Publications by authors named "Bob W J Pirok"

29 Publications

  • Page 1 of 1

Characterization and comparison of smokeless powders by on-line two-dimensional liquid chromatography.

J Chromatogr A 2022 Jun 16;1672:463072. Epub 2022 Apr 16.

Analytical Chemistry Group, Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Noord-Holland, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), the Netherlands; Co van Ledden Hulsebosch Center (CLHC), Amsterdam Center for Forensic Science and Medicine, Amsterdam, the Netherlands.

Smokeless powders (SPs) are one of the most commonly used propellants for ammunition but can also be abused as energetic material in improvised explosive devices (IEDs) such as pipe bombs. After a shooting or explosion, unburnt or partially burnt particulates may be observed which can be used for forensic investigation. SPs comprise mainly nitrocellulose (NC) and additives. Therefore, the characterization of both NC and the additives is of significant forensic importance. Typically, the identification, classification, and chemical profiling of smokeless powders are based exclusively on the analysis of the additives. In this study, information regarding the NC base component was combined with the chemical analysis of the additives using two-dimensional liquid chromatography (2D-LC). The system combines size-exclusion chromatography (SEC) and reversed-phase liquid chromatography (RPLC) in an on-line heart-cut 2D-LC configuration. In the first dimension, the NC is characterized by its molecular-weight distribution (MWD) while being separated from the additives. The additives are then transferred to the second-dimension separation using a novel analyte-transfer system. In the second dimension, the additives are separated to obtain a detailed profile of the low-molecular-mass compounds in the SP. With this approach, the MWD of the NC and the composition of the additives in SP have been obtained within an hour. A discrimination power of 90.53% was obtained when studying exclusively the NC MWD, and 99.47% for the additive profile. This novel combination enables detailed forensic comparison of intact SPs. Additionally, no extensive sample preparation is required, making the developed method less labor intensive.
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http://dx.doi.org/10.1016/j.chroma.2022.463072DOI Listing
June 2022

Co-Polymer sequence determination over the molar mass distribution by size-exclusion chromatography combined with pyrolysis - gas chromatography.

J Chromatogr A 2022 May 20;1670:462973. Epub 2022 Mar 20.

Analytical Chemistry Group, van 't Hoff Institute for Molecular Sciences (HIMS), Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, The Netherlands; Centre for Analytical Sciences Amsterdam, Science Park 904, Amsterdam, The Netherlands; Covestro, Group Innovation, Sluisweg 12, Waalwijk, The Netherlands.

The chain sequence of co-polymers strongly affects their physical properties. It is, therefore, of crucial importance for the development and final properties of novel materials. Currently however, few analytical methods are available to monitor the sequence of copolymers. The currently preferred method in copolymer-sequence determination, nuclear-magnetic-resonance spectroscopy (NMR), is insensitive (especially when C-NMR is required) and often offers little spectral resolution between signals indicative of different subunits. These limitations are especially challenging when one is interested in monitoring the sequence across the molar-mass distribution or in quantifying low abundant subunits. Therefore, we set out to investigate pyrolysis - gas chromatography (Py-GC) as an alternative method. Py-GC is more sensitive than NMR and offers better resolution between various subunits, but it does require calibration, since the method is not absolute. We devised a method to fuse data from NMR and Py-GC to obtain quantitative information on chain sequence and composition for a set of random and block poly(methyl methacrylate-co-styrene) copolymer samples, which are challenging to analyse as MMA tends to fully depolymerize. We demonstrated that the method can be successfully used to determine the chain sequence of both random and block copolymers. Furthermore, we managed to apply Py-GC to monitor the sequence of a random and a block copolymer across the molar-mass distribution.
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http://dx.doi.org/10.1016/j.chroma.2022.462973DOI Listing
May 2022

Automated Feature Mining for Two-Dimensional Liquid Chromatography Applied to Polymers Enabled by Mass Remainder Analysis.

Anal Chem 2022 04 28;94(14):5599-5607. Epub 2022 Mar 28.

Van 't Hoff Institute for Molecular Sciences (HIMS), Analytical Chemistry Group, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, The Netherlands.

A fast algorithm for automated feature mining of synthetic (industrial) homopolymers or perfectly alternating copolymers was developed. Comprehensive two-dimensional liquid chromatography-mass spectrometry data (LC × LC-MS) was utilized, undergoing four distinct parts within the algorithm. Initially, the data is reduced by selecting regions of interest within the data. Then, all regions of interest are clustered on the time and mass-to-charge domain to obtain isotopic distributions. Afterward, single-value clusters and background signals are removed from the data structure. In the second part of the algorithm, the isotopic distributions are employed to define the charge state of the polymeric units and the charge-state reduced masses of the units are calculated. In the third part, the mass of the repeating unit (, the monomer) is automatically selected by comparing all mass differences within the data structure. Using the mass of the repeating unit, mass remainder analysis can be performed on the data. This results in groups sharing the same end-group compositions. Lastly, combining information from the clustering step in the first part and the mass remainder analysis results in the creation of compositional series, which are mapped on the chromatogram. Series with similar chromatographic behavior are separated in the mass-remainder domain, whereas series with an overlapping mass remainder are separated in the chromatographic domain. These series were extracted within a calculation time of 3 min. The false positives were then assessed within a reasonable time. The algorithm is verified with LC × LC-MS data of an industrial hexahydrophthalic anhydride-derivatized propylene glycol-terephthalic acid copolyester. Afterward, a chemical structure proposal has been made for each compositional series found within the data.
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http://dx.doi.org/10.1021/acs.analchem.1c05336DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9008690PMC
April 2022

Critical comparison of background correction algorithms used in chromatography.

