Roman Szucs

Comparison of the response of four aerosol detectors used with ultra high pressure liquid chromatography.

The responses of four different types of aerosol detectors have been evaluated and compared to establish their potential use as a universal detector in conjunction with ultra high pressure liquid chromatography (UHPLC). Two charged-aerosol detectors, namely Corona CAD and Corona Ultra, and also two different types of light-scattering detectors (an evaporative light scattering detector, and a nano-quantity analyte detector [NQAD]) were evaluated. The responses of these detectors were systematically investigated under changing experimental and instrumental parameters, such as the mobile phase flow-rate, analyte concentration, mobile phase composition, nebulizer temperature, evaporator temperature, evaporator gas flow-rate and instrumental signal filtering after detection. It was found that these parameters exerted non-linear effects on the responses of the aerosol detectors and must therefore be considered when designing analytical separation conditions, particularly when gradient elution is performed. Identical reversed-phase gradient separations were compared on all four aerosol detectors and further compared with UV detection at 200 nm. The aerosol detectors were able to detect all 11 analytes in a test set comprising species having a variety of physicochemical properties, whilst UV detection was applicable only to those analytes containing chromophores. The reproducibility of the detector response for 11 analytes over 10 consecutive separations was found to be approximately 5% for the charged-aerosol detectors and approximately 11% for the light-scattering detectors. The tested analytes included semi-volatile species which exhibited a more variable response on the aerosol detectors. Peak efficiencies were generally better on the aerosol detectors in comparison to UV detection and particularly so for the light-scattering detectors which exhibited efficiencies of around 110,000 plates per metre. Limits of detection were calculated using different mobile phase compositions and the NQAD detector was found to be the most sensitive (LOD of 10 ng/mL), followed by the Corona CAD (76 ng/mL), then UV detection at 200 nm (178 ng/mL) using an injection volume of 25 μL.

Analysis of phospholipids in lecithins comparison between micellar electrokinetic chromatography and high-performance liquid chromatography

The determination of phospholipids in lecithins by HPLC and MEKC has been compared. MEKC conditions have been optimized to provide a robust analytical method. MEKC offers the advantage of a much higher peak capacity which results in an improved resolution, especially for phosphatidylserine. Although the repeatability of the MEKC data is somewhat lower than for HPLC, good quantitative results are obtained.

Universal response model for a corona charged aerosol detector

The universality of the response of the Corona Charged Aerosol Detector (CoronaCAD) has been investigated under flow-injection and gradient HPLC elution conditions. A three-dimensional model was developed which relates the CoronaCAD response to analyte concentration and the mobile phase composition used. The model was developed using the response of four probe analytes which displayed non-volatile behavior in the CoronaCAD and were soluble over a broad range of mobile phase compositions. The analyte concentrations ranged from 1μg/mL to 1mg/mL, and injection volumes corresponded to on-column amounts of 25ng to 25μg. Mobile phases used in the model were composed of 0-80% acetonitrile, mixed with complementary proportions of aqueous formic acid (0.1%, pH 2.6). An analyte set of 23 compounds possessing a wide range of physicochemical properties was selected for the purpose of evaluating the model. The predicted response was compared to the actual analyte response displayed by the detector and the efficacy of the model under flow-injection and gradient HPLC elution conditions was determined. The average error of the four analytes used to develop the model was 9.2% (n=176), while the errors under flow-injection and gradient HPLC elution conditions for the evaluation set of analytes were found to be 12.5% and 12.8%, respectively. Some analytes were excluded from the evaluation set due to considerations of volatility (boiling point <400°C), charge and excessive retention on the column leading to elution outside the eluent range covered by the model. The two-part response model can be used to describe the relationship between response and analyte concentration and also to offer a correction for the non-linear detector response obtained with gradient HPLC for analytes which conform to the model, to provide insight into the factors affecting the CoronaCAD response for different analytes, and also as a means for accurately determining the concentration of unknown compounds when individual standards are not available for calibration.

Evaluation of 1.0 mm i.d. column performances on ultra high pressure liquid chromatography instrumentation.

