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Sample Preparation for Analysis: Advanced Techniques

Sample Preparation for Analysis: Advanced Techniques

Accurate analysis of complex samples often requires advanced preparation techniques to achieve reliable and reproducible results. Samples containing inorganic or organic materials can be challenging to dissolve or decompose effectively. Standard sample preparation methods include acid digestion, fusion, dry ashing, and wet digestion.
Acid digestion with strong acids is commonly used to dissolve inorganic materials that are insoluble (do not dissolve) in water. This method can be useful for...

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Updated: Jun 15, 2026

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

RawVegetable 2.0:通过加强质量控制来完善XL-MS数据采集

Louise Ulrich Kurt¹, Milan Avila Clasen¹, Ísis Venturi Biembengut¹

¹Laboratory for Structural and Computational Proteomics, Carlos Chagas Institute - Fiocruz Parana, Curitiba, Parana 81310-020, Brazil.

Journal of proteome research

|February 1, 2024

概括

RawVegetable 2.0 增强了质谱数据质量评估,特别是交叉链接质谱 (XL-MS). 该软件提供了新的模块,用于识别交叉链接的特定特征和评估光谱质量,改善蛋白质组学数据采集.

关键词:

生物信息学是一种生物信息学.交叉链接质谱仪的质谱仪.质量控制质量控制质量控制

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Fruit Volatile Analysis Using an Electronic Nose

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Last Updated: Jun 15, 2026

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

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科学领域:

生物化学生物化学
分析化学分析化学
生物信息学是一种生物信息学.

背景情况:

质谱学 (MS) 对蛋白质组学和交联质谱学 (XL-MS) 是至关重要的.
准确的数据质量评估对于可靠的XL-MS结果至关重要.
以前的工具可能缺乏用于XL-MS数据解释的专用功能.

研究的目的:

推出RawVegetable 2.0,一个用于评估质谱数据质量的先进软件.
为交叉连接质谱学 (XL-MS) 应用提供专门的模块.
改进蛋白质组学和XL-MS数据采集的优化.

主要方法:

开发了四个新型模块:对寻找器,诊断峰值寻找器,前体信号比率和Xrea.
配对查找器识别了特定于可切割交叉连接的离子双重.
诊断峰值寻找器定位交叉链接器的记者离子.
前体信号比率计算前体强度相对于总扫描信号.
Xrea分析了峰值强度的异质性,用于光谱质量评估.

主要成果:

RawVegetable 2.0为XL-MS数据质量评估提供了增强的功能.
新的模块提供了特定的功能,用于识别交叉链接文物和评估光谱质量.
该软件简化了质谱数据采集的优化.

结论:

对于蛋白质组学和XL-MS研究人员来说,RawVegetable 2.0是一个有价值的工具.
该软件有助于更准确,更有效地分析质谱数据.
提供免费的学术访问RawVegetable 2.0和一个教程.