Anal Chim Acta 2022 Apr 18;1201:339605. Epub 2022 Feb 18.

Analytical Chemistry Group, van 't Hoff Institute for Molecular Sciences, Faculty of Science, University of Amsterdam, Science Park 904, 1098, XH Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), the Netherlands.

The objective of the present work was to make a quantitative and critical comparison of a number of drift and noise-removal algorithms, which were proven useful by other researchers, but which had never been compared on an equal basis. To make a rigorous and fair comparison, a data generation tool is developed in this work, which utilizes a library of experimental backgrounds, as well as peak shapes obtained from curve fitting on experimental data. Several different distribution functions are used, such as the log-normal, bi-Gaussian, exponentially convoluted Gaussian, exponentially modified Gaussian and modified Pearson VII distributions. The tool was used to create a set of hybrid (part experimental, part simulated) data, in which the background and all peak profiles and areas are known. This large data set (500 chromatograms) was analysed using seven different drift-correction and five different noise-removal algorithms (35 combinations). Root-mean square errors and absolute errors in peak area were determined and it was shown that in most cases the combination of sparsity-assisted signal smoothing and asymmetrically reweighted penalized least-squares resulted in the smallest errors for relatively low-noise signals. However, for noisier signals the combination of sparsity-assisted signal smoothing and a local minimum value approach to background correction resulted in lower absolute errors in peak area. The performance of correction algorithms was studied as a function of the density and coverage of peaks in the chromatogram, shape of the background signal, and noise levels. The developed data-generation tool is published along with this article, so as to allow similar studies with other simulated data sets and possibly other algorithms. The rigorous assessment of correction algorithms in this work may facilitate further automation of data-analysis workflows.
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http://dx.doi.org/10.1016/j.aca.2022.339605DOI Listing
April 2022

Improving retention-time prediction in supercritical-fluid chromatography by multivariate modelling.

J Chromatogr A 2022 Apr 17;1668:462909. Epub 2022 Feb 17.

Van't Hoff Institute for Molecular Sciences, Analytical Chemistry Group, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), the Netherlands.

The prediction of chromatographic retention under supercritical-fluid chromatography (SFC) conditions was studied, using established and novel theoretical models over ranges of modifier content, pressure and temperature. Whereas retention models used for liquid chromatography often only consider the modifier fraction, retention in SFC depends much more strongly on pressure and temperature. The viability of combining several retention models into surfaces that describe the effects of both modifier fraction and pressure was investigated. The ability of commonly used retention models to describe retention as a function of modifier fraction, expressed either as mass or volume fraction, pressure and density was assessed. Using the multivariate surfaces, retention-time prediction for isocratic separations at constant temperature improved significantly compared to univariate modelling when both pressure and modifier fractions were changed. The "mixed-mode" model with an additional exponential pressure or density parameter was able to predict retention times within 5%, with the majority of the predictions within 2%. The use of mass fraction and density further improves retention modelling compared to volume fraction and pressure. These variables however, do require extra computations.
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http://dx.doi.org/10.1016/j.chroma.2022.462909DOI Listing
April 2022

From Centroided to Profile Mode: Machine Learning for Prediction of Peak Width in HRMS Data.

Anal Chem 2021 12 29;93(49):16562-16570. Epub 2021 Nov 29.

Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Woolloongabba, Queensland 4102, Australia.

Centroiding is one of the major approaches used for size reduction of the data generated by high-resolution mass spectrometry. During centroiding, performed either during acquisition or as a pre-processing step, the mass profiles are represented by a single value (i.e., the centroid). While being effective in reducing the data size, centroiding also reduces the level of information density present in the mass peak profile. Moreover, each step of the centroiding process and their consequences on the final results may not be completely clear. Here, we present Cent2Prof, a package containing two algorithms that enables the conversion of the centroided data to mass peak profile data and vice versa. The centroiding algorithm uses the resolution-based mass peak width parameter as the first guess and self-adjusts to fit the data. In addition to the / values, the centroiding algorithm also generates the measured mass peak widths at half-height, which can be used during the feature detection and identification. The mass peak profile prediction algorithm employs a random-forest model for the prediction of mass peak widths, which is consequently used for mass profile reconstruction. The centroiding results were compared to the outputs of the MZmine-implemented centroiding algorithm. Our algorithm resulted in rates of false detection ≤5% while the MZmine algorithm resulted in 30% rate of false positive and 3% rate of false negative. The error in profile prediction was ≤56% independent of the mass, ionization mode, and intensity, which was 6 times more accurate than the resolution-based estimated values.
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http://dx.doi.org/10.1021/acs.analchem.1c03755DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8674881PMC
December 2021

Thermal modulation to enhance two-dimensional liquid chromatography separations of polymers.

J Chromatogr A 2021 Sep 23;1653:462429. Epub 2021 Jul 23.

Analytical-Chemistry Group, Van't Hoff Institute for Molecular Sciences, Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, the Netherland; Centre for Analytical Sciences Amsterdam (CASA), the Netherland.

