Chemometric methods in data processing of mass spectrometry-based metabolomics: A review

Area of Science:

• Analytical Chemistry
• Bioinformatics
• Biotechnology

Background:

• Metabolomics is an emerging field with significant potential in biotechnology.
• Mass spectrometry is a key technique for generating metabolomics data.
• Effective data processing is crucial for extracting meaningful biological insights.

Purpose of the Study:

• To review recent and potential advances in chemometric methods for metabolomics data processing.
• To illustrate advanced skills in data preprocessing, metabolite identification, variable selection, and modeling.
• To bridge the gap between raw metabolomics data and biological knowledge.

Main Methods:

• Focus on chemometric methods and statistical data analytical strategies.
• Integration of modern chemical analysis techniques with metabolomics.
• Illustration of advanced data processing skills.

Main Results:

• Significant developments in metabolomics data analysis are outlined.
• Advanced skills in preprocessing, identification, selection, and modeling are demonstrated.
• Improved extraction of biological information from high-throughput datasets.

Conclusions:

• Chemometric methods are vital for advancing metabolomics research.
• Developments discussed enhance the biological interpretation of complex datasets.
• Future perspectives on high-throughput data analysis are considered.