Retention Prediction Models Research Articles

Retention Prediction Modeling of Ginsenosides on a Polyvinyl Alcohol-bonded Stationary Phase at Subambient Temperatures Using Multiple Linear Regression and Artificial Neural Network

The development of retention prediction models for the seven ginsenosides (Rf, Rg1, Rd, Re, Rc, Rb2 and Rb1) on a polyvinyl alcohol (PVA)-bonded stationary phase at subambient temperatures is presented. The models were derived using multiple linear regression (MLR) and artificial neural network (ANN) using the logarithm of the retention factor (log k) as the dependent variable. Using stepwise MLR, the retention of the analytes under all temperature conditions was satisfactorily described by a three-predictor model; the predictors being the percentage of acetonitrile (%MeCN) in the mobile phase, the number of hydrogen bond donors (HBD) and the ovality (Ov) of the compounds. These predictors account for the contribution of the solute-related variables (HBD and Ov) and the influence of the mobile phase composition (%MeCN) on the retention behavior of the ginsenosides. The MLR models produced adequate fits, as proven by the high calibration R2 values of the predicted versus the observed log k (> 0.95) and good predictive properties, as indicated by the high cross-validated q2 (> 0.93) and high R2 (> 0.95) values obtained from the test set. ANN modeling was also conducted using the predictors that were derived from MLR as inputs and log k as the output. A comparison of the models derived from both MLR and ANN revealed that the trained ANNs showed better predictive abilities than the MLR models in all temperature conditions as demonstrated by their higher R2 values for both training and test sets and lower average percentage deviation of the predicted log k from the observed log k of the test compounds. The ANN models also showed excellent performance when applied to the prediction of the seven ginsenosides in different sample matrices.

Modeling of the influence of the modifier concentration on the retention process in NP-HPLC

Modeling of the influence of the modifier concentration on the retention process in NP-HPLC The adsorption model for an accurate prediction of the analyte retention in the normal - phase liquid chromatography with a binary mobile phase has been proposed. This model was derived using a thermody-namically consistent modified competitive Langmuir isotherm. The performance of the proposed equation was compared with two retention models reported in literature. All models were verified for different NP-LC systems by use of three criteria: the sum of squared differences between the experimental and theoretical data, approximation of the standard deviation and the Fisher test.

Retention behavior of ginsenosides on a poly(vinyl alcohol)-bonded stationary phase in hydrophilic interaction chromatography

The influences of the organic component of the mobile phase and the column temperature on the retention of ginsenosides on a poly(vinyl alcohol) (PVA) bonded stationary phase operated under hydrophilic interaction chromatographic mode were investigated. The retention of the ginsenosides was found to increase with increasing amount of acetonitrile (MeCN) in the mobile phase, which is typical of hydrophilic interaction chromatographic behavior. It was also found that the retention of the analytes was highly affected by the type of the organic modifier used. Aqueous MeCN (75-90%) gave the most satisfactory retention and separation of ginsenosides Rf, Rg1, Rd, Re, Rc, Rb2 and Rb1 compared with aqueous methanol, isopropyl alcohol or tetrahydrofuran at the same composition levels. The effects of the different types of organic modifiers on the retention of the analytes were attributed to their solvent strength and hydrogen-bond accepting/donating properties. The effect of temperature on the retention of ginsenoside on the PVA-bonded phase was assessed by constructing van't Hoff plots for two temperature ranges: subambient (273-293 K) and ambient-elevated (298-333 K) temperatures. van't Hoff plots for all analytes were linear at the two temperature intervals; however, the slopes of the lines corresponding to ginsenosides Rg1 and Re were completely different from those for the rest of the analytes especially in the subambient temperature range. Enthalpy-entropy compensation (EEC) studies were conducted to verify the difference in thermodynamics observed for ginsenosides Rg1 and Re compared with the other analytes. EEC plots showed that Rf, Rd, Rc, Rb2 and Rb1 were possibly retained by the same retention mechanism, which was completely different from that of Rg1 and Re at subambient temperatures. Retention prediction models were derived using multiple linear regression to identify solute attributes that affected the retention of the analytes on the PVA-bonded phase. The mathematical models derived revealed that the number of hydrogen-bond donors and the ovality of the molecules are important molecular properties that govern the retention of the compounds on the chromatographic system.

