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Related Concept Videos

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
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Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview01:02

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Ultraviolet–visible (UV–visible or UV–Vis) spectroscopy is an analytical technique that investigates the interaction between matter and UV–Vis light within the electromagnetic spectrum. This method is widely used for its versatility, simplicity, and relatively quick data acquisition, making it valuable for both qualitative and quantitative analysis. When UV–Vis radiation passes through a material,  molecules absorb light depending on the energy required for...
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UV–Vis Spectroscopy of Conjugated Systems01:32

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Organic compounds with conjugated double bonds show strong absorption features in the UV–visible region of the electromagnetic spectrum attributed to π → π* electronic excitations. Generally, a UV–vis absorption spectrum is recorded as a plot of absorbance vs wavelength. The wavelength of maximum absorbance, which manifests as a peak in the absorption spectrum, is denoted as λmax.
One of the factors influencing λmax is the extent...
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Related Experiment Video

Updated: Aug 7, 2025

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

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Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods.

Xi Chen1, Zhao Yang1, Yang Xu2

  • 1Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.

Journal of Pharmaceutical Analysis
|March 13, 2023
PubMed
Summary
This summary is machine-generated.

This review explores multicomponent quantitative analysis (MCQA) for complex systems using the single standard to determine multiple components (SSDMC) approach. It discusses improving method robustness, optimizing chromatography, and leveraging software for efficient analysis.

Keywords:
Multicomponent quantification analysisPredictive softwareSingle standard to determine multiple components

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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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Area of Science:

  • Analytical Chemistry
  • Pharmacology
  • Natural Product Chemistry

Background:

  • Complex systems like natural products and foods contain multiple active components.
  • Biological activity often arises from synergistic effects, necessitating multicomponent quantitative analysis (MCQA).
  • The single standard to determine multiple components (SSDMC) approach aids MCQA when individual standards are unavailable.

Purpose of the Study:

  • To review advancements in MCQA for complex systems, focusing on the SSDMC approach.
  • To address challenges in improving MCQA method repeatability, robustness, and efficiency.
  • To explore optimization strategies for chromatographic conditions and the integration of computer software in MCQA.

Main Methods:

  • Summarizing relative correction factor calculation methods in SSDMC over the last five years.
  • Reviewing techniques to enhance peak capacity and quantitative accuracy in MCQA, including column selection and 2D chromatography.
  • Introducing computer software for predicting chromatographic conditions and analytical parameters for intelligent method development.

Main Results:

  • Recent advancements in SSDMC calculation methods and accuracy evaluations are presented.
  • Strategies for improving chromatographic separation and quantitative accuracy in MCQA are discussed.
  • The potential of computer software for intelligent method development in MCQA is highlighted.

Conclusions:

  • Improvements in MCQA repeatability, robustness, and efficiency are crucial for complex system analysis.
  • Advanced chromatographic techniques and computational tools offer promising solutions for MCQA challenges.
  • This review provides methodological insights for enhancing the analysis of complex samples, particularly through MCQA.