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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...

Sample Preparation for Analysis: Overview

Sample Preparation for Analysis: Overview

Sample preparation is an essential step in the analytical process. It involves preparing a sample so that it can be analyzed accurately. The goal is to extract the analyte, the substance you want to measure, from the sample while removing any components that may interfere with the analysis. Sample preparation techniques vary depending on the physical state of the sample.
Bulk or large solid samples are typically reduced in size using grinding, crushing, or milling techniques to increase the...

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Related Experiment Video

Updated: Oct 19, 2025

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Published on: March 14, 2013

WaveICA 2.0: a novel batch effect removal method for untargeted metabolomics data without using batch information.

Kui Deng^1,2, Falin Zhao³, Zhiwei Rong⁴

¹Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.

Metabolomics : Official Journal of the Metabolomic Society

|September 20, 2021

Summary

This summary is machine-generated.

WaveICA 2.0 removes batch effects in untargeted metabolomics without needing batch labels. This improved method enhances data quality and biological insight, outperforming existing techniques.

Keywords:

Batch effects Generalized additive model Intensity drift removal Untargeted metabolomics Wavelet transform

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Last Updated: Oct 19, 2025

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Published on: March 14, 2013

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Published on: September 20, 2022

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Area of Science:

Analytical Chemistry
Bioinformatics
Metabolomics

Background:

Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) is susceptible to batch effects, which are systematic biases unrelated to biological variation.
Existing methods like WaveICA require batch labels, limiting their application when batch information is unavailable or data comprises a single batch.

Purpose of the Study:

To develop an improved method, WaveICA 2.0, for removing batch effects in untargeted metabolomics data without relying on batch labels.
To provide a practical R package, WaveICA_2.0, for implementing the enhanced batch effect removal method.

Main Methods:

The WaveICA method was enhanced to create WaveICA 2.0, enabling batch effect correction without prior batch information.
An R package, WaveICA_2.0, was developed to facilitate the application of this novel method.

Main Results:

WaveICA 2.0 demonstrated comparable performance to the original WaveICA for multi-batch data, effectively grouping quality control and subject samples and improving data analysis metrics.
For single-batch metabolomics data, WaveICA 2.0 effectively removed intensity drift, revealed more biological information, and outperformed QC-RLSC and QC-SVRC methods.

Conclusions:

WaveICA 2.0 is a practical and effective tool for removing batch effects in untargeted metabolomics data, even when batch information is unknown.
The method enhances the reliability and biological interpretability of metabolomics datasets, broadening the applicability of batch effect correction techniques.