Fingerprinting of hydroxy propyl methyl cellulose by comprehensive two-dimensional liquid chromatography-mass spectrometry of monomers resulting from acid hydrolysis

Hydroxypropyl methyl cellulose (HPMC) is a type of cellulose derivative with properties that render it useful in e.g. food, cosmetics, and pharmaceutical industry. The substitution degree and composition of the β-glucose subunits of HPMC affect its physical and functional properties, but HPMC characterization is challenging due to its high structural heterogeneity, including many isomers. In this study, comprehensive two-dimensional liquid chromatography-mass spectrometry was used to examine substituted glucose monomers originating from complete acid hydrolysis of HPMC. Resolution between the different monomers was achieved using a C18 and cyano column in the first and second LC dimension, respectively. The data analysis process was structured to obtain fingerprints of the monomers of interest. The results revealed that isomers of the respective monomers could be selectively separated based on the position of substituents. The examination of two industrial HPMC products revealed differences in overall monomer composition. While both products contained monomers with a similar degree of substitution, they exhibited distinct regioselectivity.

Characterization of Dye-Loaded Poly(lactic-co-glycolic acid) Nanoparticles by Comprehensive Two-Dimensional Liquid Chromatography Combining Hydrodynamic and Reversed-Phase Liquid Chromatography

Analytical methods for the assessment of drug-delivery systems (DDSs) are commonly suitable for characterizing individual DDS properties, but do not allow determination of several properties simultaneously. A comprehensive online two-dimensional liquid chromatography (LC × LC) system was developed that is aimed to be capable of characterizing both nanoparticle size and encapsulated cargo over the particle size distribution of a DDS by using one integrated method. Polymeric nanoparticles (NPs) with encapsulated hydrophobic dyes were used as model DDSs. Hydrodynamic chromatography (HDC) was used in the first dimension to separate the intact NPs and to determine the particle size distribution. Fractions from the first dimension were taken comprehensively and disassembled online by the addition of an organic solvent, thereby releasing the encapsulated cargo. Reversed-phase liquid chromatography (RPLC) was used as a second dimension to separate the released dyes. Conditions were optimized to ensure the complete disassembly of the NPs and the dissolution of the dyes during the solvent modulation step. Subsequently, stationary-phase-assisted modulation (SPAM) was applied for trapping and preconcentration of the analytes, thereby minimizing the risk of analyte precipitation or breakthrough. The developed HDC × RPLC method allows for the characterization of encapsulated cargo as a function of intact nanoparticle size and shows potential for the analysis of API stability.

Sample transformation in online separations: how chemical conversion advances analytical technology

While the advent of modern analytical technology has allowed scientists to determine the complexity of mixtures, it also spurred the demand to understand these sophisticated mixtures better. Chemical transformation can be used to provide insights into properties of complex samples such as degradation pathways or molecular heterogeneity that are otherwise unaccessible. In this article, we explore how sample transformation is exploited across different application fields to empower analytical methods. Transformation mechanisms include molecular-weight reduction, controlled degradation, and derivatization. Both offline and online transformation methods have been explored. The covered studies show that sample transformation facilitates faster reactions (e.g. several hours to minutes), reduces sample complexity, unlocks new sample dimensions (e.g. functional groups), provides correlations between multiple sample dimensions, and improves detectability. The article highlights the state-of-the-art and future prospects, focusing in particular on the characterization of protein and nucleic-acid therapeutics, nanoparticles, synthetic polymers, and small molecules.

Comprehensive two-dimensional gas chromatography- A discussion on recent innovations

Although comprehensive 2-D GC is an established and often applied analytical method, the field is still highly dynamic thanks to a remarkable number of innovations. In this review, we discuss a number of recent developments in comprehensive 2-D GC technology. A variety of modulation methods are still being actively investigated and many exciting improvements are discussed in this review. We also review interesting developments in detection methods, retention modeling, and data analysis.