Peptide retention prediction applied to proteomic data analysis

A retention prediction model was developed for peptides separated in reversed-phase chromatography. The model was utilized to identify and exclude the false positive (FP) peptide identifications obtained via database search. The selected database included human proteins, as well as decoy sequences of random proteins. The FP peptide detection rate was defined either as number of retention time outliers, or random decoy sequence identifications. The FP rate for various MASCOT scores was calculated. The peptides identified in one-dimensional (1D) and two-dimensional (2D) liquid chromatography/mass spectrometry (LC/MS) experiments were validated by prediction models. Multi-dimensional LC was based on two orthogonal reversed-phase chromatography modes; prediction models were successfully applied for data filtering in both separation dimensions.

Retention prediction of adrenoreceptor agonists and antagonists on a diol column in hydrophilic interaction chromatography

Retention prediction models using multiple linear regression (MLR) and artificial neural networks (ANN) were developed for adrenoreceptor agonists and antagonists chromatographed on a diol column under hydrophilic interaction chromatographic (HILIC) mode at three pH conditions (3.0, 4.0 and 5.0). Using stepwise MLR, the retention behavior of the analytes was satisfactorily described by a five-predictor model; the predictors being the percentage of acetonitrile in the mobile phase (% ACN), the logarithm of partition coefficient (logD), the number of hydrogen bond donor (HBD), the number of hydrogen bond acceptor (HBA), and the total absolute atomic charge of the molecule (TAAC). Among the five descriptors, % ACN had the strongest effect on the retention as indicated by its relatively higher standardized coefficient compared to the other four predictors. The inclusion of the four predictors which are related to the properties of the compounds (logD, HBD, HBA and TAAC), suggested hydrophilic interaction, hydrogen bonding and ionic interaction as possible mechanisms of retention of the analytes on the studied system. The models derived from MLR also showed adequate fit as proven by the high correlation (R2 as high as 0.9667) between observed and predicted logk values for the training set and good predictive power on the test set (R2 greater than 0.97). ANN analyses of the data were also conducted using the five predictors derived from MLR as inputs and logk as output. The trained ANNs showed better predictive abilities as compared to MLR models as indicated by relative higher R2 and lower root mean square error of predictions (RMSEP) for both training and test sets. The derived models can be used as references for method development and optimization of chromatographic conditions for the separation of adrenoreceptor agonists and antagonists.

Retention prediction of adrenoreceptor agonists and antagonists on unmodified silica phase in hydrophilic interaction chromatography

The development of retention prediction models for adrenoreceptor agonists and antagonists chromatographed on an unmodified silica stationary phase under the hydrophilic interaction chromatographic (HILIC) mode at three pH conditions (3.0, 4.0 and 5.0) is described. The models were derived using multiple linear regression (MLR) and an artificial neural network (ANN) using the logarithm of the retention factor (log k) as the dependent variable. In addition to the effects of the solute-related variables (molecular descriptors), the percentage of acetonitrile (%ACN) was also used as a predictor to gauge the influence of the mobile phase on the retention behavior of the analytes. Using stepwise MLR, the retention behavior of the studied compounds at pH 3.0 were satisfactorily described by a four-predictor model; the predictors being the %ACN, the logarithm of the partition coefficient (log D), the number of hydrogen bond acceptors (HBA), and the magnitude of the dipole moment (DipolMag). In addition to these four predictors, the total absolute atomic charge (TAAC) was found to be a significant predictor of retention at pH 4.0 and 5.0. Among the five descriptors, %ACN had the strongest effect on the retention, as indicated by its higher standardized coefficient than those obtained for the other four predictors. The inclusion of these four predictors which are related to the molecular properties of the compounds (log D, HBA, DipolMag, and TAAC) suggested that hydrophilic interactions, hydrogen bonding and ionic interactions are possible mechanisms by which analytes are retained on the studied system. The reliability and predictive ability of the derived MLR equations were tested using cross-validation and a test set which was not used when fitting the model. The models derived from MLR produced adequate fits, as proven by the high R2 values obtained for all calibration and training sets (0.9497 and above), and their good predictive power, as indicated by the high cross-validated q2 (0.9465 and above) and high R2 (0.9305 and above) values obtained for the test sets. ANN prediction models were also derived using the predictors derived from MLR as inputs and log k as output. A comparison of the models derived from both ANN and MLR revealed that the trained ANNs showed better predictive abilities than the MLR models, as indicated by their higher R2 values and their lower root mean square error of predictions (RMSEP) for both training and test sets under all pH conditions. The derived models can be used as references and they provide a useful tool for method development and the optimization of chromatographic conditions for the separation of adrenoreceptor agonists and antagonists.

Retention Process in Normal‐Phase TLC Systems

The influence of mobile phase composition on the retention of selected test analytes in different normal‐phase TLC systems has been studied. A novel adsorption model for an accurate prediction of the analyte retention in the thin‐layer chromatography with binary mobile phase has been proposed. The performance of the model was compared with the retention models reported in the literature. All the models were verified for different TLC systems by three criteria: the sum of squared differences between the experimental and theoretical data, the approximation of the standard deviation, and the Fisher test.

