iu for compounds in MegaMix B. Compared with the literature results, the small value of standard deviation in the calculated retention index using surface fitting method shows that the surface fitting method has less measurement variability than the conventional isovolatility curve approach.
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