Many materials used in a wide range of fields consist of polymers that feature great structural complexity. One particularly suitable technique for characterising these complex polymers, that often feature correlated distributions in e.g. microstructure, chemical composition, or molecular weight, is comprehensive two-dimensional liquid chromatography (LC × LC). For example, using a combination of reversed-phase LC and size-exclusion chromatography (RPLC × SEC). Efficient and sensitive LC × LC often requires focusing of the analytes between the two stages. For the analysis of large-molecule analytes, such as synthetic polymers, thermal modulation (or cold trapping) may be feasible. This approach is studied for the analysis of a styrene/butadiene "star" block copolymer. Trapping efficiency is evaluated qualitatively by monitoring the effluent of the trap with an evaporative light-scattering detector and quantitatively by determining the recovery of polystyrene standards from RPLC × SEC experiments. The recovery was dependant on the molecular weight and the temperatures of the first-dimension column and of the trap, and ranged from 46% for a molecular weight of 2.78 kDa to 86% (or up to 94.5% using an optimized set-up) for a molecular weight of 29.15 kDa, all at a first-dimension-column temperature of 80 °C and a trap temperature of 5 °C. Additionally a strategy to reduce the pressure pulse from the modulation has been developed, bringing it down from several tens of bars to only a few bar.
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http://dx.doi.org/10.1016/j.chroma.2021.462429DOI Listing
September 2021

Peak-tracking algorithm for use in comprehensive two-dimensional liquid chromatography - Application to monoclonal-antibody peptides.

J Chromatogr A 2021 Feb 21;1639:461922. Epub 2021 Jan 21.

van 't Hoff Institute for Molecular Sciences, Analytical Chemistry Group, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), the Netherlands.

A peak-tracking algorithm was developed for use in comprehensive two-dimensional liquid chromatography coupled to mass spectrometry. Chromatographic peaks were tracked across two different chromatograms, utilizing the available spectral information, the statistical moments of the peaks and the relative retention times in both dimensions. The algorithm consists of three branches. In the pre-processing branch, system peaks are removed based on mass spectra compared to low intensity regions and search windows are applied, relative to the retention times in each dimension, to reduce the required computational power by elimination unlikely pairs. In the comparison branch, similarity between the spectral information and statistical moments of peaks within the search windows is calculated. Lastly, in the evaluation branch extracted-ion-current chromatograms are utilized to assess the validity of the pairing results. The algorithm was applied to peptide retention data recorded under varying chromatographic conditions for use in retention modelling as part of method optimization tools. Moreover, the algorithm was applied to complex peptide mixtures obtained from enzymatic digestion of monoclonal antibodies. The algorithm yielded no false positives. However, due to limitations in the peak-detection algorithm, cross-pairing within the same peaks occurred and six trace compounds remained falsely unpaired.
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http://dx.doi.org/10.1016/j.chroma.2021.461922DOI Listing
February 2021

Measuring and using scanning-gradient data for use in method optimization for liquid chromatography.

J Chromatogr A 2021 Jan 2;1636:461780. Epub 2020 Dec 2.

University of Amsterdam, van 't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group, Science Park 904, 1098 XH Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), The Netherlands; Department of Chemistry, Gustavus Adolphus College, Saint Peter, Minnesota 56082, USA.

The use of scanning gradients can significantly reduce method-development time in reversed-phase liquid chromatography. However, there is no consensus on how they can best be used. In the present work we set out to systematically investigate various factors and to formulate guidelines. Scanning gradients are used to establish retention models for individual analytes. Different retention models were compared by computing the Akaike information criterion and the prediction accuracy. The measurement uncertainty was found to influence the optimum choice of model. The use of a third parameter to account for non-linear relationships was consistently found not to be statistically significant. The duration (slope) of the scanning gradients was not found to influence the accuracy of prediction. The prediction error may be reduced by repeating scanning experiments or - preferably - by reducing the measurement uncertainty. It is commonly assumed that the gradient-slope factor, i.e. the ratio between slopes of the fastest and the slowest scanning gradients, should be at least three. However, in the present work we found this factor less important than the proximity of the slope of the predicted gradient to that of the scanning gradients. Also, interpolation to a slope between that of the fastest and the slowest scanning gradient is preferable to extrapolation. For comprehensive two-dimensional liquid chromatography (LC × LC) our results suggest that data obtained from fast second-dimension gradients cannot be used to predict retention in much slower first-dimension gradients.
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http://dx.doi.org/10.1016/j.chroma.2020.461780DOI Listing
January 2021

Reducing the influence of geometry-induced gradient deformation in liquid chromatographic retention modelling.

J Chromatogr A 2021 Jan 13;1635:461714. Epub 2020 Nov 13.

Van 't Hoff Institute for Molecular Science (HIMS), University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands; Centre for Analytical Sciences Amsterdam (CASA), The Netherlands.

