New practical algorithm for modelling retention times in gradient reversed-phase high-performance liquid chromatography

Computer models have been widely used to predict the chromatographic behaviour of liquid chromatography systems. With the introduction of mass spectrometric detection and the use of lower mobile phase flow rates with conventional LC equipment, the influence of the dwell volume on the shape of the gradient curve becomes an issue in predicting the retention times. A new straight forward algorithm is proposed for the modelling of retention times in reversed-phase LC, taking the effect of the dwell volume on the gradient shape into account. The results show that the dwell volume has a large effect at lower flow rates and on the retention times of the analytes eluting at the end of fast gradient curves. The proposed model is able to make reliable predictions and can be helpful in LC-MS method development.     

A general strategy for performing temperature-programming in high performance liquid chromatography--prediction of segmented temperature gradients

In the present work it is shown that the linear elution strength (LES) model which was adapted from temperature-programming gas chromatography (GC) can also be employed to predict retention times for segmented-temperature gradients based on temperature-gradient input data in liquid chromatography (LC) with high accuracy. The LES model assumes that retention times for isothermal separations can be predicted based on two temperature gradients and is employed to calculate the retention factor of an analyte when changing the start temperature of the temperature gradient. In this study it was investigated whether this approach can also be employed in LC. It was shown that this approximation cannot be transferred to temperature-programmed LC where a temperature range from 60°C up to 180°C is investigated. Major relative errors up to 169.6% were observed for isothermal retention factor predictions. In order to predict retention times for temperature gradients with different start temperatures in LC, another relationship is required to describe the influence of temperature on retention. Therefore, retention times for isothermal separations based on isothermal input runs were predicted using a plot of the natural logarithm of the retention factor vs. the inverse temperature and a plot of the natural logarithm of the retention factor vs. temperature. It could be shown that a plot of lnk vs. T yields more reliable isothermal/isocratic retention time predictions than a plot of lnk vs. 1/T which is usually employed. Hence, in order to predict retention times for temperature-gradients with different start temperatures in LC, two temperature gradient and two isothermal measurements have been employed. In this case, retention times can be predicted with a maximal relative error of 5.5% (average relative error: 2.9%). In comparison, if the start temperature of the simulated temperature gradient is equal to the start temperature of the input data, only two temperature-gradient measurements are required. Under these conditions, retention times can be predicted with a maximal relative error of 4.3% (average relative error: 2.2%). As an example, the systematic method development for an isothermal as well as a temperature gradient separation of selected sulfonamides by means of the adapted LES model is demonstrated using a pure water mobile phase. Both methods are compared and it is shown that the temperature-gradient separation provides some advantages over the isothermal separation in terms of limits of detection and analysis time.     

Development of Gradient Retention Model in Ion Chromatography. Part I: Conventional QSRR Approach

New retention methodology that integrates the conventional quantitative structure-retention relationship (QSRR) approach and gradient retention modeling based on isocratic retention data is developed and presented in this paper. Such an integrated approach removes the general QSRR limitation of highly predefined application conditions (i.e., QSRR are generally applicable only under the conditions used during model development) and allows the prediction of retentions over a wide range of different elution conditions (practically for any isocratic or gradient elution profile). At the same time, it retains the ability to predict retention of components unknown to the model, i.e., the components that have not been used in modeling. Ion-exchange chromatography (IC) analysis of carbohydrates was selected as modeling environment. Three regression techniques were applied and compared during QSRR modeling, namely: stepwise multiple linear regression, partial least squares (PLS), and uninformative variable elimination–PLS regression. The obtained prediction results of the best QSRR model (root-mean-square error of prediction = 22.69 %) were similar to those found in the literature. The upgrade from QSRR to the integrated model did not diminish the predictive ability of the model, indicating an excellent potential of the developed methodology not only in IC but also in chromatography in general.     

