Analysis of amino acids using serially coupled columns

Serially coupled column liquid chromatography: An alternative separation tool

Despite the rapid development of liquid chromatography (LC) in recent decades, it remains a challenge to achieve the desired chromatographic separation of complex matrices using a single column. Multi-column LC techniques, particularly serially coupled column LC (SCC-LC), have emerged as a promising solution to overcome this challenge. While more attention has been focused on heart-cutting or comprehensive two-dimensional LC, reviews specifically focusing on SCC-LC, which offers advantages in terms of precision and facile instrumentation, are scarce. Here, our concerns are devoted to the progress summary regarding the instrumentation and applications of SCC-LC. Emphasis is placed on column selection aiming to enlarge peak capacity, selectivity, or both through the optimization of combination types (e.g. RPLC-RPLC, -RPLC-HILIC, and achiral-chiral LC), connection devices (e.g. zero dead volume connector, tubing, and T-type connector), elution program (i.e. isocratic or gradient) and detectors (e.g. mass spectrometer, ultraviolet detector, and fluorescence detector). The application of SCC-LC in pharmaceutical, biological, environmental, and food fields is also reviewed, and future perspectives and potential directions for SCC-LC are discussed. We envision that the review can give meaningful information to analytical scientists when facing heavy chromatographic separation tasks for complicated matrices.

In Silico Method Development of Achiral and Chiral Tandem Column Reversed-phase Liquid Chromatography for Multicomponent Pharmaceutical Mixtures

Tandem column liquid chromatography (LC) is a convenient, cost-effective approach to resolve multicomponent mixtures by serially coupling columns on readily available one-dimensional separation systems without specialized user training. Yet, adoption of this technique remains limited, mainly due to the difficulty in identifying optimal selectivity out of many possible tandem column combinations. At this point, method development and optimization require laborious "hit-or-miss" experimentation and "blind" screening when investigating different column selectivity without standard analytes. As a result, many chromatography practitioners end up combining two columns of similar selectivity, limiting the scope and potential of tandem column LC as a mainstay for industrial applications. To circumvent this challenge, we herein introduce a straightforward in silico multifactorial approach as a framework to expediently map the separation landscape across multiple tandem columns (achiral and chiral) and eluent combinations (isocratic and gradient elution) under reversed-phase LC conditions. Retention models were built using commercially available LC simulator software showcasing less than 2% difference between experimental and simulated retention times for analytes of interest in multicomponent pharmaceutical mixtures (e.g., metabolites and cyclic peptides).

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.

Interpretive search of optimal isocratic and gradient separations in micellar liquid chromatography in extended organic solvent domains

Micellar liquid chromatography (MLC) is a reversed-phase mode with mobile phases containing an organic solvent and a micellised surfactant. Most procedures developed in MLC are implemented in the isocratic mode, since the general elution problem in chromatography is less troublesome. However, gradient elution may be still useful in MLC to analyse mixtures of compounds within a wide range of polarities, in shorter times. MLC using gradients is attractive to determine by direct injection moderate to low polar compounds in physiological samples. In these analyses, the use of initial micellar conditions (isocratic or gradient) with a fixed amount of surfactant above the critical micellar concentration, keeping the organic solvent content low, will provide better protection of the column against the precipitation of the proteins in the physiological fluid. Once the proteins are swept away, the elution strength can be increased using a positive gradient of organic solvent to reduce the analysis time. This may give rise to the transition from the micellar to the submicellar mode, since micelles are destroyed at sufficiently high concentration of organic solvent. In this work, several retention models covering extended solvent domains in MLC are developed and tested, and applied to investigate the performance in isocratic, linear and multi-linear gradient separations. The study was applied to the screening of β-adrenoceptor antagonists in urine samples, using mobile phases prepared with sodium dodecyl sulphate and 1-propanol. Predicted chromatograms were highly accurate in all situations, although suffered of baseline problems and minor shifts for peaks eluting close to a steep gradient segment. Two columns (C18 and C8) were investigated, with the C8 column being preferable owing to the smaller amount of adsorbed surfactant.

Multi-scale optimisation vs. genetic algorithms in the gradient separation of diuretics by reversed-phase liquid chromatography

Multi-linear gradients are a convenient solution to get separation of complex samples by modulating carefully the gradient slope, in order to accomplish the local selectivity needs for each particular solute cluster. These gradients can be designed by trial-and-error according to the chromatographer experience, but this strategy becomes quickly inappropriate for complex separations. More evolved solutions imply the sequential construction of multi-segmented gradients. However, this strategy discards part of the search space in each step of the construction and, again, cannot deal properly with very complex samples. When the complexity is too large, the only valid alternative for finding the best gradient is the use of global search methods, such as genetic algorithms (GAs). Recently, a new global approach where the level of detail is increased along the search has been proposed, namely Multi-scale optimisation (MSO). In this strategy, cubic splines are applied to build intermediate curves to define any arbitrary solvent variation function. Subdivision schemes are used to generate the cubic splines and control their level of detail. The search was subjected to a number of restrictions, such as avoiding long elution and favouring a balanced peak distribution. The aim of this work is evaluating and comparing the results of GAs and MSO. Both approaches were tested with a set of 14 diuretics and probenecid, eluted with acetonitrile-water mixtures using a C18 column. Satisfactory baseline resolution was obtained with an analysis time of 15-16 min. We found that GAs optimisation offered results equivalent to those provided by MSO, when the penalisation parameters were included in the cost function.