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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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Related Experiment Video

Updated: Jun 15, 2026

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

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RawVegetable 2.0: Refining XL-MS Data Acquisition through Enhanced Quality Control.

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

Summary

RawVegetable 2.0 enhances mass spectrometry data quality assessment, particularly for cross-linking mass spectrometry (XL-MS). This software offers new modules for identifying cross-linking specific features and evaluating spectral quality, improving data acquisition for proteomics.

Keywords:

bioinformatics cross-linking mass spectrometry quality control

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Last Updated: Jun 15, 2026

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Published on: March 13, 2020

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Area of Science:

Biochemistry
Analytical Chemistry
Bioinformatics

Background:

Mass spectrometry (MS) is crucial for proteomics and cross-linking mass spectrometry (XL-MS).
Accurate data quality assessment is essential for reliable XL-MS results.
Previous tools may lack specialized functionalities for XL-MS data interpretation.

Purpose of the Study:

To introduce RawVegetable 2.0, an advanced software for assessing mass spectrometry data quality.
To provide specialized modules for cross-linking mass spectrometry (XL-MS) applications.
To improve the optimization of data acquisition for proteomics and XL-MS.

Main Methods:

Development of four novel modules: Pair Finder, Diagnostic Peak Finder, Precursor Signal Ratio, and Xrea.
Pair Finder identifies ion doublets specific to cleavable cross-linking.
Diagnostic Peak Finder locates cross-linker reporter ions.
Precursor Signal Ratio computes precursor intensity relative to total scan signal.
Xrea analyzes peak intensity heterogeneity for spectral quality evaluation.

Main Results:

RawVegetable 2.0 offers enhanced capabilities for XL-MS data quality assessment.
The new modules provide specific functionalities for identifying cross-linking artifacts and evaluating spectral quality.
The software streamlines the optimization of mass spectrometry data acquisition.

Conclusions:

RawVegetable 2.0 is a valuable tool for researchers in proteomics and XL-MS.
The software facilitates more accurate and efficient analysis of mass spectrometry data.
Free academic access to RawVegetable 2.0 and a tutorial is provided.