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Related Concept Videos

High-Performance Liquid Chromatography: Introduction01:11

High-Performance Liquid Chromatography: Introduction

High-performance liquid chromatography(HPLC), formerly referred to as High-pressure liquid chromatography, is a powerful technique used to separate, identify, and quantify components in complex mixtures. The term "high pressure" refers to using high pressure to push the liquid mobile phase through the tightly packed columns.
In HPLC, two phases play a critical role in the separation process:
High-Performance Liquid Chromatography: Elution Process01:05

High-Performance Liquid Chromatography: Elution Process

In High-Performance Liquid Chromatography (HPLC), the elution process is critical to the separation of analytes and the quality of chromatographic results. Elution describes how compounds move through the column and separate based on their interactions with the mobile and stationary phases. This process determines the resolution, peak shape, and retention times in the chromatogram, which are essential for identifying and quantifying components in complex mixtures. Understanding the elution...
High-Performance Liquid Chromatography: Instrumentation00:57

High-Performance Liquid Chromatography: Instrumentation

High-performance liquid chromatography, or HPLC, is an analytical technique that separates liquid samples under high pressures. An HPLC instrument consists of glass bottles for storing solvents called mobile phase reservoirs. HPLC-grade solvents are used to maintain high purity, and the dissolved gases are removed using a degasser, such as a vacuum pumping system or sparging with helium. The solvents are then pumped into the analytical column using a screw-driven syringe or reciprocating pumps.
Development of Analytical Methods01:21

Development of Analytical Methods

An analytical methodology can be divided into four sequential steps: technique, method, procedure, and protocol. A technique is a scientific principle that rationalizes a specific phenomenon through chemical measurements. Adapting a technique for analyzing a sample of interest is termed a method. The procedure outlines the directions for performing the analysis via an analytical method. The protocol is the detailed guidelines on the procedure, which should be strictly followed to obtain the...
Data Validation01:15

Data Validation

Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Chromatographic Methods: Classification01:12

Chromatographic Methods: Classification

Chromatographic techniques are classified in three ways: the classification is based on the physical state of the stationary and mobile phases, how the mobile phase and the stationary phase contact each other, or through the chemical or physical processes that isolate the components of the sample. Typically, the mobile phase is either a liquid or gas, while the stationary phase is either a solid or a liquid layer applied to a solid surface.
Chromatographic techniques are typically named by...

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Updated: May 15, 2026

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)
11:00

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

Published on: May 20, 2013

Uncertainty-driven model-based search methods for method development in liquid chromatography.

Leon E Niezen1, Deirdre Cabooter2, Gert Desmet1

  • 1Vrije Universiteit Brussel, Department of Chemical Engineering, Pleinlaan 2, 1050 Brussel, Belgium.

Journal of Chromatography. A
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

Uncertainty-driven strategies significantly improve chromatographic separation accuracy, especially when peaks overlap. These methods outperform conventional approaches by reducing search failures, though they require more analysis time for better retention data.

Keywords:
Chromatogram simulationOptimizationPredictive uncertaintyRetention modelling

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Applications of Liquid-Chromatography Tandem Mass Spectrometry in Natural Products Research: Tropane Alkaloids as a Case Study
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Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)
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Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems
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Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems

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Applications of Liquid-Chromatography Tandem Mass Spectrometry in Natural Products Research: Tropane Alkaloids as a Case Study
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Applications of Liquid-Chromatography Tandem Mass Spectrometry in Natural Products Research: Tropane Alkaloids as a Case Study

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Area of Science:

  • Analytical Chemistry
  • Chromatography
  • Method Development

Background:

  • Empirical retention modeling uses initial experiments to build analyte retention models.
  • Smart algorithms can update these models to find optimal separation conditions.

Purpose of the Study:

  • Compare different search strategies for optimizing chromatographic separations.
  • Evaluate strategy performance under varying peak detectability.

Main Methods:

  • In silico comparison of three search strategies: CRF-driven, global uncertainty-driven, and per-analyte uncertainty-driven.
  • Evaluated using 122 randomized mixtures and varying peak detectability (resolution thresholds).

Main Results:

  • Uncertainty-driven strategies significantly outperformed CRF-driven searches when peak overlap occurred.
  • At Rs = 0.5, uncertainty-driven searches failed 0.8% vs. 14% for CRF-driven.
  • At Rs = 1.0, failure rates were 4% (uncertainty-driven) vs. 42% (CRF-driven).

Conclusions:

  • Uncertainty-driven strategies offer superior accuracy for chromatographic separation optimization compared to CRF-driven approaches.
  • Improved accuracy comes with increased total analysis time due to richer data acquisition.