Rapid optimization of gradient liquid chromatographic (LC) separations often utilizes analyte retention modelling to predict retention times as function of eluent composition. However, due to the dwell volume and technical imperfections, the actual gradient may deviate from the set gradient in a fashion unique to the employed instrument. This makes accurate retention modelling for gradient LC challenging, in particular when very fast separations are pursued. Although gradient deformation has been addressed in method-transfer situations, it is rarely taken into account when reporting analyte retention parameters obtained from gradient LC data, hampering the comparison of data from various sources. In this study, a response-function-based algorithm was developed to determine analyte retention parameters corrected for geometry-induced deformations by specific LC instruments. Out of a number of mathematical distributions investigated as response-functions, the so-called "stable function" was found to describe the formed gradient most accurately. The four parameters describing the model resemble the statistical moments of the distribution and are related to chromatographic parameters, such as dwell volume and flow rate. The instrument-specific response function can then be used to predict the actual shape of any other gradient programmed on that instrument. To incorporate the predicted gradient in the retention modelling of the analytes, the model was extended to facilitate an unlimited number of linear gradient steps to solve the equations numerically. The significance and impact of distinct gradient deformation for fast gradients was demonstrated using three different LC instruments. As a proof of principle, the algorithm and retention parameters obtained on a specific instrument were used to predict the retention times on different instruments. The relative error in the predicted retention times went down from an average of 9.8% and 12.2% on the two other instruments when using only a dwell-volume correction to 2.1% and 6.5%, respectively, when using the proposed algorithm. The corrected retention parameters are less dependent on geometry-induced instrument effects.
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http://dx.doi.org/10.1016/j.chroma.2020.461714DOI Listing
January 2021

Recent applications of retention modelling in liquid chromatography.

J Sep Sci 2021 Jan 3;44(1):88-114. Epub 2020 Nov 3.

Analytical Chemistry Group, van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, The Netherlands.

Recent applications of retention modelling in liquid chromatography (2015-2020) are comprehensively reviewed. The fundamentals of the field, which date back much longer, are summarized. Retention modeling is used in retention-mechanism studies, for determining physical parameters, such as lipophilicity, and for various more-practical purposes, including method development and optimization, method transfer, and stationary-phase characterization and comparison. The review focusses on the effects of mobile-phase composition on retention, but other variables and novel models to describe their effects are also considered. The five most-common models are addressed in detail, i.e. the log-linear (linear-solvent-strength) model, the quadratic model, the log-log (adsorption) model, the mixed-mode model, and the Neue-Kuss model. Isocratic and gradient-elution methods are considered for determining model parameters and the evaluation and validation of fitted models is discussed. Strategies in which retention models are applied for developing and optimizing one- and two-dimensional liquid chromatographic separations are discussed. The review culminates in some overall conclusions and several concrete recommendations.
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http://dx.doi.org/10.1002/jssc.202000905DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7821232PMC
January 2021

Detection challenges in quantitative polymer analysis by liquid chromatography.

J Sep Sci 2021 Jan 4;44(1):63-87. Epub 2020 Oct 4.

Analytical Chemistry Group, van't Hoff Institute for Molecular Sciences (HIMS), Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands.

Accurate quantification of polymer distributions is one of the main challenges in polymer analysis by liquid chromatography. The response of contemporary detectors is typically influenced by compositional features such as molecular weight, chain composition, end groups, and branching. This renders the accurate quantification of complex polymers of which there are no standards available, extremely challenging. Moreover, any (programmed) change in mobile-phase composition may further limit the applicability of detection techniques. Current methods often rely on refractive index detection, which is not accurate when dealing with complex samples as the refractive-index increment is often unknown. We review current and emerging detection methods in liquid chromatography with the aim of identifying detectors, which can be applied to the quantitative analysis of complex polymers.
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http://dx.doi.org/10.1002/jssc.202000768DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7821191PMC
January 2021

Parallel gradients in comprehensive multidimensional liquid chromatography enhance utilization of the separation space and the degree of orthogonality when the separation mechanisms are correlated.

J Chromatogr A 2020 Sep 5;1628:461452. Epub 2020 Aug 5.

Department of Chemistry, University of Waterloo, Waterloo, ON, Canada. Electronic address:

Comprehensive two-dimensional liquid chromatography (LC×LC) offers increased peak capacity, resolution and selectivity compared to one-dimensional liquid chromatography. It is commonly accepted that the technique produces the best results when the separation mechanisms in the two dimensions are completely orthogonal, which necessitates the use of gradient elution for each second-dimension fraction. Recently, the use of similar separation mechanisms in both dimensions has been gaining popularity, but full or shifted gradients are still used for each second dimension fraction. Herein, we argue that when the separation mechanisms are correlated in the two dimensions, the best results can be obtained with the use of parallel gradients in the second dimension, which makes the technique nearly as user-friendly as comprehensive two-dimensional gas chromatography. This has been illustrated through the separation of a mixture of 39 pharmaceutical compounds using reversed phase in both dimensions. Different selectivity in the second dimension was obtained through the use of different stationary phase chemistries and/or mobile phase organic modifiers. The best coverage of the separation space was obtained when parallel gradients were applied in both dimensions, and the same was true for practical peak capacity.
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http://dx.doi.org/10.1016/j.chroma.2020.461452DOI Listing
September 2020

Recent applications of chemometrics in one- and two-dimensional chromatography.

J Sep Sci 2020 May 19;43(9-10):1678-1727. Epub 2020 Mar 19.

Analytical Chemistry Group, van 't Hoff Institute for Molecular Sciences, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands.

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.
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http://dx.doi.org/10.1002/jssc.202000011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7317490PMC
May 2020

Accurate modelling of the retention behaviour of peptides in gradient-elution hydrophilic interaction liquid chromatography.

J Chromatogr A 2020 Mar 23;1614:460650. Epub 2019 Oct 23.

