Benefits of solvent concentration pulses in retention time modelling of liquid chromatography

Navarro-Huerta, J.A.; Gisbert-Alonso, A.; Torres-Lapası́o, J.R.; Garcı́a-Alvarez-Coque, M.C.

doi:10.1016/j.chroma.2019.03.016

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…Baeza‐Baeza et al. combined the accuracy of isocratic data with the speed of gradient experiments by adding solvent concentration pulses in the isocratic runs [121]. The predicted parameters were found to agree with those obtained from isocratic experiments.…”

Section: Methodsmentioning

confidence: 90%

Recent applications of retention modelling in liquid chromatography

Uijl

Schoenmakers

Pirok

et al. 2020

J of Separation Science

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

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Section: Methodsmentioning

confidence: 90%

Recent applications of retention modelling in liquid chromatography

Uijl

Schoenmakers

Pirok

et al. 2020

J of Separation Science

View full text Add to dashboard Cite

show abstract

“…Before and after, other optimization methods, such as predictive elution-window shifting and stretching (PEWS) [231] and "concentration pulses" have been developed. A concentration pulse is a temporary increase in modifier content when a compound elutes and is typically used in multistep isocratic measurements [282] and multistep gradients [283,284]. These optimization strategies may require a good deal of computational power to perform brute-force computations.…”

Section: Gradient Optimizationmentioning

confidence: 99%

“…Using computer modelling to predict and optimize separation in GC × GC GC × GC 2008 [280] Benefits of solvent concentration pulses in retention time modelling of LC LC 2019 [282] Enhancement in the computation of gradient retention times in LC using root-finding methods LC 2019 [285] Gradient design for LC using multi-scale optimizations LC 2018 [284] (Continues) T A B L E 2 (Continued)…”

Section: Subcategory Year Referencementioning

confidence: 99%

Recent applications of chemometrics in one‐ and two‐dimensional chromatography

Bos

Knol

Molenaar

et al. 2020

J of Separation Science

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The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as highresolution mass spectrometry, causes an urgent need for highly efficient dataanalysis and optimization strategies. This is especially true for comprehensive twodimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from oneand two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

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“…Before and after, other optimization methods, such as predictive elution-window shifting and stretching (PEWS) [231] and "concentration pulses" have been developed. A concentration pulse is a temporary increase in modifier content when a compound elutes and is typically used in multi-step isocratic measurements [283] and multi-step gradients [284,285]. These optimization strategies may require a good deal of computational power to perform brute-force computations.…”

Section: Gradient Optimizationmentioning

confidence: 99%

Chemometric tools for automated method-development and data interpretation in liquid chromatography

Bos

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The thesis explores the challenges and advancements in the field of liquid chromatography (LC), particularly focusing on complex sample analysis using high-resolution mass spectrometry (MS) and two-dimensional (2D) LC techniques. The research addresses the need for efficient optimization and data-handling strategies in modern LC practice. The thesis is divided into several chapters, each addressing specific aspects of LC and polymer analysis. Chapter 2 provides an overview of the need for chemometric tools in LC practice, discussing methods for processing and analyzing data from 1D and 2D-LC systems and how chemometrics can be utilized for method development and optimization. Chapter 3 introduces a novel approach for interpreting the molecular-weight distribution and intrinsic viscosity of polymers, allowing quantitative analysis of polymer properties without prior knowledge of their interactions. This method correlates the curvature parameter of the Mark-Houwink plot with the polymer's structural and chemical properties. Chapters 4 and 5 focus on the analysis of cellulose ethers (CEs), essential in various industrial applications. A new method is presented for mapping the substitution degree and composition of CE samples, providing detailed compositional distributions. Another method involves a comprehensive 2D LC-MS/MS approach for analyzing hydroxypropyl methyl cellulose (HPMC) monomers, revealing subtle differences in composition between industrial HPMC samples. Chapter 6 introduces AutoLC, an algorithm for automated and interpretive development of 1D-LC separations. It uses retention modeling and Bayesian optimization to achieve optimal separation within a few iterations, significantly improving the efficiency of gradient LC separations. Chapter 7 focuses on the development of an open-source algorithm for automated method development in 2D-LC-MS systems. This algorithm improves separation performance by refining gradient profiles and accurately predicting peak widths, enhancing the reliability of complex gradient LC separations. Chapter 8 addresses the challenge of gradient deformation in LC instruments. An algorithm based on the stable function corrects instrument-specific gradient deformations, enabling accurate determination of analyte retention parameters and improving data comparability between different sources. Chapter 9 introduces a novel approach using capacitively-coupled-contactless-conductivity detection (C4D) to measure gradient profiles without adding tracer components. This method enhances inter-system transferability of retention models for polymers, overcoming the limitations of UV-absorbance detectable tracer components. Chapter 10 discusses practical choices and challenges faced in the thesis chapters, highlighting the need for well-defined standard samples in industrial polymer analysis and emphasizing the importance of generalized problem-solving approaches. The thesis identifies future research directions, emphasizing the importance of computational-assisted methods for polymer analysis, the utilization of online reaction modulation techniques, and exploring continuous distributions obtained through size-exclusion chromatography (SEC) in conjunction with triple detection. Chemometric tools are recognized as essential for gaining deeper insights into polymer chemistry and improving data interpretation in the field of LC.

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Benefits of solvent concentration pulses in retention time modelling of liquid chromatography

Cited by 8 publications

References 17 publications

Recent applications of retention modelling in liquid chromatography

Recent applications of retention modelling in liquid chromatography

Recent applications of chemometrics in one‐ and two‐dimensional chromatography

Chemometric tools for automated method-development and data interpretation in liquid chromatography

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