Computer-driven optimization of complex gradients in comprehensive two-dimensional liquid chromatography

Method development in comprehensive two-dimensional liquid chromatography (LC × LC) is a complicated endeavor. The dependency between the two dimensions and the possibility of incorporating complex gradient profiles, such as multi-segmented gradients or shifting gradients, renders method development by "trial-and-error" time-consuming and highly dependent on user experience. In this work, an open-source algorithm for the automated and interpretive method development of complex gradients in LC × LC-mass spectrometry (MS) was developed. A workflow was designed to operate within a closed-loop that allowed direct interaction between the LC × LC-MS system and a data-processing computer which ran in an unsupervised and automated fashion. Obtaining accurate retention models in LC × LC is difficult due to the challenges associated with the exact determination of retention times, curve fitting because of the use of gradient elution, and gradient deformation. Thus, retention models were compared in terms of repeatability of determination. Additionally, the design of shifting gradients in the second dimension and the prediction of peak widths were investigated. The algorithm was tested on separations of a tryptic digest of a monoclonal antibody using an objective function that included the sum of resolutions and analysis time as quality descriptors. The algorithm was able to improve the separation relative to a generic starting method using these complex gradient profiles after only four method-development iterations (i.e., sets of chromatographic conditions). Further iterations improved retention time and peak width predictions and thus the accuracy in the separations predicted by the algorithm.

Algorithm for tracking peaks amongst numerous datasets in comprehensive two-dimensional chromatography to enhance data analysis and interpretation

Analytical data processing often requires the comparison of data, i.e. finding similarities and differences within separations. In this context, a peak-tracking algorithm was developed to compare multiple datasets in one-dimensional (1D) and two-dimensional (2D) chromatography. Two application strategies were investigated: i) data processing where all chromatograms are produced in one sequence and processed simultaneously, and ii) method optimization where chromatograms are produced and processed cumulatively. The first strategy was tested on data from comprehensive 2D liquid chromatography and comprehensive 2D gas chromatography separations of academic and industrial samples of varying compound classes (monoclonal-antibody digest, wine volatiles, polymer granulate headspace, and mayonnaise). Peaks were tracked in up to 29 chromatograms at once, but this could be upscaled when necessary. However, the peak-tracking algorithm performed less accurate for trace analytes, since, peaks that are difficult to detect are also difficult to track. The second strategy was tested with 1D liquid chromatography separations, that were optimized using automated method-development. The strategy for method optimization was quicker to detect peaks that were still poorly separated in earlier chromatograms compared to assigning a target chromatogram, to which all other chromatograms are compared. Rendering it a useful tool for automated method optimization.

Capacitively coupled contactless conductivity detection to account for system-induced gradient deformation in liquid chromatography

The time required for method development in gradient-elution liquid chromatography (LC) may be reduced by using an empirical modelling approach to describe and predict analyte retention and peak width. However, prediction accuracy is impaired by system-induced gradient deformation, which can be especially prominent for steep gradients. As the deformation is unique to each LC instrument, it needs to be corrected for if retention modelling for optimization and method transfer is to become generally applicable. Such a correction requires knowledge of the actual gradient profile. The latter has been measured using capacitively coupled "contactless" conductivity detection (C⁴D), featuring a low detection volume (approximately 0.05 μL) and compatibility with very high pressures (80 MPa or more). Several different solvent gradients, from water to acetonitrile, water to methanol, and acetonitrile to tetrahydrofuran, could be measured directly without the addition of a tracer component to the mobile phase, exemplifying the universal nature of the approach. Gradient profiles were found to be unique for each solvent combination, flowrate, and gradient duration. The profiles could be described by convoluting the programmed gradient with a weighted sum of two distribution functions. Knowledge of the exact profiles was used to improve the inter-system transferability of retention models for toluene, anthracene, phenol, emodin, sudan-I and several polystyrene standards.

