QSRR prediction of gas chromatography retention indices of essential oil components

Prediction of retardation factor of protein amino acids in reversed phase TLC and ethanol-sodium azide solution as the mobile phase using QSRR

“…The ideal objective of quantitative structure-retention relationships (QSRR) models, as a subfield in quantitative structure-retention relationships (QSPR), is its application to predict the retention behavior of newly identified molecules and similar synthesized derivatives or to estimate the involved mechanisms during various interactions such as solute-solute, solute-stationary phase and solutemobile phase 3,4 . The prediction of retention/retardation of compounds in chromatographic systems using QSRR can reduce trial-and-error experiments which is important in some series of species such as polychlorinated biphenyls (PCBs), polybrominated, diphenyl ethers (PBDEs), polychlorinated dibenzofurans (PCDFs), etc.…”

Prediction of the GC-MS retention time for terpenoids detected in sage (Salvia officinalis L.) essential oil using QSRR approach

“…6 Elution or retention of each compound is a complex phenomenon determined by several intermolecular forces such are dipole-dipole forces, dipole-induced forces, hydrogen bonds, etc. 7 The retention profile of compounds can be determined by measuring various parameters, e.g., retention time (RT), retention distance, linear-temperature retention index and Kováts retention index. [6][7][8] Identification of new compounds with GC technique was significantly improved with introduction of mass spectra (MS) detectors.…”

“…7 The retention profile of compounds can be determined by measuring various parameters, e.g., retention time (RT), retention distance, linear-temperature retention index and Kováts retention index. [6][7][8] Identification of new compounds with GC technique was significantly improved with introduction of mass spectra (MS) detectors. The possibility to separate and identify molecules according to their retention profiles and molecular mass of fragments, makes GC-MS systems more selective and sensitive, and distinguishes them from other GC configurations (e.g., GC coupled with flame ionization detector).…”

“…Also, new compounds are not always included in MS libraries, thus their identification could be rather difficult and may lead to false positive results. 7 Another issue could be related to the availability and purity of analytical standards which are used for molecules identification. This rise an idea to use models as additional tool for identification of new compounds and predictive RT.…”

Quantitative structure-retention relationship model for predicting retention indices of constituents of essential oils of Thymus vulgaris (Lamiaceae)

“…In the particular case of essential oils, despite the vast literature on the identification and characterization of novel constituent compounds, and apart from the study presented by Marrero-Ponce et al 20 involving a database of 791 essential oil components with corresponding gas chromatographic retention properties, which seems to be an exception, the majority of QSRR models have generally been built on data sets of sizes ranging from 25 to 169 compounds, with most of these belonging to a single chemical series.…”