Contribution of Modified Harary Index to Predict Kováts Retention Indices for a Set of PAHs

Touhami, Imen; Haddag, Hamza; Didi, Mabrouka; Messadi, Djelloul

doi:10.1007/s10337-016-3120-2

Cited by 2 publications

(1 citation statement)

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“…Gas chromatography is a primary method for analyzing low-molecular-weight PAHs, but it is not optimally suited for the higher-molecular-weight analytes (≥C 24 -PAH) due to their low vapor pressures [ 16 , 17 ]. Nevertheless, most studies reported in the literature that applied QSRR methods for this class of compounds involved gas chromatography [ 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Structure–Retention Relationship Analysis of Polycyclic Aromatic Compounds in Ultra-High Performance Chromatography

et al. 2023

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A comparative quantitative structure–retention relationship (QSRR) study was carried out to predict the retention time of polycyclic aromatic hydrocarbons (PAHs) using molecular descriptors. The molecular descriptors were generated by the software Dragon and employed to build QSRR models. The effect of chromatographic parameters, such as flow rate, temperature, and gradient time, was also considered. An artificial neural network (ANN) and Partial Least Squares Regression (PLS-R) were used to investigate the correlation between the retention time, taken as the response, and the predictors. Six descriptors were selected by the genetic algorithm for the development of the ANN model: the molecular weight (MW); ring descriptor types nCIR and nR10; radial distribution functions RDF090u and RDF030m; and the 3D-MoRSE descriptor Mor07u. The most significant descriptors in the PLS-R model were MW, RDF110u, Mor20u, Mor26u, and Mor30u; edge adjacency indice SM09_AEA (dm); 3D matrix-based descriptor SpPosA_RG; and the GETAWAY descriptor H7u. The built models were used to predict the retention of three analytes not included in the calibration set. Taking into account the statistical parameter RMSE for the prediction set (0.433 and 0.077 for the PLS-R and ANN models, respectively), the study confirmed that QSRR models, associated with chromatographic parameters, are better described by nonlinear methods.

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