Characterization and Discrimination of Chinese Marinated Pork Hocks by Volatile Compound Profiling Using Solid Phase Microextraction Gas Chromatography-Mass Spectrometry/Olfactometry, Electronic Nose and Chemometrics.

The primary aim of this study was to investigate volatile constituents for the differentiation of Chinese marinated pork hocks from four local brands, Dahongmen (DHM), Daoxiangcun (DXC), Henghuitong (HHT) and Tianfuhao (TFH). To this end the volatile constituents were evaluated by gas chromatography-mass spectrometry/olfactometry (GC-MS/O), electronic nose (E-nose) and chemometrics. A total of 62 volatile compounds were identified and quantified in all pork hocks, and 24 of them were considered as odour-active compounds because their odour activity values (OAVs) were greater than 1. Hexanal (OAV at 3.6⁻20.3), octanal (OAV at 30.3⁻47.5), nonanal (OAV at 68.6⁻166.3), 1,8-cineole (OAV at 36.4⁻133.3), anethole (OAV at 5.9⁻28.3) and 2-pentylfuran (OAV at 3.5⁻29.7) were the key odour-active compounds contributing to the integral flavour of the marinated pork hocks. According to principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) of GC-MS/O and E-nose data, the results showed that the marinated pork hocks were clearly separated into three groups: DHM, HHT, and DXC-TFH. Nine odour-active compounds, heptanal, nonanal, 3-carene, d-limonene, ß-phellandrene, p-cymene, eugenol, 2-ethylfuran and 2-pentylfuran, were determined to represent potential flavour markers for the discrimination of marinated pork hocks. This study indicated the feasibility of using GC-MS/O coupled with the E-nose method for the differentiation of the volatile profile in different brands of marinated pork hocks.

Effect of household cooking techniques on the microbiological load and the nutritional quality of mealworms (Tenebrio molitor L. 1758).

Mealworms are new food products in Europe, but consumers do not know how to cook them. Although cooking could increase the safety, acceptability, palatability, and digestibility of insects, the heating process could have deleterious effects on protein and lipid quality. Therefore, this study characterized the effects of different household cooking methods (boiling, pan-frying, vacuum cooking, and oven cooking) on the microbial load and nutritive value of mealworms, with a focus on protein digestibility and fatty acid composition. Boiling and cooking under vacuum were the most efficient techniques to reduce microbial load while maintaining the high levels of protein and polyunsaturated fatty acids of mealworms. Cooking method-related changes were very low on macronutrients content except for pan-fried mealworms which exhibited the highest lipid content. Cooking slightly changed fatty acid composition of mealworms by principally decreasing their level of saturated fatty acids but also increased the in vitro crude protein digestibility of mealworms.

Trace analysis of estrogenic compounds in surface and groundwater by ultra high performance liquid chromatography-tandem mass spectrometry as pyridine-3-sulfonyl derivatives.

Natural estrogens (estrone: E1, 17ß-estradiol: E2, estriol: E3) and synthetic 17α-ethynylestradiol (EE2) are reported as strong endocrine disruptors even at extremely low concentrations. Therefore, the watch list from the European Commission regarding emerging aquatic pollutants recommended maximum detection limits of 0.035 ng/L for EE2 and 0.4 ng/L for E1 and E2. In this study, a UHPLC-ESI-MS/MS method allowing quantification of E1, E2, E3 and EE2 in aqueous matrices was developed. The analytes were derivatized using pyridine-3-sulfonyl chloride and a broad range of product ions were generated and their specificity was assessed by analyzing both surface and groundwater. At least two product ions for each estrogenic compound were proved to be specific and hence suitable for quantification and confirmation. In complex aqueous matrices, analyte responses were particularly affected by ion suppression. This phenomenon was reduced by optimizing the clean-up and selecting a suitable stationary phase for the chromatographic separation. The limits of quantification assessed in surface water with the optimized method ranged from 0.098 ng/L (EE2) to 2.73 ng/L (E3).

Generic approach to the method development of intact protein separations using hydrophobic interaction chromatography.

