Retention-time prediction in comprehensive two-dimensional gas chromatography to aid identification of unknown contaminants

Abstract

Comprehensive two-dimensional (2D) gas chromatography (GC×GC) coupled to mass spectrometry (MS, GC×GC-MS), which enhances selectivity compared to GC-MS analysis, can be used for non-directed analysis (non-target screening) of environmental samples. Additional tools that aid in identifying unknown compounds are needed to handle the large amount of data generated. These tools include retention indices for characterizing relative retention of compounds and prediction of such. In this study, two quantitative structure–retention relationship (QSRR) approaches for prediction of retention times (1tR and 2tR) and indices (linear retention indices (LRIs) and a new polyethylene glycol–based retention index (PEG-2I)) in GC × GC were explored, and their predictive power compared. In the first method, molecular descriptors combined with partial least squares (PLS) analysis were used to predict times and indices. In the second method, the commercial software package ChromGenius (ACD/Labs), based on a “federation of local models,” was employed. Overall, the PLS approach exhibited better accuracy than the ChromGenius approach. Although average errors for the LRI prediction via ChromGenius were slightly lower, PLS was superior in all other cases. The average deviations between the predicted and the experimental value were 5% and 3% for the 1tR and LRI, and 5% and 12% for the 2tR and PEG-2I, respectively. These results are comparable to or better than those reported in previous studies. Finally, the developed model was successfully applied to an independent dataset and led to the discovery of 12 wrongly assigned compounds. The results of the present work represent the first-ever prediction of the PEG-2I. Graphical abstractᅟ Electronic supplementary materialThe online version of this article (10.1007/s00216-018-1415-x) contains supplementary material, which is available to authorized users.

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“…A data set with more than 800 compounds is published. The above-mentioned works [11,28] use proprietary ACD ChromGenius software for building an accurate QSRR model, and there are only sparse explanations of how exactly retention is predicted. Some works consider relatively large data sets consisting of compounds of very similar structures, e.g., polychlorinated biphenyls [29] and polybrominated diphenyl ethers [30].…”

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“…The results showed that L-M ANN outperformed all the other linear and nonlinear models. Similarly, Veenaas et al [12] reported that the QSRR method could be used for prediction of retention time (tR), which can provide an insight into the separation mechanisms. The study reported that the prediction oftR is vital for decreasing the time needed for analyte identification and method development, especially in nontargeted analysis.…”