Cumulative Neutral Loss Model for Fragment Deconvolution in Electrospray Ionization High-Resolution Mass Spectrometry Data

Clean high-resolution mass spectra (HRMS) are essential to a successful structural elucidation of an unknown feature during nontarget analysis (NTA) workflows. This is a crucial step, particularly for the spectra generated during data-independent acquisition or during direct infusion experiments. The most commonly available tools only take advantage of the time domain for spectral cleanup. Here, we present an algorithm that combines the time domain and mass domain information to perform spectral deconvolution. The algorithm employs a probability-based cumulative neutral loss (CNL) model for fragment deconvolution. The optimized model, with a mass tolerance of 0.005 Da and a scoreCNL threshold of 0.00, was able to achieve a true positive rate (TPr) of 95.0%, a false discovery rate (FDr) of 20.6%, and a reduction rate of 35.4%. Additionally, the CNL model was extensively tested on real samples containing predominantly pesticides at different concentration levels and with matrix effects. Overall, the model was able to obtain a TPr above 88.8% with FD rates between 33 and 79% and reduction rates between 9 and 45%. Finally, the CNL model was compared with the retention time difference method and peak shape correlation analysis, showing that a combination of correlation analysis and the CNL model was the most effective for fragment deconvolution, obtaining a TPr of 84.7%, an FDr of 54.4%, and a reduction rate of 51.0%.

Principles and potential of solvent gradient size-exclusion chromatography for polymer analysis

The properties of a polymeric material are influenced by its underlying molecular distributions, including the molecular-weight (MWD), chemical-composition (CCD), and/or block-length (BLD) distributions. Gradient-elution liquid chromatography (LC) is commonly used to determine the CCD. Due to the limited solubility of polymers, samples are often dissolved in strong solvents. Upon injection of the sample, such solvents may lead to broadened or poorly shaped peaks and, in unfavourable cases, to "breakthrough" phenomena, where a part of the sample travels through the column unretained. To remedy this, a technique called size-exclusion-chromatography gradients or gradient size-exclusion chromatography (gSEC) was developed in 2011. In this work, we aim to further explore the potential of gSEC for the analysis of the CCD, also in comparison with conventional gradient-elution reversed-phase LC, which in this work corresponded to gradient-elution reversed-phase liquid chromatography (RPLC). The influence of the mobile-phase composition, the pore size of the stationary-phase particles, and the column temperature were investigated. The separation of five styrene/ethyl acrylate copolymers was studied with one-dimensional RPLC and gSEC. RPLC was shown to lead to a more-accurate CCD in shorter analysis time. The separation of five styrene/methyl methacrylate copolymers was also explored using comprehensive two-dimensional (2D) LC involving gSEC, i.e. SEC × gSEC and SEC × RPLC. In 2D-LC, the use of gSEC was especially advantageous as no breakthrough could occur.

Hyphenation of liquid chromatography and pyrolysis-flame ionization detection/mass spectrometry for polymer quantification and characterization

Size-exclusion chromatography (SEC) hyphenated to pyrolysis-gas chromatography (Py-GC) has been demonstrated as a powerful tool in polymer analysis. A main limitation to the wider application of the method are the long second-dimension Py-GC analysis times, resulting in limited first-dimension sampling and/or long overall run times. Therefore, we set out to develop an online hyphenated SEC×Py-MS/FID method, removing the GC separation and allowing for a drastically reduced second-dimension analysis time compared to SEC-Py-GC. The pyrolysis method had a cycle time of 1.31 min, which was facilitated by liquid nitrogen cooling of the programmable temperature vaporizer (PTV) used for pyrolysis. The developed method featured no molar mass discrimination for masses above ±1.3 kDa, rendering it applicable to most commercial polymer systems. The method was demonstrated on multiple samples, including a complex industrial sample, yielding chemical composition heterogeneity and in some cases sequence heterogeneity information over the molar mass distribution.