We describe a liquid chromatography method development approach for the separation of intact proteins using hydrophobic interaction chromatography. First, protein retention was determined as function of the salt concentration by isocratic measurements and modeled using linear regression. The error between measured and predicted retention factors was studied while varying gradient time (between 15 and 120 min) and gradient starting conditions, and ranged between 2 and 15%. To reduce the time needed to develop optimized gradient methods for hydrophobic interaction chromatography separations, retention-time estimations were also assessed based on two gradient scouting runs, resulting in significantly improved retention-time predictions (average error < 2.5%) when varying gradient time. When starting the scouting gradient at lower salt concentrations (stronger eluent), retention time prediction became inaccurate in contrast to predictions based on isocratic runs. Application of three scouting runs and a nonlinear model, incorporating the effects of gradient duration and mobile-phase composition at the start of the gradient, provides accurate results (improved fitting compared to the linear solvent-strength model) with an average error of 1.0% and maximum deviation of -8.3%. Finally, gradient scouting runs and retention-time modeling have been applied for the optimization of a critical-pair protein isoform separation encountered in a biotechnological sample.

Supercritical fluid chromatography: a promising alternative to current bioanalytical techniques.

During the last years, chemistry was involved in the worldwide effort toward environmental problems leading to the birth of green chemistry. In this context, green analytical tools were developed as modern Supercritical Fluid Chromatography in the field of separative techniques. This chromatographic technique knew resurgence a few years ago, thanks to its high efficiency, fastness and robustness of new generation equipment. These advantages and its easy hyphenation to MS fulfill the requirements of bioanalysis regarding separation capacity and high throughput. In the present paper, the technical aspects focused on bioanalysis specifications will be detailed followed by a critical review of bioanalytical supercritical fluid chromatography methods published in the literature.

Benchmarking of Computational Methods for Creation of Retention Models in Quantitative Structure-Retention Relationships Studies.

Quantitative structure-retention relationship (QSRR) models are powerful techniques for the prediction of retention times of analytes, where chromatographic retention parameters are correlated with molecular descriptors encoding chemical structures of analytes. Many QSRR models contain geometrical descriptors derived from the three-dimensional (3D) spatial coordinates of computationally predicted structures for the analytes. Therefore, it is sensible to calculate these structures correctly, as any error is likely to carry over to the resulting QSRR models. This study compares molecular modeling, semiempirical, and density functional methods (both B3LYP and M06) for structure optimization. Each of the calculations was performed in a vacuum, then repeated with solvent corrections for both acetonitrile and water. We also compared Natural Bond Orbital analysis with the Mulliken charge calculation method. The comparison of the examined computational methods for structure calculation shows that, possibly due to the error inherent in descriptor creation methods, a quick and inexpensive molecular modeling method of structure determination gives similar results to experiments where structures are optimized using an expensive and time-consuming level of computational theory. Also, for structures with low flexibility, vacuum or gas phase calculations are found to be as effective as those calculations with solvent corrections added.

Towards a chromatographic similarity index to establish localised quantitative structure-retention relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography.

Quantitative Structure-Retention Relationships (QSRR) are used to predict retention times of compounds based only on their chemical structures encoded by molecular descriptors. The main concern in QSRR modelling is to build models with high predictive power, allowing reliable retention prediction for the unknown compounds across the chromatographic space. With the aim of enhancing the prediction power of the models, in this work, our previously proposed QSRR modelling approach called "federation of local models" is extended in ion chromatography to predict retention times of unknown ions, where a local model for each target ion (unknown) is created using only structurally similar ions from the dataset. A Tanimoto similarity (TS) score was utilised as a measure of structural similarity and training sets were developed by including ions that were similar to the target ion, as defined by a threshold value. The prediction of retention parameters (a- and b-values) in the linear solvent strength (LSS) model in ion chromatography, log k=a - blog[eluent], allows the prediction of retention times under all eluent concentrations. The QSRR models for a- and b-values were developed by a genetic algorithm-partial least squares method using the retention data of inorganic and small organic anions and larger organic cations (molecular mass up to 507) on four Thermo Fisher Scientific columns (AS20, AS19, AS11HC and CS17). The corresponding predicted retention times were calculated by fitting the predicted a- and b-values of the models into the LSS model equation. The predicted retention times were also plotted against the experimental values to evaluate the goodness of fit and the predictive power of the models. The application of a TS threshold of 0.6 was found to successfully produce predictive and reliable QSRR models (Qext(F2)2>0.8 and Mean Absolute Error<0.1), and hence accurate retention time predictions with an average Mean Absolute Error of 0.2min.