The present study focuses on the evaluation of 1.0 mm i.d. (internal diameter) columns on a commercial Ultra-High Pressure system. These systems have been developed specifically to operate columns with small volumes, typically 2.1 mm i.d., by reducing extra-column volume dispersion. The use of columns with smaller i.d. results in a reduced solvent consumption and required sample volume. The evaluation of the columns was carried out with samples containing neutral and pharmaceutical compounds. In isocratic mode, the extra-column volume produced additional band broadening leading to poor performances compared to equivalent 2.1 mm i.d. columns. By increasing the length of the column, the influence of the extra-column bandspreading could be reduced and 75,000 plates were obtained when four columns were coupled. In gradient mode, the effect of the extra-column contribution on efficiency was limited and about 80% of the performance of the 2.1 mm i.d. columns was obtained. Optimum conditions in gradient mode were further investigated by changing flow rate, gradient time and column length. A different approach of the calculation of peak capacity was also considered for the comparison of the influence of these different parameters.

Investigation of polar organic solvents compatible with Corona Charged Aerosol Detection and their use for the determination of sugars by hydrophilic interaction liquid chromatography.

A range of organic solvents (ethanol, isopropanol and acetone) has been investigated as alternatives to acetonitrile and methanol when used in conjunction with Corona Charged Aerosol Detection (Corona CAD). These solvents have been evaluated with regard to their effect on the response of the Corona CAD. Three dimensional response surfaces were constructed using raw data showing the relationship between detector response, analyte concentration and percentage of organic solvent in the mobile phase, using sucralose or quinine as the test analyte. The detector response was non-linear in terms of analyte concentration for all solvents tested. However, detector response varied in an approximately linear manner with percentage of organic solvent over the range 0-40% for ethanol or isopropanol and 0-80% for acetone and methanol. The chromatographic performance of the various solvents when used as aqueous-organic mobile phases was evaluated for isocratic and gradient separations of sugars and sugar alcohols by hydrophilic interaction liquid chromatography (HILIC) using an Asahipak NH2P-504E column coupled with Corona CAD detection. It was found that whilst acetonitrile provided the highest column efficiencies and lowest detection limits of the solvents studied, acetone also performed well and could be used to resolve the same number of analytes as was possible with acetonitrile. Typical efficiencies and detection limits of 5330 plates m(-1) and 1.25 μg mL(-1), respectively, were achieved when acetone was used as the organic modifier. Acetone was utilised successfully as an organic modifier in the HILIC separation of carbohydrates in a beer sample and also for a partially digested dextran sample.

The history and analytical chemistry of beer bitter acids

Abstract The organoleptic characteristics of beer are mainly determined by the bitter-tasting iso-α-acids, which in the brewing process are formed from the α-acids occurring in hops. Quantification of the individual iso-α-acids is not straightforward, but recent results obtained by liquid chromatography and micellar electrokinetic chromatography are promising. Correlation of the sensory analysis of beer with the distribution of the iso-α-acids requires investigation by trained taste experts of each iso-α-acid in its pure state.

Performance comparison of partial least squares-related variable selection methods for quantitative structure retention relationships modelling of retention times in reversed-phase liquid chromatography

The relative performance of six multivariate data analysis methods derived from or combined with partial least squares (PLS) has been compared in the context of quantitative structure-retention relationships (QSRR). These methods include, GA (genetic algorithm)-PLS, Monte Carlo uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), iteratively retaining informative variables (IRIV), variable iterative space shrinkage approach (VISSA) and PLS with automated backward selection of predictors (autoPLS). A set of 825 molecular descriptors was computed for 86 suspected sports doping compounds and used for predicting their gradient retention times in reversed-phase liquid chromatography (RPLC). The correlation between molecular descriptors selected by each technique and the retention time was established using the PLS method. All models derived from a selected subset of descriptors outperformed the reference PLS model derived from all descriptors, with very small demands of computational time and effort. A performance comparison indicated great diversity of these methods in selecting the most relevant molecular descriptors, ranging from 28 for CARS to 263 for MC-UVE. While VISSA provided the lowest degree of over-fitting for the training set, CARS demonstrated the best compromise between the prediction accuracy and the number of selected descriptors, with the prediction error of as low as 46s for the external test set. Only ten descriptors were found to be common for all models, with the characteristics of these descriptors being representative of the retention mechanism in RPLC.