Van 't Hoff Institute for Molecular Sciences, Science Park 904, 1098 XH Amsterdam, the Netherlands; Centre for Analytical Science Amsterdam, Science Park 904, 1098 XH Amsterdam, the Netherlands.

The applicability of models to describe peptide retention in hydrophilic interaction liquid chromatography (HILIC) was investigated. A tryptic digest of bovine-serum-albumin (BSA) was used as a test sample. Several different models were considered, including adsorption, mixed-mode, exponential, quadratic and Neue-Kuss models. Gradient separations were performed on three different HILIC stationary-phases under three different mobile-phase conditions to obtain model parameters. Methods to track peaks for specific peptides across different chromatograms are shown to be essential. The optimal mobile-phase additive for the separation of BSA digest on each of the three columns was selected by considering the retention window, peak width and peak intensity with mass-spectrometric detection. The performance of the models was investigated using the Akaike information criterion (AIC) to measure the goodness-of-fit and evaluated using prediction errors. The F-test for regression was applied to support model selection. RPLC separations of the same sample were used to test the models. The adsorption model showed the best performance for all the HILIC columns investigated and the lowest prediction errors for two of the three columns. In most cases prediction errors were within 1%.
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http://dx.doi.org/10.1016/j.chroma.2019.460650DOI Listing
March 2020

Perspectives on the future of multi-dimensional platforms.

Faraday Discuss 2019 08;218(0):72-100

University of Amsterdam, van 't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group, Science Park 904, 1098 XH Amsterdam, The Netherlands.

Two-dimensional liquid chromatography (2D-LC) formats have emerged to help address separation problems that are too complex for conventional one-dimensional LC. There are a number of obstacles to the proliferation of 2D-LC that are gradually being removed. Reliable commercial instrumentation has become available and data analysis software is being improved. Detector-sensitivity and phase-system compatibility issues can largely be solved by using active-modulation strategies. The remaining challenge, developing good and fast 2D-LC methods within a reasonable time, may be solved with smart algorithms. The technology platform that has been developed for 2D-LC also creates a number of other possibilities. Between the two separation stages, all kinds of physical (e.g. dissolution) or chemical (e.g. enzymatic or light-induced degradation) processes can be made to take place, allowing a wide variety of experiments to be performed within a single, efficient and automated analysis. All these developments are discussed in this paper and a number of critical issues are identified. A practical example, the characterization of polysorbates by high-resolution comprehensive two-dimensional liquid chromatography in combination with high-resolution mass spectrometry, is described as a culmination of recent developments in 2D-LC and as an illustration of the current state of the art.
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http://dx.doi.org/10.1039/c8fd00233aDOI Listing
August 2019

Computer-aided gradient optimization of hydrophilic interaction liquid chromatographic separations of intact proteins and protein glycoforms.

J Chromatogr A 2019 Aug 3;1598:67-76. Epub 2019 Apr 3.

Division of Bioanalytical Chemistry, Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam, de Boelelaan 1085, 1081 HV Amsterdam, The Netherlands; University of Amsterdam, van 't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group, Science Park 904, 1098 XH Amsterdam, The Netherlands; Centre for Analytical Sciences Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands. Electronic address:

Protein glycosylation is one of the most common and critical post-translational modification, which results from covalent attachment of carbohydrates to protein backbones. Glycosylation affects the physicochemical properties of proteins and potentially their function. Therefore it is important to establish analytical methods which can resolve glycoforms of glycoproteins. Recently, hydrophilic-interaction liquid chromatography (HILIC)-mass spectrometry has demonstrated to be a useful tool for the efficient separation and characterization of intact protein glycoforms. In particular, amide-based stationary phases in combination with acetonitrile-water gradients containing ion-pairing agents, have been used for the characterization of glycoproteins. However, finding the optimum gradient conditions for glycoform resolution can be quite tedious as shallow gradients (small decrease of acetonitrile percentage in the elution solvent over a long time) are required. In the present study, the retention mechanism and peak capacity of HILIC for non-glycosylated and glycosylated proteins were investigated and compared to reversed-phase liquid chromatography (RPLC). For both LC modes, ln k vs. φ plots of a series of test proteins were calculated using linear solvent strength (LSS) analysis. For RPLC, the plots were spread over a wider φ range than for HILIC, suggesting that HILIC methods require shallower gradients to resolve intact proteins. Next, the usefulness of computer-aided method development for the optimization of the separation of intact glycoform by HILIC was examined. Five retention models including LSS, adsorption, and mixed-mode, were tested to describe and predict glycoprotein retention under gradient conditions. The adsorption model appeared most suited and was applied to the gradient prediction for the separation of the glycoforms of six glycoproteins (Ides-digested trastuzumab, alpha-acid glycoprotein, ovalbumin, fetuin and thyroglobulin) employing the program PIOTR. Based on the results of three scouting gradients, conditions for high-efficiency separations of protein glycoforms varying in the degree and complexity of glycosylation was achieved, thereby significantly reducing the time needed for method optimization.
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http://dx.doi.org/10.1016/j.chroma.2019.03.038DOI Listing
August 2019

Analysis of charged acrylic particles by on-line comprehensive two-dimensional liquid chromatography and automated data-processing.

Anal Chim Acta 2019 Apr 9;1054:184-192. Epub 2019 Jan 9.

University of Amsterdam, van 't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group, Science Park 904, 1098 XH, Amsterdam, the Netherlands.