Retention prediction of low molecular weight anions in ion chromatography based on quantitative structure-retention relationships applied to the linear solvent strength model.

Quantitative Structure-Retention Relationships (QSRRs) represent a popular technique to predict the retention times of analytes, based on molecular descriptors encoding the chemical structures of the analytes. The linear solvent strength (LSS) model relating the retention factor, k to the eluent concentration (log k=a-blog [eluent]), is a well-known and accurate retention model in ion chromatography (IC). In this work, QSRRs for inorganic and small organic anions were used to predict the regression parameters a and b in the LSS model (and hence retention times) for these analytes under a wide range of eluent conditions, based solely on their chemical structures. This approach was performed on retention data of inorganic and small organic anions from the "Virtual Column" software (Thermo Fisher Scientific). These retention data were recalibrated via a "porting" methodology on three columns (AS20, AS19, and AS11HC), prior to the QSRR modeling. This provided retention data more applicable on recently produced columns which may exhibit changes of column behavior due to batch-to-batch variability. Molecular descriptors for the analytes were calculated with Dragon software using the geometry-optimized molecular structures, employing the AM1 semi-empirical method. An optimal subset of molecular descriptors was then selected using an evolutionary algorithm (EA). Finally, the QSRR models were generated by multiple linear regression (MLR). As a result, six QSRR models with good predictive performance were successfully derived for a- and b-values on three columns (R2>0.98 and RMSE<0.11). External validation showed the possibility of using the developed QSRR models as predictive tools in IC (Qext(F3)2>0.7 and RMSEP<0.4). Moreover, it was demonstrated that the obtained QSRR models for the a- and b-values can predict the retention times for new analytes with good accuracy and predictability (R2 of 0.98, RMSE of 0.89min, Qext(F3)2 of 0.96 and RMSEP of 1.18min).

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

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

Computer assisted liquid chromatographic method development for the separation of therapeutic proteins.

This review summarizes the use of computer assisted liquid chromatographic method development for the analytical characterization of protein biopharmaceuticals. Several modes of chromatography including reversed-phase liquid chromatography (RPLC), ion exchange chromatography (IEX), hydrophobic interaction chromatography (HIC) and some perspectives are discussed. For all these chromatographic modes, the most important variables for tuning retention and selectivity are exposed. Then, the retention models that were applied in the literature in RPLC, IEX and HIC are described and critically discussed. Finally, some representative examples of separation of therapeutic proteins and mAbs are shown, to illustrate the possibilities offered by the retention modeling approach. At the end, the reliability of the models was excellent, whatever the chromatographic mode, and the retention time prediction errors were systematically below 2%. In addition, a significant amount of time can be saved during method development and robustness testing.

Applicability of linear and nonlinear retention-time models for reversed-phase liquid chromatography separations of small molecules, peptides, and intact proteins.

The applicability and predictive properties of the linear solvent strength model and two nonlinear retention-time models, i.e., the quadratic model and the Neue model, were assessed for the separation of small molecules (phenol derivatives), peptides, and intact proteins. Retention-time measurements were conducted in isocratic mode and gradient mode applying different gradient times and elution-strength combinations. The quadratic model provided the most accurate retention-factor predictions for small molecules (average absolute prediction error of 1.5%) and peptides separations (with a prediction error of 2.3%). An advantage of the Neue model is that it can provide accurate predictions based on only three gradient scouting runs, making tedious isocratic retention-time measurements obsolete. For peptides, the use of gradient scouting runs in combination with the Neue model resulted in better prediction errors (<2.2%) compared to the use of isocratic runs. The applicability of the quadratic model is limited due to a complex combination of error and exponential functions. For protein separations, only a small elution window could be applied, which is due to the strong effect of the content of organic modifier on retention. Hence, the linear retention-time behavior of intact proteins is well described by the linear solvent strength model. Prediction errors using gradient scouting runs were significantly lower (2.2%) than when using isocratic scouting runs (3.2%).

