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Active Ruthenium-Based High-Entropy Nanozyme Through a "Chemical Tongue" for Recognizing Bioactive Components in

Yuan Nie1, Yuexiang Lu2, Huashuo Dou1

  • 1Department of Chemistry, Capital Normal University, Beijing 100048, China.

Analytical Chemistry
|May 16, 2026
PubMed
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A novel high-entropy nanozyme "chemical tongue" coupled with machine learning accurately identifies seven phenolic compounds in honeysuckle. This method enhances food quality control and health by providing precise ingredient analysis.

Area of Science:

  • * Materials Science: Development of high-entropy nanozymes.
  • * Analytical Chemistry: Application of colorimetric sensor arrays and machine learning for chemical analysis.
  • * Food Science: Quality control of medicinal and edible honeysuckle.

Background:

  • * Accurate identification of bioactive ingredients in honeysuckle is crucial for food quality and human health.
  • * Existing methods for analyzing honeysuckle components can be complex or lack sensitivity.
  • * Development of advanced analytical tools is needed for precise ingredient discrimination.

Purpose of the Study:

  • * To develop a high-entropy nanozyme-based
  • chemical tongue
  • for recognizing phenolic components in honeysuckle.
  • * To integrate this system with machine learning for accurate identification and discrimination.
  • * To establish a robust analytical platform for food quality control.

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Main Methods:

  • * Design and synthesis of a quinary metal element (RuNiFeMnCo) high-entropy layered double hydroxide (HE-LDH) nanozyme with peroxidase-like activity.
  • * Utilization of the HE-LDH nanozyme to catalyze substrates, creating color-oxidized products as sensor channels.
  • * Application of machine learning algorithms (LDA and RF) for fingerprint decryption and phenolic compound recognition.
  • * Testing the system with seven phenolic compounds at low concentrations and in actual honeysuckle samples.

Main Results:

  • * The quinary HE-LDH nanozyme exhibited significantly higher specific activity (34.85 U/mg) compared to lower-entropy counterparts.
  • * The system achieved excellent recognition of seven phenolic compounds at concentrations as low as 10 nM.
  • * The random forest algorithm improved identification accuracy from 58.84% to 98.04% in real honeysuckle samples.
  • * A concentration-independent and matrix-independent recognition model was successfully constructed.

Conclusions:

  • * The integration of high-entropy nanozymes and machine learning offers a powerful new strategy for analyzing complex natural products.
  • * This approach provides a sensitive, accurate, and robust method for identifying and discriminating phenolic compounds in honeysuckle.
  • * The developed
  • chemical tongue
  • platform holds significant potential for food quality control and health-related applications.