Development of retention index based simulation for validation of compound identification in GC?GC

Abstract

Comprehensive two-dimensional gas chromatography (GC?GC) is a high-performance technique for separation, identification and quantification of volatiles and semi-volatiles in complex multi-component samples such as biomolecular molecules, essential oil, foods, and petroleum. One of the most popular detectors used for peak identification with GC?GC is mass spectrometer (MS) allowing identification of separated peaks based on comparison with mass spectral library. However, only MS library comparison shows low confidence in compound identifications due to the fact that compounds with similar structures (especially for isomers) often have similar mass spectra. Apart from sample preparation, a great challenge is to effectively select types of stationary phase and experimental condition for improved separation of each sample (i.e. column selection, temperature program, modulation period and hold up time). This research established the computational approach to simulate GC?GC results by using first and second dimensional retention index (1I and 2I) based calculation approach is established to simulate retention times (1tR and 2tR) and contour plots of samples from (GC?GC-MS). For the result without 1tR and 2tR data of alkane references (1tR(n) and 2tR(n)), the following steps were applied: (1) curve fitting based on van den Dool and Kratz relationship in order to simulate 1tR(n) using a training set of volatile compounds in a sample with their experimental 1tR data, and (2) simulation of 2tR(n) at different 1tR(n) to construct their isovolatility curves based on a nonlinear equation with six constants. These parameters were obtained by performing curve fitting according to the experimental 2tR data of the same training set. Simulation of 1tR and 2tR of target analytes (1tR,sim and 2tR,sim) with known 1I and 2I were performed using 1tR(n) and the simulated isovolatility curves. Gaussian equations were then applied to generate the peak intensity profiles, and summation of peak profiles of all the analytes was performed in order to simulate the contour plot for each sample using MATLAB. All the calculations and curve fittings were carried out by using Solver in Microsoft Excel. The approach was applied to simulate results for 622 compounds in several samples including saffron (Crocus sativas L.), Boswellia papyrifera, acacia honey, incense powder/smoke and perfume. These were compared with the experimental data showing good correlation with the R2 of 0.975-0.999 and 0.449-0.992 for 1tR and 2tR, respectively. This approach was then applied to propose 10 compounds which may be incorrectly identified from the literatures based on the great differences between 2tR,sim and the experimental 2tR.