AI Article Synopsis

  • The study investigated how 43 nitrogen-containing compounds behave in a mixed-mode chromatography system that combines reversed-phase (RP) and hydrophilic interaction (HILIC) processes.
  • The research found that retention in HILIC is better explained by a partition model rather than an adsorption model, and identified a relationship between retention, mobile phase polarity, and volume fractions.
  • Advanced statistical methods like Multiple Linear Regression (MLR) and Support Vector Machine (SVM) were used to analyze molecular properties, leading to improved predictions of retention characteristics for compounds such as imidazoline and piperazine derivatives.

Article Abstract

Investigation of the retention behavior of a wide range of analytes, 43 nitrogen containing heterocyclic and guanidine derivatives such, as imidazoline and serotonin receptor ligands or their related compounds, was performed on mixed-mode stationary phase in the combined reversed-phase (RP) and hydrophilic interaction liquid chromatography (HILIC) modes. Suitability of the linear retention modelling in the HILIC and RP modes was tested including separate contributions from adsorption and partition. For the HILIC retention, the partition model was found to provide better description compared with the adsorption model. In a wider range of the aqueous eluent volume fractions, φ(aq), retention was described as a function of volume fractions and total polarity of mobile phase using the mixed-mode retention modelling. The obtained results revealed that the shift of the chromatographic mode can be calculated from the change of total polarity of mobile phase in a multi-modal relation, logarithm of retention factor vs. total polarity, with the minimum value representing the turning point between the HILIC and the RP mode. Molecular properties of the investigated compounds that influence the retention behavior and the turning point were selected using Multiple Linear Regression (MLR) and Support Vector Machine (SVM). Slightly better statistical results were found for the logk(aq)/MLR, logk(org)/MLR, logk(aq)/MLR, and φ (aq)/SVM (RBF) QSRR models than for the logk(aq)/SVM, logk(org)/SVM, logk(aq)/SVM, and φ(aq)/MLR modelling. With this insight, it is possible to precisely define and predict the retention characteristics based on physico-chemical properties of imidazoline and piperazine related compounds.

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Source
http://dx.doi.org/10.1016/j.chroma.2018.11.051DOI Listing

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