A thorough understanding of particle formation and polymer growth during emulsion polymerization is indispensable for the development of particles and products with very specific properties. This has created a demand for the detailed characterization of various properties and property distributions - and the relation between these. A method is described that enables comprehensive, simultaneous determination of the size distribution of nanoparticles and the molecular-weight distribution of the constituting polymers as a function of the particle size. The result is a complete two-dimensional distribution that details the interdependence of the two parameters. The approach comprehensively combines hydrodynamic chromatography with size-exclusion chromatography. An automated band-broadening filter has been developed to improve the accuracy of the measured distributions. The algorithm utilizes automated curve-fitting approaches to describe detected particle distributions for each horizontal slice of the 2D-LC chromatogram, and filters band broadening using calibration curves. The method has been applied to samples of complex nanoparticles comprising hydrophobic, hydrophilic and charged moieties, viz. stabilized dispersions of poly[(methyl methacrylate)-co-(butyl acrylate)-co-(methacrylic acid)]-nanoparticles in water. We consistently found that, within a single population of particles, the weight-average molecular weight increases with particle size.
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http://dx.doi.org/10.1016/j.aca.2018.12.059DOI Listing
April 2019

On-line microfluidic immobilized-enzyme reactors: A new tool for characterizing synthetic polymers.

Anal Chim Acta 2019 Apr 12;1053:62-69. Epub 2018 Dec 12.

Universiteit van Amsterdam, Van 't Hoff Institute for Molecular Sciences, Science Park 904, 1098 XH, Amsterdam, the Netherlands.

Biodegradable polymeric materials may eventually replace biostable materials for medical applications, including therapeutic devices, scaffolds for tissue engineering, and drug-delivery vehicles. To further develop such materials, a more fundamental understanding is necessary to correlate parameters including chemical-composition distribution within a macromolecular structure with the final properties of the material, including particle-size. A wide variety of analytical techniques have been applied for the characterization of polymer materials, including hyphenated techniques such as comprehensive two-dimensional liquid chromatography (LC × LC). In this context, we have investigated enzymatic degradation of polyester-based nanoparticles, both in-solution and by the use of an immobilized-enzyme reactor (IMER). We have demonstrated for the first time the implementation of such an IMER in a size-exclusion chromatography system for on-line degradation and subsequent analysis of the polymer degradation products. The effect of residence times ranging from 12 s to 4 min on polymer degradation was assessed. IMER-assisted degradation is much faster compared to in-solution degradation, which requires several hours to days, and opens the possibility to use such reactors in LC × LC modulation interfaces.
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http://dx.doi.org/10.1016/j.aca.2018.12.002DOI Listing
April 2019

Characterization of Dye Extracts from Historical Cultural-Heritage Objects Using State-of-the-Art Comprehensive Two-Dimensional Liquid Chromatography and Mass Spectrometry with Active Modulation and Optimized Shifting Gradients.

Anal Chem 2019 02 1;91(4):3062-3069. Epub 2019 Feb 1.

van 't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group , University of Amsterdam , Science Park 904 , 1098 XH , Amsterdam , The Netherlands.

Unbiased characterization of dyes and their degradation products in cultural-heritage objects requires an analytical method which provides universal separation power regardless of dye classes. Dyes are small molecules that vary widely in chemical structure and properties, which renders their characterization by a single method challenging. We have developed a comprehensive two-dimensional liquid chromatography method hyphenated with mass spectrometry and UV-vis detection. We use stationary-phase-assisted modulation to enhance the method in terms of detection limits and solvent compatibility and to reduce the analysis time. The PIOTR program was used to optimize an assembly of shifting second-dimension gradients, which resulted in a high degree of orthogonality (80% in terms of the asterisk concept). The resulting method is universally applicable to all classes of dyes extracted from cultural-heritage objects. Thanks to the high peak capacity and orthogonality, dye components can be separated from chemically similar impurities and degradation products, providing a detailed fingerprint of the dyes mixture in a specific sample. The method was applied to a number of challenging dye extracts from 17th- and 19th-century cultural-heritage objects.
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http://dx.doi.org/10.1021/acs.analchem.8b05469DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6383186PMC
February 2019

Peak-Tracking Algorithm for Use in Automated Interpretive Method-Development Tools in Liquid Chromatography.

Anal Chem 2018 12 15;90(23):14011-14019. Epub 2018 Nov 15.

van 't Hoff Institute for Molecular Sciences, Analytical Chemistry Group , University of Amsterdam , Science Park 904 , 1098 XH Amsterdam , The Netherlands.

A peak-tracking algorithm for chromatograms recorded using liquid chromatography and mass spectrometry was developed. Peaks are tracked across chromatograms using the spectrometric information, the statistical moments of the chromatographic peaks, and the relative retention. The algorithm can be applied to pair chromatographic peaks in two very different chromatograms, obtained for different samples using different methods. A fast version of the algorithm was specifically tailored to process chromatograms obtained during method development or optimization, where a few similar mobile-phase-composition gradients (same eluent components, but different ranges and programming rates) are applied to the same sample for the purpose of obtaining model parameters to describe the retention of sample components. Due to the relative similarity between chromatograms, time-saving preselection protocols can be used to locate a candidate peak in another chromatogram. The algorithm was applied to two different samples featuring isomers. The automatically tracked peaks and the resulting retention parameters generally yielded prediction errors of less than 1%.
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http://dx.doi.org/10.1021/acs.analchem.8b03929DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6282104PMC
December 2018

Recent Developments in Two-Dimensional Liquid Chromatography: Fundamental Improvements for Practical Applications.

