Prediction of the retention in reversed-phase liquid chromatography using solute–mobile phase–stationary phase polarity parameters

134

Several chromatographic test procedures are in use for the characterization of commercially available packings. The results of the test procedure used in our laboratory are updated and further refined. Two well-defined physico-chemical properties of packings can be derived from this test procedure, the hydrophobicity of a packing and the silanophilic activity at pH 7. In addition, our method is unique in its ability to differentiate between classical packings and packings with incorporated polar groups. We can also distinguish CN packings or fluorinated packings. Our database now includes over 100 commercially available packings. The ability of the test to cleanly discriminate between different classes of packings has been strengthened. Limited comparisons with other published tests are now possible as well, and the results of these comparisons are discussed.

Section: Silanophilic Interaction At Phmentioning

confidence: 99%

Characterization of HPLC packings

Neue¹,

Tran²,

Iraneta³

et al. 2003

134

“…It is recognized in the literature [e. g. 28,29] that plots of the logarithm of the retention factor versus the volume fraction of organic solvent in the mobile phase are often curved. They are always visibly curved for ACN/waterbased mobile phases, while they are often rather linear for methanol -water mixtures.…”

Section: Theorymentioning

confidence: 99%

Selectivity in reversed‐phase separations: General influence of solvent type and mobile phase pH

Neue¹,

Méndez²

2007

The influence of the mobile phase on retention is studied in this paper for a group of over 70 compounds with a broad range of multiple functional groups. We varied the pH of the mobile phase (pH 3, 7, and 10) and the organic modifier (methanol, acetonitrile (ACN), and tetrahydrofuran (THF)), using 15 different stationary phases. In this paper, we describe the overall retention and selectivity changes observed with these variables. We focus on the primary effects of solvent choice and pH. For example, transfer rules for solvent composition resulting in equivalent retention depend on the packing as well as on the type of analyte. Based on the retention patterns, one can calculate selectivity difference values for different variables. The selectivity difference is a measure of the importance of the different variables involved in method development. Selectivity changes specific to the type of analyte are described. The largest selectivity differences are obtained with pH changes.

“…A first consequence is that the sets of solute polarities encoding the effect of molecular structure on retention in a given column are different for water-methanol and water-ACN mobile phases [13]. Moreover, to attempt cross-eluent or cross-column retention a preliminary scaling procedure is required to set up linear equations which transfer solute polarity data between the investigated chromatographic systems [16,17]. A potential limitation of the polarity-based approach can be related to the substantial experimental nature of polarity indexes, which are obtained from an iterative refinement procedure based on a suitable number of available retention data [16].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, to attempt cross-eluent or cross-column retention a preliminary scaling procedure is required to set up linear equations which transfer solute polarity data between the investigated chromatographic systems [16,17]. A potential limitation of the polarity-based approach can be related to the substantial experimental nature of polarity indexes, which are obtained from an iterative refinement procedure based on a suitable number of available retention data [16]. As to the solute polarity, the database nowadays available still includes a relatively small number of solutes, about 250 compounds [17], which prevents the method from application to most chemicals of analytical interest.…”

Section: Introductionmentioning

confidence: 99%

Multiple‐column RP‐HPLC retention modelling based on solvatochromic or theoretical solute descriptors

D’Archivio

Maggi

Ruggieri

2010

Recently, we have proposed an approach to multi-column RP-HPLC retention modelling under isocratic conditions based on a combination of five molecular descriptors, the volume fraction of organic modifier in the mobile phase and a column descriptor, simultaneously considered as dependent variables of artificial neural network (ANN) regression. The column descriptor, in particular, was identified with the observed average retention of the solutes used in calibration extrapolated to pure water as the mobile phase. The ANN-based model was seen to accurately describe retention on a pool of octadecylsiloxane-bonded (C(18)) columns in a wide range of mobile phase composition. Reliability of this approach is further examined here by analysing the retention data of Reta et al. (Anal. Chem. 1999, 71, 3484-3496) referring to 17 aromatic compounds collected in water-methanol mobile phases at the compositions 45, 50, 55 and 60% v/v of methanol with eight different columns based on various hydrocarbon, fluorocarbon and aromatic bonded stationary phases. Further, in this study we compare the explanatory capability of two different kinds of molecular descriptors: the well-known solvatochromic descriptors and theoretical descriptors extracted by genetic algorithm variable selection from the large set provided by the popular software Dragon.