Effect of gradient steepness on the kinetic performance limits and peak compression for reversed-phase gradient separations of small molecules.

The effect of gradient steepness on the kinetic performance limits and peak compression effects has been assessed in gradient mode for the separation of phenol derivatives using columns packed with 2.6µm core-shell particles. The effect of mobile-phase velocity on peak capacity was measured on a column with fixed length while maintaining the retention factor at the moment of elution and the peak-compression factor constant. Next, the performance limits were determined at the maximum system pressure of 100MPa while varying the gradient steepness. For the separation of small molecules applying a linear gradient with a broad span, the best performance limits in terms of peak capacity and analysis time were obtained applying a gradient-time-to-column-dead-time (tG/t0) ratio of 12. The magnitude of the peak-compression factor was assessed by comparing the isocratic performance with that in gradient mode applying different gradient times. Therefore, the retention factors for different analytes were determined in gradient mode and the mobile-phase composition in isocratic mode was tuned such that the difference in retention factor was smaller than 2%. Peak-compression factors were quantitatively determined between 0.95 and 0.65 depending on gradient steepness and the gradient retention factor.

On the inherent data fitting problems encountered in modeling retention behavior of analytes with dual retention mechanism.

Some valuable insights have been obtained in the inherent fitting problems when trying to predict the retention time of complex, multi-modal retention modes such as encountered in HILIC and SFC. In this study, we used mathematical models with known input parameters to generate different sets of numerical test curves representative for systems exhibiting a complex, non-LSS dual retention behavior. Subsequently, we tried to fit these data sets using some popular (non-linear) literature models. Even in cases where a physical fitting model exists (e.g., the mixed model in case of pure additive adsorptive and partitioning retention), the fitting quality can only be expected to be relatively good (prediction errors expressed in terms of a normalized resolution error ɛRs) when carefully selecting the scouting runs and the appropriate starting values for the fitting algorithm. The latter can best be done using a comprehensive grid search scanning a wide range of different starting values. This becomes even more important when no good physical model is available and one has to use a non-physical fitting model, such as the empirical Neue-model. The use of higher-order models is found to be quasi indispensable to keep the prediction errors on the order of some ΔRs=0.05. Also, the choice of the scouting runs becomes even more important using these higher-order models. For highly retained compounds we recommend using scouting runs with long tG/t0-values or to include a run with a higher fraction of eluting solvent at the start of the gradient. When trying to predict gradient retention, errors with which the isocratic retention behavior is fitted are much less important for high retention factors k than errors made in the range of k near the one at the point of elution. The results obtained with a so-called segmented Neue-model (containing 7 parameters) were less good and thus practically not interesting (because of the high number of initial runs).

Computer-assisted multi-segment gradient optimization in ion chromatography.

This study reports simulation and optimization of ion chromatography separations using multi-segment gradient elution. First, an analytical expression for the gradient retention factor under these complex elution profiles was derived. This allows a rapid retention time prediction calculations under different gradient conditions, during computer-assisted method development. Next, these analytical expressions were implemented in an in-house written Matlab(®) routine that searches for the optimal (multi-segment) gradient conditions, either via a four-segment grid search or via the recently proposed one-segment-per-component search, in which the slope is adjusted after the elution of each individual component. Evaluation of the retention time simulation and optimization approaches was performed on a mixture of 18 inorganic anions and different subsets with varying number of compounds. The two considered multi-segment gradient optimization searches resulted in similar proposed gradient profiles, and corresponding chromatograms. Moreover, the resultant chromatograms were clearly superior to the chromatograms obtained from the best simple linear gradient profiles, found via a fine grid search. The proposed approach is useful for automated method development in ion chromatography in which complex elution profiles are often used to increase the separation power.

Possibilities of retention modeling and computer assisted method development in supercritical fluid chromatography.