Closed-loop automatic gradient design for liquid chromatography using Bayesian optimization

Contemporary complex samples require sophisticated methods for full analysis. This work describes the development of a Bayesian optimization algorithm for automated and unsupervised development of gradient programs. The algorithm was tailored to LC using a Gaussian process model with a novel covariance kernel. To facilitate unsupervised learning, the algorithm was designed to interface directly with the chromatographic system. Single-objective and multi-objective Bayesian optimization strategies were investigated for the separation of two complex (n>18, and n>80) dye mixtures. Both approaches found satisfactory optima in under 35 measurements. The multi-objective strategy was found to be powerful and flexible in terms of exploring the Pareto front. The performance difference between the single-objective and multi-objective strategy was further investigated using a retention modeling example. One additional advantage of the multi-objective approach was that it allows for a trade-off to be made between multiple objectives without prior knowledge. In general, the Bayesian optimization strategy was found to be particularly suitable, but not limited to, cases where retention modelling is not possible, although its scalability might be limited in terms of the number of parameters that can be simultaneously optimized.

Composition mapping of highly substituted cellulose-ether monomers by liquid chromatography-mass spectrometry and probability-based data deconvolution

Cellulose ethers (CEs) are semi-synthetic polymers produced by derivatization of natural cellulose, yielding highly substituted products such as ethyl hydroxyethyl cellulose (EHEC) or methyl ethyl hydroxyethyl cellulose (MEHEC). CEs are commonly applied as pharmaceutical excipients and thickening agents in paints and drymix mortars. CE properties, such as high viscosity in solution, solubility, and bio-stability are of high interest to achieve required product qualities, which may be strongly affected by the substitution pattern obtained after derivatization. The average and molar degree of substitution often cannot explain functional differences observed among CE batches, and more in-depth analysis is needed. In this work, a new method was developed for the comprehensive mapping of the substitution degree and composition of β-glucose monomers of CE samples. To this end, CEs were acid-hydrolyzed and then analyzed by gradient reversed-phase liquid chromatography-mass spectrometry (LC-MS) using an acid-stable LC column and time-of-flight (TOF) mass spectrometer. LC-MS provided monomer resolution based on ethylene oxide, hydroxyl, and terminating methyl/ethyl content, allowing the assignment of detailed compositional distributions. An essential further distinction of constitutional isomer distributions was achieved using an in-house developed probability-based deconvolution algorithm. Aided by differential heat maps for visualization and straightforward interpretation of the measured LC-MS data, compositional variation between bio-stable and non-bio-stable CEs could be identified using this new approach. Moreover, it disclosed unexpected methylations in EHEC samples. Overall, the obtained molecular information on relevant CE samples demonstrated the method's potential for the study of CE structure-property relationships.

Incorporating a liquid-core-waveguide cell in recycling liquid chromatography for detailed studies of photodegradation reactions

In this work, a microfluidic photoreactor was embedded in a recycling liquid-chromatography system. Mixtures were separated on an analytical column and compounds of interest were subsequently introduced into the light-reactor cell. After degradation, the content of the light-reactor cell was reinjected onto the same column to separate the parent compound from its degradation products. A separated degradation product could be re-introduced into the photoreactor and irradiated again. The next generation of degradation products could again be separated on the same analytical column. This recycling procedure proved an excellent tool to elucidate degradation pathways. This was demonstrated using riboflavin, better known as vitamin B2. By degrading it in the first cycle, degradation products were isolated and subjected to a second degradation in the light-reactor cell. This allows pinpointing secondary products and connect these with primary degradation products. Compared to previous work, this configuration is simpler, cheaper, and more user-friendly, while offering the unique possibility to easily connect degradation products to the initial compounds in a mixture.

