Search research articles

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

Data Validation

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:

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

Safety assessment of the process Bandera Twin used to recycle post-consumer PET into food contact materials.

EFSA journal. European Food Safety Authority·2026

Same author

Safety assessment of the substance l-aspartic acid, <i>N</i>-benzoyl-, disodium salt for use in plastic food contact materials.

EFSA journal. European Food Safety Authority·2026

Same author

Integrated Redox Profiling: Simultaneous Determination of Ubiquinol-10, Ubiquinone-10, and Alpha-Lipoic Acid in Serum by LC-MS/MS.

Metabolites·2026

Same author

Exploring Cerumen as an Alternative Matrix for Drug Detection and Quantification: Method Development and Application in Clinical Samples.

Drug testing and analysis·2026

Same author

Correction: What's in a name? Metabolite identification: challenges and pitfalls in untargeted metabolomics.

Metabolomics : Official journal of the Metabolomic Society·2026

Same author

Safety assessment of poly(2-ethyl-2-oxazoline) for use in food contact materials.

EFSA journal. European Food Safety Authority·2026

Search research articles

相关实验视频

Updated: Jun 2, 2025

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

基于LC-MS的代谢学质量控制和验证问题

Olga Begou^1,2,3, Helen G Gika^4,5, Georgios Theodoridis^1,2,3

¹Department of Chemistry, Aristotle University, Thessaloniki, Greece.

Methods in molecular biology (Clifton, N.J.)

|January 15, 2025

概括

这项研究引入了一项质量控制 (QC) 协议,以提高非目标代谢学的可靠性. 该方法侧重于监测液态染色学-质谱学 (LC-MS) 分析中的分析精度,以改进数据验证.

关键词:

生物样本生物样本这就是LC-MS.质量控制质量控制没有针对性的代谢组分.验证验证验证验证

更多相关视频

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

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

Published on: May 20, 2013

Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Published on: January 5, 2021

相关实验视频

Last Updated: Jun 2, 2025

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

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

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

Published on: May 20, 2013

Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Published on: January 5, 2021

科学领域:

分析化学分析化学
生物化学生化学
系统生物学系统生物学

背景情况:

非定位代谢学提供了一种整体方法来分析复杂的生物样本中的小分子.
尽管有大量的研究投资,但在代谢学中,数据验证和质量控制方面的挑战仍然存在.
社区的努力,包括工作组和研讨会,突出了在代谢学中强有力的质量控制 (QC) 的需要.

研究的目的:

描述一种新的质量控制 (QC) 协议,用于监测基于液态染色体质谱 (LC-MS) 的代谢分析.
解决在非目标代谢学中改善验证和数据质量的关键需求.
提出一种以提高代谢学分析精度为重点的方法.

主要方法:

为LC-MS代谢学制定和实施一个特定的质量控制 (QC) 协议.
专注于监测分析精度作为一个关键绩效指标.
尿液分析的详细方法,可适应各种生物矩阵.

主要成果:

描述的QC协议有效地监测LC-MS代谢学中的分析精度.
该方法提供了一个框架,以提高代谢学数据的可靠性.
已证明适用于尿样,有可能用于更广泛的矩阵使用.

结论:

提出的质量控制协议对于确保代谢学研究的质量和可重复性至关重要.
实施这种质量控制策略可以显著改善对非目标代谢学结果的验证.
这种方法为代谢学社区提供了一种有价值的工具,可以提高各种生物样本的分析严谨性.