The multi-modal retention mechanism in supercritical fluid chromatography (SFC) results in a non-linear dependency of log(k) on the fraction of organic solvent φ and log(φ). In the present study, the possibility of retention modeling for method development purposes in SFC was investigated, considering several non-linear isocratic relationships. Therefore, both isocratic and gradient runs were performed, involving different column chemistries and analytes possessing diverse physico-chemical properties. The isocratic retention data of these compounds could be described accurately using the non-linear retention models typically used in HILIC and reversed-phase LC. The interconversion between isocratic and gradient retention data was found to be less straightforward than in RPLC and HILIC because of pressure effects. The possibility of gradient predictions using gradient scouting runs to estimate the retention parameters was investigated as well, showing that predictions for other gradients with the same starting conditions were acceptable (always below 5%), whereas prediction errors for gradients with a different starting condition were found to be highly dependent on the compound. The second part of the study consisted of the gradient optimization of two pharmaceutical mixtures (one involving atorvastatin and four related impurities, and one involving a 16 components mixture including eight drugs and their main phase I metabolites). This could be done via individual retention modeling based on gradient scouting runs. The best linear gradient was found via a grid search and the best multi-segment gradient via the previously published one-segment-per-component search. The latter improved the resolution between the critical pairs for both mixtures, while still giving accurate prediction errors (using the same starting concentrations as the gradient scouting runs used to build the model). The optimized separations were found in less than 3 h and 8 h of analysis time (including equilibration times), respectively.

Use of individual retention modeling for gradient optimization in hydrophilic interaction chromatography: separation of nucleobases and nucleosides.

In this study, the separation of twelve nucleobases and nucleosides was optimized via chromatogram simulation (i.e., prediction of individual retention times and estimation of the peak widths) with the use of an empirical (reversed-phase) non-linear model proposed by Neue and Kuss. Retention time prediction errors of less than 2% were observed for all compounds on different stationary phases. As a single HILIC column could not resolve all peaks, the modeling was extended to coupled-column systems (with different stationary phase chemistries) to increase the separation efficiency and selectivity. The analytical expressions for the gradient retention factor on a coupled column system were derived and accurate retention time predictions were obtained (<2% prediction errors in general). The optimized gradient (predicted by the optimization software) included coupling of an amide and an pentahydroxy functionalized silica stationary phases with a gradient profile from 95 to 85%ACN in 6 min and resulted in almost baseline separation of the twelve nucleobases and nucleosides in less than 7 min. The final separation was obtained in less than 4h of instrument time (including equilibration times) and was fully obtained via computer-based optimization. As such, this study provides an example of a case where individual retention modeling can be used as a way to optimize the gradient conditions in the HILIC mode using a non-linear model such as the Neue and Kuss model.

A universal comparison study of chromatographic response functions.

We report on a large scale in silico comparison study of so-called chromatographic response functions (CRFs). These are single number descriptors of the separation quality that can be used to guide search-based optimizations for chromatographic separations. A comprehensive set of literature and new CRFs were compared for their ability to guide a search based on first order chromatographic data (i.e., no spectral information available) and for cases where the number of sample compounds is not known beforehand. The results are discussed based on the available separation power. It was found that CRFs increasing monotonically with the number of observed peaks perform significantly better than those that do not possess this property. CRFs based on the discrimination factor or the peak-to-valley ratio can better cope with peak asymmetry than CRFs based on Snyder resolution Rs. Unfortunately, the former lose their advantage as soon as the noise level becomes significant. Most CRFs perform best when the search is conducted on a column offering just, or, even better, a bit less than the required efficiency to baseline separate the sample. The best results over the entire range of possible efficiencies are obtained with a CRF giving preference to the number of observed compounds before further ranking the conditions based on the achieved separation resolution or the required analysis time. When the search is conducted on columns with an insufficient efficiency, even the best possible CRFs suffer from the incomplete information about the sample, and deviating searches cannot be avoided without resorting to spectral information of the sample.

Enhanced selectivity and search speed for method development using one-segment-per-component optimization strategies.