Interconversion of gradient and isocratic retention data in reversed-phase liquid chromatography: Effect of the uptake of eluent modifier on the retention of analytes

The experimental technique of mass spectrometric tracer pulse chromatography was used to study the effect of the sorption of eluent components by a C₁₈-bonded silica RPLC packing on the retention of a series of test analytes during isocratic and gradient elution experiments. The analytes of interest were a substituted phenol, a substituted nitroaniline, an anti-malaria drug, tetrahydrofuran, and methanol. The eluent used was a mixture of acetonitrile and water. The solutes and isotopically labeled eluent components were injected at fixed time intervals during each gradient run. The mass specific detector allowed the assignment of individual analyte peaks even when there was overlap in the chromatograms from successive injections. Thus, the retention time of each analyte could be determined as a function of gradient slope and initial eluent composition at the time of each injection. Experimental gradient retention time data were then compared with the calculated results from two theoretical models. The first model assumed the velocity of the mobile phase and eluent were equal. The second and most realistic model assumed the velocity of the eluent was less than the velocity of the mobile phase due to the uptake of eluent by the stationary phase. Gradient retention times predicted by the two models were reasonably accurate with the sorption model giving slightly more accurate values. Inverse calculations, i.e., calculation of isocratic retention factors from gradient elution data were also carried out with very similar results. That is, the model allowing for the uptake of eluent was slightly more accurate than the model assuming no eluent-stationary phase interaction.     

Development and optimization of an HPLC analysis of citalopram and its four nonchiral impurities using experimental design methodology

In this study, the RP-HPLC method was investigated for the separation of citalopram and its four impurities by use of statistical experimental design. Initially, the influence of different experimental conditions (buffer pH, flow rate, and column temperature) on the chromatographic behavior of citalopram and its four impurities was investigated by use of partial least squares regression (PLSR) and multilayer perceptron (MLP) artificial neural networks (ANNs) trained by back-propagation. The developed models and the corresponding response surface plots were used to select the optimal HPLC conditions, buffer pH 7.0, flow rate 1.0 mL/ min, and column temperature 25 degrees C, for an efficient separation of citalopram and its four impurities. The elaborated HPLC method was found to be linear, specific, sensitive, precise, accurate, and robust. Retention times of citalopram and its impurities, obtained with the developed HPLC method, and the computed molecular parameters of the examined compounds were used in a quantitative structure retention relationship (QSRR) study. The PLSR and ANN algorithms were applied for the development of the QSRR methods. The MLP-two layers-ANN-QSRR model with root mean square error of prediction 0.105 and r(2) (observed versus predicted) 0.978 was selected. Since many different reaction conditions are applied for the synthesis of citalopram, different impurities and degradation products can be formed. Therefore, the developed QSRR model can be extended to the prediction of the retention times with the other citalopram impurities, degradation products, and metabolites.     

Retention Characteristics of Peptides in RP-LC: Peptide Retention Prediction

A review of recent results of the use of chromatographic retention data in peptide identification and in the development of procedures for peptide retention prediction is presented. In recent years, reversed phase LC (RP-LC) has become an important tool in the separation of peptides in MS analysis. A challenging problem in a further expansion of RP-LC applications is the use of already available retention information for the identification purposes simultaneously with MS–MS identification. This overview focuses on the retention characteristics suggested in LC. We will discuss the application of the retention index concept in LC, which is widely used in GC to characterize retention of organic compounds. The use of retention indices as retention characteristics of analytes in LC was first suggested at the end of 1970s, however the application of retention indices is still somewhat rare today. There are several reasons for this. One is the relatively high sensitivity and variability of retention indices to the change of parameters of chromatographic systems. Another is the chemical restrictions in the search of the universal set of reference compounds suitable for retention scaling. Several methods were suggested for the prediction of the retention times of peptides. A frequently used approach is based on the additivity scheme and calculation of the elution time through the summation of retention coefficients of amino acids constituting the peptide. Such an approach allows fairly accurate predictions of the retention time of peptides made up of not more then 15–20 amino acid residues. Additional correction factors were suggested to improve predictions including corrections for the peptide length, peptide hydrophobicity, sequence of amino acids, etc. Suggested procedures are discussed in detail. Application of predicted retention times in the identification of peptides is considered. Current status of LC retention data collections is presented.