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
Highlights
IntroductionAn ever-increasing number of chemicals is being produced and used. More than 100,000 chemicals are used daily [1] and, the need to identify compounds through a non-directed analysis (non-target screening) is great
Nowadays, an ever-increasing number of chemicals is being produced and used
Retention-time predictions have been used in several fields of study including proteomics [14, 15], metabolomics [16], or the analysis of organic pollutants using Gas chromatography (GC)-mass spectrometry (MS) or liquid chromatography (LC)-MS techniques [16, 17]
Summary
An ever-increasing number of chemicals is being produced and used. More than 100,000 chemicals are used daily [1] and, the need to identify compounds through a non-directed analysis (non-target screening) is great. Retention-time predictions have been used in several fields of study including proteomics [14, 15], metabolomics [16], or the analysis of organic pollutants using GC-MS or LC-MS techniques [16, 17] These predictions are applicable to various compounds with different molar masses, polarities, and boiling points [17]. Such predictions can be performed in various ways These include using thermodynamic properties in mobile and stationary phases in GC [17], a federation of local models approach in combination with physico-chemical properties [18], neural networks [18], and quantitative structure–retention relationships (QSRR) with partial least squares (PLS) [19] to derive an analyte’s retention time or index, respectively, from its structure
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