Anal Chem 2019 01 14;91(1):240-263. Epub 2018 Nov 14.

University of Amsterdam , van 't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group , Science Park 904 , 1098 XH Amsterdam , The Netherlands.

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http://dx.doi.org/10.1021/acs.analchem.8b04841DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6322149PMC
January 2019

Comprehensive two-dimensional liquid chromatography of heavy oil.

J Chromatogr A 2018 Aug 5;1564:110-119. Epub 2018 Jun 5.

Universiteit van Amsterdam, Van' t Hoff Institute for Molecular Sciences, Analytical-Chemistry Group, Science Park 904, 1098 XH, Amsterdam, The Netherlands.

Heavy oil refers to the part of crude oil that is not amenable to further distillation. Processing of these materials to useful products provides added value, but requires advanced technology as well as extensive characterization in order to optimize the yield of the most profitable products. The use of comprehensive two-dimensional liquid chromatography (LC × LC) was investigated for the characterization of de-asphalted short residue, also called maltenes. Initial studies were performed on a polycyclic aromatic hydrocarbon standard, an aromatic extract of hydrowax, and the fractions obtained after solvent fractionation of the maltenes. Cyanopropyl- and octadecyl-silica were used as first-dimension and second-dimension columns, respectively. The analysis of the maltenes and fractions thereof required a change in first-dimension stationary phase to biphenyl as well as an increase in modifier strength to improve recovery. The extensive characterization of maltenes with LC × LC within four hours was demonstrated. The Program for the Interpretive Optimization of Two-dimensional Resolution (PIOTR) has been applied to aid the method development, but due to the absence of specific peaks in the chromatograms it was challenging to apply to the maltenes or its fractions. Nonetheless, an approach is suggested for resolution optimization in cases such as the present one, in which regions of co-elution are observed, rather than clearly separated peaks.
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http://dx.doi.org/10.1016/j.chroma.2018.06.001DOI Listing
August 2018

Applicability of retention modelling in hydrophilic-interaction liquid chromatography for algorithmic optimization programs with gradient-scanning techniques.

J Chromatogr A 2017 Dec 11;1530:104-111. Epub 2017 Nov 11.

University of Amsterdam, van 't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group, Science Park 904, 1098 XH, Amsterdam, The Netherlands.

Computer-aided method-development programs require accurate models to describe retention and to make predictions based on a limited number of scouting gradients. The performance of five different retention models for hydrophilic-interaction chromatography (HILIC) is assessed for a wide range of analytes. Gradient-elution equations are presented for each model, using Simpson's Rule to approximate the integral in case no exact solution exists. For most compound classes the adsorption model, i.e. a linear relation between the logarithm of the retention factor and the logarithm of the composition, is found to provide the most robust performance. Prediction accuracies depended on analyte class, with peptide retention being predicted least accurately, and on the stationary phase, with better results for a diol column than for an amide column. The two-parameter adsorption model is also attractive, because it can be used with good results using only two scanning gradients. This model is recommended as the first-choice model for describing and predicting HILIC retention data, because of its accuracy and linearity. Other models (linear solvent-strength model, mixed-mode model) should only be considered after validating their applicability in specific cases.
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http://dx.doi.org/10.1016/j.chroma.2017.11.017DOI Listing
December 2017

Optimizing separations in online comprehensive two-dimensional liquid chromatography.

J Sep Sci 2018 Jan 23;41(1):68-98. Epub 2017 Nov 23.

University of Amsterdam, Analytical-Chemistry Group, van 't Hoff Institute for Molecular Sciences, Amsterdam, The Netherlands.

Online comprehensive two-dimensional liquid chromatography has become an attractive option for the analysis of complex nonvolatile samples found in various fields (e.g. environmental studies, food, life, and polymer sciences). Two-dimensional liquid chromatography complements the highly popular hyphenated systems that combine liquid chromatography with mass spectrometry. Two-dimensional liquid chromatography is also applied to the analysis of samples that are not compatible with mass spectrometry (e.g. high-molecular-weight polymers), providing important information on the distribution of the sample components along chemical dimensions (molecular weight, charge, lipophilicity, stereochemistry, etc.). Also, in comparison with conventional one-dimensional liquid chromatography, two-dimensional liquid chromatography provides a greater separation power (peak capacity). Because of the additional selectivity and higher peak capacity, the combination of two-dimensional liquid chromatography with mass spectrometry allows for simpler mixtures of compounds to be introduced in the ion source at any given time, improving quantitative analysis by reducing matrix effects. In this review, we summarize the rationale and principles of two-dimensional liquid chromatography experiments, describe advantages and disadvantages of combining different selectivities and discuss strategies to improve the quality of two-dimensional liquid chromatography separations.
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http://dx.doi.org/10.1002/jssc.201700863DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5814945PMC
January 2018

Nanoparticle Analysis by Online Comprehensive Two-Dimensional Liquid Chromatography combining Hydrodynamic Chromatography and Size-Exclusion Chromatography with Intermediate Sample Transformation.

Anal Chem 2017 09 8;89(17):9167-9174. Epub 2017 Aug 8.

