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

Some valuable insights have been obtained in the inherent fitting problems when trying to predict theretention time of complex, multi-modal retention modes such as encountered in HILIC and SFC. In thisstudy, we used mathematical models with known input parameters to generate different sets of numericaltest curves representative for systems exhibiting a complex, non-LSS dual retention behavior. Subse-quently, we tried to fit these data sets using some popular (non-linear) literature models. Even in caseswhere a physical fitting model exists (e.g., the mixed model in case of pure additive adsorptive andpartitioning retention), the fitting quality can only be expected to be relatively good (prediction errorsexpressed in terms of a normalized resolution error εRs) when carefully selecting the scouting runs andthe appropriate starting values for the fitting algorithm. The latter can best be done using a comprehen-sive grid search scanning a wide range of different starting values. This becomes even more importantwhen no good physical model is available and one has to use a non-physical fitting model, such as theempirical Neue-model. The use of higher-order models is found to be quasi indispensable to keep theprediction errors on the order of some ΔRs= 0.05. Also, the choice of the scouting runs becomes evenmore important using these higher-order models. For highly retained compounds we recommend usingscouting runs with long tG/t0-values or to include a run with a higher fraction of eluting solvent at thestart of the gradient. When trying to predict gradient retention, errors with which the isocratic retentionbehavior is fitted are much less important for high retention factors k than errors made in the range of knear the one at the point of elution. The results obtained with a so-called segmented Neue-model (con-taining 7 parameters) were less good and thus practically not interesting (because of the high number ofinitial runs). © 2015 Elsevier B.V

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. © 2014 Elsevier B.V

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 4. h 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. © 2014 Elsevier B.V

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. © 2015 Elsevier B.V

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 (PEWS2) 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). © 2014 Elsevier B.V

Chromatographic analysis of alkaloids in Aconitum pollen : towards new insights in plant protection mechanisms

Alkaloids are a class of naturally occurring organic nitrogen-containing bases that are found primarily in plants. They display a huge diversity with more than 3,000 different types already identified. Next to their different pharmacological and therapeutic effects, alkaloids can have a deleterious impact on organisms as they are known to be neurotoxic and cardiotoxic for mammals and insects. In the current context of worldwide bee decline, occurrence of such compounds in floral production, i.e. nectar and pollen, raises major concerns. They could be beneficial to bees by protecting them against disease and pathogens but they could also cause toxicity. Until now alkaloids, and their effect on human health, are mainly studied in vegetative parts of plants. More recently the natural occurrence of alkaloids in nectar was also studied to investigate their effect on bee health. Whereas nectar chemicals can relatively easily and quickly be analyzed by chromatography, extracting chemicals from low pollen amount remains a challenge because of pollen structure and complexity. However, characterization of pollen chemicals can lead to valuable insight in their impact on pollinators allowing the development of mitigation strategies. In this study, we used a UHPLC-(ESI)-Q-ToF/MS method allowing the identification and quantification of alkaloids in pollen matrices from four Aconitum species; A. lycoctonum, A. napellus compactum, A. napellus neomontanum and A. variegatum. Alkaloid extraction was performed using bead-beating disruption of the pollen sample and chromatographic analysis was carried out on an Acquity UPLC system interfaced with a Synapt G2 QTOF. The separation was achieved in gradient mode on an Acquity UPLC BEH C18 column and detection was performed in electrospray positive ionization mode (ES+). Alkaloid concentrations were measured as aconitine equivalents by using a pure aconitine standard as reference compound. The total amount of alkaloids in Aconitum pollen ranged from 0.75 to 1.20 mg/g with 859 different compounds detected, some of them being pollen-specific. Statistical analyses were conducted on the global dataset to assess both quantitative and qualitative interspecific differences. One-way analysis of variance was performed on the total alkaloid content while a permutational test of multivatiate analysis of variance was used to compare the alkaloid profiles among the four Aconitum species. Results are briefly discussed in an ecological context

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.FEDER PHARE ULg Analytiqu