Combining Photodegradation in a Liquid-Core-Waveguide Cell with Multiple-Heart-Cut Two-Dimensional Liquid Chromatography

Photodegradation greatly affects everyday life. It poses challenges when food deteriorates or when objects of cultural heritage fade, but it can also create opportunities applied in advanced oxidation processes in water purification. Studying photodegradation, however, can be difficult because of the time needed for degradation, the inaccessibility of pure compounds, and the need to handle samples manually. A novel light-exposure cell, based on liquid-core-waveguide (LCW) technology, was embedded in a multiple-heart-cut two-dimensional liquid chromatography system by coupling the LCW cell to the multiple-heart-cut valve. The sample was flushed from the heart-cut loops into the cell by an isocratic pump. Samples were then irradiated using different time intervals and subsequently transferred by the same isocratic pump to a second-dimension sample loop. The mixture containing the transformation products was then subjected to the second-dimension separation. In the current setup, about 30–40% of the selected fraction was transferred. Multiple degradation products could be monitored. Degradation was found to be faster when a smaller sample amount was introduced (0.3 μg as compared to 1.5 μg). The system was tested with three applications, that is, fuchsin, a 19th-century synthetic organic colorant, annatto, a lipophilic food dye, and vitamin B complex.

An approach to high throughput measurement of accurate retention data in liquid chromatography

Efforts to model and simulate various aspects of liquid chromatography (LC) separations (e.g., retention, selectivity, peak capacity, injection breakthrough) depend on experimental retention measurements to use as the basis for the models and simulations. Often these modeling and simulation efforts are limited by datasets that are too small because of the cost (time and money) associated with making the measurements. Other groups have demonstrated improvements in throughput of LC separations by focusing on "overhead" associated with the instrument itself - for example, between-analysis software processing time, and autosampler motions. In this paper we explore the possibility of using columns with small volumes (i.e., 5 mm x 2.1 mm i.d.) compared to conventional columns (e.g., 100 mm x 2.1 mm i.d.) that are typically used for retention measurements. We find that isocratic retention factors calculated for columns with these dimensions are different by about 20%; we attribute this difference - which we interpret as an error in measurements based on data from the 5 mm column - to extra-column volume associated with inlet and outlet frits. Since retention factor is a thermodynamic property of the mobile/stationary phase system under study, it should be independent of the dimensions of the column that is used for the measurement. We propose using ratios of retention factors (i.e., selectivities) to translate retention measurements between columns of different dimensions, so that measurements made using small columns can be used to make predictions for separations that involve conventional columns. We find that this approach reduces the difference in retention factors (5 mm compared to 100 mm columns) from an average of 18% to an average absolute difference of 1.7% (all errors less than 8%). This approach will significantly increase the rate at which high quality retention data can be collected to thousands of measurements per instrument per day, which in turn will likely have a profound impact on the quality of models and simulations that can be developed for many aspects of LC separations.

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

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 (i.e., 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.

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

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 m/z 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.

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

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.

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

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.

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

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.

Recent applications of retention modelling in liquid chromatography

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.

Detection challenges in quantitative polymer analysis by liquid chromatography

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.

Perspectives on the future of multi-dimensional platforms

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.

Heterogeneity analysis of polymeric carboxylic acid functionality by selective derivatization followed by size exclusion chromatography

Waterborne polymers are increasingly applied in our society, replacing traditional solvent-borne coatings and thus reducing environmental impact of coatings. The majority of waterborne dispersions are stabilized by the incorporation of neutralizable carboxylic acid functionality. The characterization of synthetic waterborne polymer systems can be performed by a wide variety of chromatographic and spectroscopic techniques. However, none of these approaches is able to determine the acid functionality distribution over the molecular-weight distribution directly. In this research, an innovative approach is developed which enables this analysis. The approach is based on the specific and complete derivatization of carboxylic acid functionality with phenacylbromide. Size exclusion chromatography (SEC) analysis of the derivatized polymers is performed followed by ultraviolet- (UV) and refractive index (RI) detection, enabling the quantitative determination of the acid content per molecular weight fraction. The applicability of the developed protocol is shown for various polymer systems.

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

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.

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

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%.

Optimizing separations in online comprehensive two-dimensional liquid chromatography

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.

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

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.