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A simple array integrating machine learning for identification of flavonoids in red wines.

Jiaojiao Qin1, Hao Wang1, Yu Xu1

  • 1State Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China jinsong.han@cpu.edu.cn.

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A novel sensor array effectively detects bioactive flavonoids in red wine using phenylboronic acid modified perylene diimide derivatives. This method accurately identifies 14 flavonoids and distinguishes 8 wine types, showcasing potential for food analysis.

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Area of Science:

  • Analytical Chemistry
  • Biochemistry
  • Materials Science

Background:

  • Bioactive flavonoids in red wine offer cardiovascular benefits but are difficult to identify.
  • Existing methods for flavonoid detection are often complex and time-consuming.

Purpose of the Study:

  • To develop a simple yet effective sensor array for the detection and discrimination of flavonoids.
  • To assess the array's capability in analyzing complex mixtures like red wine.

Main Methods:

  • Fabrication of a sensor array using phenylboronic acid modified perylene diimide derivatives (PDIs).
  • Utilizing multiple non-specific interactions for flavonoid binding and fluorescence modulation.
  • Employing machine learning algorithms to analyze fluorescence intensity patterns for identification.

Main Results:

  • The PDI sensor array accurately discriminated 14 diverse flavonoids with 100% accuracy.
  • The array successfully performed parallel detection of 8 different red wine types and origins.
  • Fluorescence modulation patterns generated unique fingerprints for each flavonoid.

Conclusions:

  • The developed sensor array offers a highly accurate and efficient method for flavonoid detection.
  • This technology shows significant potential for analyzing complex food matrices, including red wine.
  • The array's ability to differentiate wine types highlights its application in food authentication and quality control.