Analytical-Chemistry Group, University of Amsterdam, van't Hoff Institute for Molecular Sciences , Science Park 904, 1098 XH Amsterdam, The Netherlands.

Polymeric nanoparticles have become indispensable in modern society with a wide array of applications ranging from waterborne coatings to drug-carrier-delivery systems. While a large range of techniques exist to determine a multitude of properties of these particles, relating physicochemical properties of the particle to the chemical structure of the intrinsic polymers is still challenging. A novel, highly orthogonal separation system based on comprehensive two-dimensional liquid chromatography (LC × LC) has been developed. The system combines hydrodynamic chromatography (HDC) in the first-dimension to separate the particles based on their size, with ultrahigh-performance size-exclusion chromatography (SEC) in the second dimension to separate the constituting polymer molecules according to their hydrodynamic radius for each of 80 to 100 separated fractions. A chip-based mixer is incorporated to transform the sample by dissolving the separated nanoparticles from the first-dimension online in tetrahydrofuran. The polymer bands are then focused using stationary-phase-assisted modulation to enhance sensitivity, and the water from the first-dimension eluent is largely eliminated to allow interaction-free SEC. Using the developed system, the combined two-dimensional distribution of the particle-size and the molecular-size of a mixture of various polystyrene (PS) and polyacrylate (PACR) nanoparticles has been obtained within 60 min.
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http://dx.doi.org/10.1021/acs.analchem.7b01906DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5588091PMC
September 2017

Size-exclusion chromatography using core-shell particles.

J Chromatogr A 2017 Feb 14;1486:96-102. Epub 2016 Dec 14.

University of Amsterdam, van't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group, Science Park 904, 1098 XH Amsterdam, The Netherlands.

Size-exclusion chromatography (SEC) is an indispensable technique for the separation of high-molecular-weight analytes and for determining molar-mass distributions. The potential application of SEC as second-dimension separation in comprehensive two-dimensional liquid chromatography demands very short analysis times. Liquid chromatography benefits from the advent of highly efficient core-shell packing materials, but because of the reduced total pore volume these materials have so far not been explored in SEC. The feasibility of using core-shell particles in SEC has been investigated and contemporary core-shell materials were compared with conventional packing materials for SEC. Columns packed with very small core-shell particles showed excellent resolution in specific molar-mass ranges, depending on the pore size. The analysis times were about an order of magnitude shorter than what could be achieved using conventional SEC columns.
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http://dx.doi.org/10.1016/j.chroma.2016.12.015DOI Listing
February 2017

Program for the interpretive optimization of two-dimensional resolution.

J Chromatogr A 2016 Jun 29;1450:29-37. Epub 2016 Apr 29.

University of Amsterdam, van't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group, Science Park 904, 1098 XH Amsterdam, The Netherlands.

The challenge of fully optimizing LC×LC separations is horrendous. Yet, it is essential to address this challenge if sophisticated LC×LC instruments are to be utilized to their full potential in an efficient manner. Currently, lengthy method development is a major obstacle to the proliferation of the technique, especially in industry. A program was developed for the rigorous optimization of LC×LC separations, using gradient-elution in both dimensions. The program establishes two linear retention models (one for each dimension) based on just two LC×LC experiments. It predicts LC×LC chromatograms using a simple van-Deemter model to generalize band-broadening. Various objectives (analysis time, resolution, orthogonality) can be implemented in a Pareto-optimization framework to establish the optimal conditions. The program was successfully applied to a separation of a complex mixture of 54 aged, authentic synthetic dyestuffs, separated by ion-exchange chromatography and ion pair chromatography. The main limitation experienced was the retention-time stability in the first (ion-exchange) dimension. Using the PIOTR program LC×LC method development can be greatly accelerated, typically from a few months to a few days.
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http://dx.doi.org/10.1016/j.chroma.2016.04.061DOI Listing
June 2016

Characterization of synthetic dyes by comprehensive two-dimensional liquid chromatography combining ion-exchange chromatography and fast ion-pair reversed-phase chromatography.

J Chromatogr A 2016 Mar 30;1436:141-6. Epub 2016 Jan 30.

University of Amsterdam, van 't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group, Science Park 904, 1098 XH Amsterdam, The Netherlands.

In the late 19th century, newly invented synthetic dyes rapidly replaced the natural dyes on the market. The characterization of mixtures of these so-called early synthetic dyes is complicated through the occurrence of many impurities and degradation products. Conventional one-dimensional liquid chromatography does not suffice to obtain fingerprints with sufficient resolution and baseline integrity. Comprehensive two-dimensional liquid chromatography (LC×LC) is employed in this study, with ion-exchange chromatography in the first dimension and fast ion-pair liquid chromatography in the second. Retention in the first dimension is largely determined by the number of charges, while the selection of a small ion-pair reagent (tetramethylammonium hydroxide) in the second dimension causes retention to be largely determined by the molecular structure of the dye. As a result, there is a high degree of orthogonality of the two dimensions, similar to the values typically encountered in GC×GC. The proposed LC×LC method shows a theroretical peak capacity of about 2000 in an analysis time of about three hours. Clear, informative fingerprints are obtained that open a way to a more efficient characterization of dyes used in objects of cultural heritage.
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http://dx.doi.org/10.1016/j.chroma.2016.01.070DOI Listing
March 2016
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