Linear gradient programs are very frequently used in reversed phase liquid chromatography to enhance the selectivity compared to isocratic separations. Multi-linear gradient programs on the other hand are only scarcely used, despite their intrinsically larger separation power. Because the gradient-conformity of the latest generation of instruments has greatly improved, a renewed interest in more complex multi-segment gradient liquid chromatography can be expected in the future, raising the need for better performing gradient design algorithms. We explored the possibilities of a new type of multi-segment gradient optimization algorithm, the so-called "one-segment-per-group-of-components" optimization strategy. In this gradient design strategy, the slope is adjusted after the elution of each individual component of the sample, letting the retention properties of the different analytes auto-guide the course of the gradient profile. Applying this method experimentally to four randomly selected test samples, the separation time could on average be reduced with about 40% compared to the best single linear gradient. Moreover, the newly proposed approach performed equally well or better than the multi-segment optimization mode of a commercial software package. Carrying out an extensive in silico study, the experimentally observed advantage could also be generalized over a statistically significant amount of different 10 and 20 component samples. In addition, the newly proposed gradient optimization approach enables much faster searches than the traditional multi-step gradient design methods.

Gradient-elution parameters in capillary liquid chromatography for high-speed separations of peptides and intact proteins.

This contribution relates to the assessment of gradient-elution parameters in capillary liquid chromatography affecting the peak widths in the reversed-phase separation of peptides and intact proteins. Gradient separations were performed using both a poly(sytrene-co-divinylbenzene) monolithic column and a microparticulate fused-core column (silica C18, 2.7µm). The applicability of the conventional linear (LSS) and non-linear solvent-strength model (Neue-Kuss) were investigated to describe the retention behaviour of the compounds as a function of the mobile-phase composition. This was performed by using a wide range of gradient conditions, including different gradient slopes (ß, ranging from 0.05 to 0.65min(-1)) and mobile-phase compositions (Δϕ, i.e. gradient span). Although the LSS-model provided accurate retention time predictions (<1.3% deviation) of scouting runs with more conventional gradient slopes, the prediction of high-speed separations with a high degree of accuracy (<2%) could only be obtained with the non-linear model. The solvent-strength parameters resulting from the use of both models, as well as the retention factors at the moment of elution (ke), further served as input parameters to assess the influence of the gradient slope on the expected peak-compression effects in gradient mode, with a focus on high-speed separations. The importance of the correct model choice was emphasized in terms of compression; while the LSS-model lead to the conclusion of peak broadening rather than peak sharpening, the use of a more accurate non-linear model showed the existence of peak compression effect. The results presented in this manuscript show the occurrence of gradient-related focusing effects, which appear to be more prevalent for extremely fast separations.

Retention modeling and method development in hydrophilic interaction chromatography.

In the present study, the possibility of retention modeling in the HILIC mode was investigated, testing several different literature relationships over a wide range of different analytical conditions (column chemistries and mobile phase pH) and using analytes possessing diverse physico-chemical properties. Furthermore, it was investigated how the retention prediction depends on the number of isocratic or gradient trial or initial scouting runs. The most promising set of scouting runs seems to be a combination of three isocratic runs (95, 90 and 70%ACN) and one gradient run (95 to 65%ACN in 10min), as the average prediction errors were lower than using six equally spaced isocratic runs and because it is common in Method development (MD) to perform at least one scouting gradient run in the screening step to find out the best column, temperature and pH conditions. Overall, the retention predictions were much less accurate in HILIC than what is usually experienced in RPLC. This has severe implications for MD, as it restricts the use of commercial software packages that require the simulation of the retention of every peak in the chromatogram. To overcome this problem, the recently proposed predictive elution window shifting and stretching (PEWS(2)) approach can be used. In this computer-assisted MD strategy, only an (approximate) prediction of the retention of the first and the last peak in the chromatogram is required to conduct a well-targeted trial-and-error search, with suggested search conditions uniformly covering the entire possible search and elution space. This strategy was used to optimize the separation of three representative pharmaceutical mixtures possessing diverse physico-chemical properties (pteridins, saccharides and cocktail of drugs/metabolites). All problems could be successfully handled in less than 2.5h of instrument time (including equilibration).