Influence of mobile phase composition on retention factors in different HPLC systems with chemically bonded stationary phases.

The influence of mobile phase composition on the retention of selected test analytes in different normal- and reversed-phase chromatographic systems is studied. A novel adsorption model for an accurate prediction of the analyte retention in the column chromatography with binary mobile phase is proposed. Performance of the model is compared with the retention model reported in the literature. Both models are verified for different HPLC systems by use of three criteria: (a). the sum of squared differences between the experimental and theoretical data, (b). approximation of the standard deviation, and (c). the Fisher test.

Description and prediction of retention in normal-phase high-performance liquid chromatography with binary and ternary mobile phases

The suitabilities of several earlier reported models for description and prediction of retention in normal-phase systems with mobile phases comprised of two organic solvents — a polar and a non-polar one — were tested on the chromatographic behaviour of phenylurea herbicides and alkyl-, aryl- and nitrophenols as sample solutes with a silica-gel column and 2-propanol, n-heptane and dioxane as mobile phase components. With few exceptions, the data obtained from the best-fit three-parameter retention equation differe from the experimental capacity factors at less than 0.1, or 2%, and for most compounds the fit is better than 1%. Of the two two-parameter equations derived from theoretical models, one failed to describe the retention behaviour. The fit of the other two-parameter equation to the retention data of phenols is slightly inferior than with the three-parameter equation. The differences between the fitted data and the experimental capacity factors of substituted phenylureas were ⩽ 0.2, or 5%. Three-parameter equations were suggested to describe the retention behaviour in three-component mobile phases either at a constants sum or at a consant ratio of the two stronger solvents in ternary mobile phases. To fit the three-component data at any combination of concentrations of the three solvents with the error comparable to that in binary mobile phases, a nine-parameter equation is necessary. Suitability of these equations to describe the experimental behaviour of substituted phenylureas and phenols was verified in ternary mobile phases with various concentrations of 2-propanol, dioxane and n-heptane. Finally, a method was suggested for the prediction of retention in ternary mobile phases with varying concentration ratios of the two polar solvents from the parameters of best-fit equations in binary mobile phases and a single experimental capacity factor in a ternary mobile phase. For most capacity factors, the error of prediction was lower than 0.2 or 5%. Dried solvents were used to improve the reproducibility of the results and the temperature was controlled to ±0.1°C in all experiments. With these precautions, differences between the original retention volumes and the data from repeated experiments measured after ten months of use of the column in the system tested were less than 0.2 ml for 85% of the values compared.

Reversed-Phase LC Retention Prediction in Water-Methanol-THF Using Factor-Analytical Modeling

A factor-analytical retention prediction model is used to analyze the reversed-phase high-performance liquid chromatographic retention behavior of 23 organic compounds in solvent systems of water-methanol-tetrahydrofuran ranging from 0 to 70% water. Principal component factor analysis and target transformation factor analysis are the methods used. The retention data are reproduced using three factors ; there is a reproduction error of 2.2% in capacity factors from 0.08 to 95.1. This model is further tested by extrapolating the retention prediction to new compounds and solvents.

Correction. Models for Prediction of Retention in Nonsuppressed Ion Chromatography

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTCorrection. Models for Prediction of Retention in Nonsuppressed Ion ChromatographyDennis R. Jenke and Gordon K. PagenkopfCite this: Anal. Chem. 1984, 56, 7, 1200Publication Date (Print):June 1, 1984Publication History Published online6 September 2005Published inissue 1 June 1984https://doi.org/10.1021/ac00271a603RIGHTS & PERMISSIONSArticle Views18Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (76 KB) Get e-Alerts Get e-Alerts

Models for prediction of retention in nonsuppressed ion chromatography

The retention behavior of Br/sup -/, NO/sub 3//sup -/, Cl/sup -/, SO/sub 4//sup 2 -/, and S/sub 2/O/sub 3//sup 2 -/ in nonsuppressed ion chromatography is studied as a function of changing eluent composition. Three models, multiple species eluent, single species eluent, and single interaction sites, are utilized to predict chromatographic behavior. While all three are based on a thermodynamic equilibrium consideration of the ion exchange process, they differ in their characterization of the analyte/eluent competition or the ion/resin interaction. Despite this difference in approach, all three models effectively characterize the behavior of the analytes under elution conditions which are of practical importance. The relative utility of each model is discussed. 17 references, 4 tables.