Author :
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ISBN 13 : 9789464835250
Total Pages : 0 pages
Book Rating : 4.8/5 (352 download)
Book Synopsis Chemometric Tools for Automated Method-development and Data Interpretation in Liquid Chromatography by :
Download or read book Chemometric Tools for Automated Method-development and Data Interpretation in Liquid Chromatography written by and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.