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Related Experiment Video

Updated: Jul 31, 2025

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Development and application of a data processing method for food metabolomics analysis.

Yuanluo Lei1, Xiaoying Chen1, Jiachen Shi1

  • 1State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Reacher Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, No. 1800, Lihu Road, Wuxi 214122, Jiangsu, People's Republic of China. yjxutju@gmail.com.

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Summary

This study presents an integrated data processing method for food metabolomics using KNIME and OpenMS. The approach enhances untargeted LC-MS data analysis, significantly reducing false positives for reliable results.

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Area of Science:

  • Food science
  • Analytical chemistry
  • Bioinformatics

Background:

  • Food metabolomics generates vast datasets, posing downstream analysis challenges.
  • Existing tools for food metabolomics data analysis are fragmented and lack integration.
  • Untargeted LC-MS (liquid chromatography-mass spectrometry) data processing requires robust and unified methods.

Purpose of the Study:

  • To develop an integrated data processing method for untargeted LC-MS metabolomics data in food systems.
  • To combine computational MS tools from OpenMS within the KNIME workflow system.
  • To improve the accuracy and efficiency of food metabolomics data analysis.

Main Methods:

  • Integration of OpenMS computational MS tools into the KNIME workflow system.
  • Development of a data processing pipeline for raw LC-MS data.
  • Implementation of MS1 and MS2 spectra-based identification workflows, including GNPSExport-GNPS.
  • Utilizing retention time and mass-to-charge ratio (m/z) tolerance for combined identification.

Main Results:

  • The developed method successfully processes untargeted LC-MS data, producing high-quality visualizations.
  • Combining MS1 and MS2 spectra-based identification workflows via tolerance significantly reduces false positives.
  • Filtering with tolerance removed over 50% of potential identifications while retaining 90% of correct ones.
  • Demonstrated a rapid and reliable approach for food metabolomics data processing.

Conclusions:

  • The integrated KNIME-OpenMS method offers a powerful solution for food metabolomics data challenges.
  • This approach enhances the reliability of metabolite identification in complex food samples.
  • The developed method facilitates more efficient and accurate downstream analysis in food metabolomics research.