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Bioactive compounds in functional foods using mass spectrometry: A systematic review
Fabiola Eugelio1, Marcello Mascini1, Federico Fanti1
1Department of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Italy.
Food Chemistry
|May 13, 2026
Summary
Mass spectrometry (MS) in functional food research identified over 2000 molecules, with phenolic compounds being most common. However, analytical challenges like matrix effects and lack of standardization hinder accurate bioactive compound discovery.
Area of Science:
- Food Science
- Analytical Chemistry
- Biochemistry
Background:
- Functional foods are increasingly researched for health benefits.
- Bioactive compounds in foods are key to their functionality.
- Mass spectrometry (MS) is a powerful tool for identifying these compounds.
Purpose of the Study:
- To provide a data-centric overview of bioactive compound discovery in functional foods using MS (2020-2025).
- To evaluate the methodologies and identify challenges in current foodomics research.
- To advocate for improved analytical strategies for reliable compound identification.
Main Methods:
- Systematic review of 250 foodomics studies published between 2020 and 2025.
- Analysis of identified molecules across food categories and molecular classes.
- Evaluation of mass spectrometry techniques, particularly LC-ESI-QTOF-MS/MS, and chemometric workflows.
Main Results:
- Over 2000 unique molecules identified across 14 food categories.
- Phenolic compounds (ferulic acid, gallic acid, rutin) are prevalent, especially in fruits and botanicals.
- Identified analytical vulnerabilities: extraction biases, thermal degradation, matrix effects, and false-positive annotations due to library matching.
Conclusions:
- Current MS-based foodomics research faces significant analytical and standardization challenges.
- There is a critical need for validation using authentic standards to prevent misidentification of bioactive compounds.
- Integrated analytical strategies combining untargeted HRMS discovery with targeted QqQ quantification are recommended for robust research.
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