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Related Experiment Video

Updated: Sep 18, 2025

Author Spotlight: Technologies and Challenges in Elemental Analysis of Food Samples
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Artificial intelligence-enabled analysis methods and their applications in food chemistry.

Chunyan Gu1, Gang Wang1, Weihua Zhuang1

  • 1Department of Laboratory Medicine, Precision Medicine Translational Research Center, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.

Critical Reviews in Food Science and Nutrition
|June 22, 2025
PubMed
Summary

Artificial intelligence (AI) is revolutionizing food chemistry by enhancing analytical methods for better food quality control, contaminant detection, and ingredient design. This technology offers precise and intelligent solutions for complex food analysis challenges.

Keywords:
Food chemistryartificial intelligencefood quality controlfood safetymachine learning

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Area of Science:

  • Food Chemistry
  • Analytical Chemistry
  • Artificial Intelligence

Background:

  • Food chemistry analyzes food composition, properties, and health impacts at molecular levels.
  • Advancements in artificial intelligence (AI) are transforming food chemistry analysis.
  • AI offers efficient, precise, and intelligent solutions for food analysis.

Purpose of the Study:

  • To review the integration of AI technologies with conventional analytical methods in food chemistry.
  • To highlight recent advancements and applications of AI in food analysis.
  • To demonstrate AI's potential in various food chemistry domains.

Main Methods:

  • Review of AI-driven approaches in spectroscopic analysis.
  • Examination of AI applications in chromatography and mass spectrometry.
  • Analysis of AI integration with sensor technology for food analysis.

Main Results:

  • AI enhances analytical efficiency and accuracy in food chemistry.
  • AI facilitates improved food quality control and contaminant detection.
  • AI aids in identifying bioactive constituents, nutritional analysis, and novel ingredient design.

Conclusions:

  • AI integration offers innovative solutions for food chemistry research and applications.
  • AI significantly advances analytical capabilities in food science.
  • The synergy of AI and traditional methods promises a future of intelligent food analysis.