Towards a chromatographic similarity index to establish localized quantitative structure-retention models for retention prediction: Use of retention factor ratio

Quantitative Structure-Retention Relationships (QSRR) have the potential to speed up the screening phase of chromatographic method development as the initial exploratory experiments are replaced by prediction of analyte retention based solely on the structure of the molecule. The present study offers further proof-of-concept of localized QSRR modelling, in which the retention of any given compound is predicted using only the most chromatographically similar compounds in the available dataset. To this end, each compound in the dataset was sequentially removed from the database and individually utilized as a test analyte. In this study, we propose the retention factor k as the most relevant chromatographic similarity measure and compare it with the Tanimoto index, the most popular similarity measure based on chemical structure. Prediction error was reduced by up to 8 fold when QSRR was based only on chromatographically similar compounds rather than using the entire dataset. The study therefore shows that the design of a practically useful structural similarity index should select the same compounds in the dataset as does the k-similarity filter in order to establish accurate predictive localized QSRR models. While low average prediction errors (Mean Absolute Error (MAE)<0.5min) and slopes of the regression lines through the origin close to 1.00 were obtained using k-similarity searching, the use of the structural Tanimoto similarity index, considered as the gold standard in Quantitative Structure-Activity Relationships (QSAR) studies, generally resulted in much higher prediction errors (MAE>1min) and significant deviations from the reference slope of 1.0. The Tanomoto similarity index therefore appears to have limited general utility in QSRR studies. Future studies therefore aim at designing a more appropriate chromatographic similarity index that can then be applied for unknown compounds (that is, compounds which have not been tested previously on the chromatographic system used, but for which the chemical structures are known).

Comparison of CZE, open‐tubular CEC and non‐aqueous CE coupled to electrospray MS for impurity profiling of drugs

Open‐tubular CEC and non‐aqueous CE (NACE) methods were developed for the analysis of six pharmaceutical compounds and their respective process‐related impurities, comprising 22 analytes in total with a range of functional groups and lipophilicities. These methods were assessed for orthogonality of analyte separation with respect to existing CZE‐ESI‐MS and HPLC‐ESI‐MS methods, in order to complement a generic analytical strategy for impurity profiling of pharmaceutical compounds. Open‐tubular CEC, using etched and chemically modified capillaries, induced weak reversed‐phase‐type interactions between some of the analytes and the bonded phases (0.811

Prediction of retention in hydrophilic interaction liquid chromatography using solute molecular descriptors based on chemical structures

Quantitative structure-retention relationship (QSRR) models are developed to predict the retention times of analytes on five hydrophilic interaction liquid chromatography (HILIC) stationary phases (bare silica, amine, amide, diol and zwitterionic), with a view to selecting the most suitable stationary phase(s) for the separation of these analytes. The study was conducted using six β-adrenergic agonists as target analytes. Molecular descriptors were calculated based only on chemical structures optimized using density functional theory. A genetic algorithm (GA) was then used to select the most relevant molecular descriptors and these were used to build a retention model for each stationary phase using partial least squares (PLS) regression. This model was then used to predict the retention of the test set of target analytes. This process created an optimized descriptor set which enhanced the reliability of the developed QSRR models. Finally, the QSRR models developed in the work were utilized to provide some insight into the separation mechanisms operating in the HILIC mode. Three performance criteria - mean absolute error (MAE), root mean square error of prediction scaled to retention time (RMSEP), and the number of selected descriptors, were used to evaluate the developed models when applied to an external test set of six β-adrenergic agonists and showed highly predictive abilities. MAE values ranged from 13 to 25s on four of the stationary phases, with a somewhat higher error (50s) being observed for the zwitterionic phase. RMSEP values of 4.88-11.12% were recorded. Validation was performed through Y-randomization and chemical domain applicability, from which it was evident that the developed optimized GA-PLS models were robust. The high levels of accuracy, reliability and applicability of the models were to a large extent due to the optimization of the GA descriptor set and the presence of relevant structural and geometric molecular descriptors, together with descriptors based on important physicochemical properties, which establish a strong connection between retention time and meaningful chemical properties. The present strategy, while it is a pilot study, holds great promise for broader screening of HILIC stationary phases for desired separation, as well as for acquisition of information about molecular mechanisms of separation under chromatographic conditions.