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Quantitative SERS sensor for mycotoxins with extraction and identification function.

Yang Zhang1, Chuping Zhao1, Pierre Picchetti2

  • 1International Joint Research Laboratory of Intelligent Agriculture and Agriproducts Processing, China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China.

Food Chemistry
|June 15, 2024
PubMed
Summary
This summary is machine-generated.

A new sensor rapidly detects multiple food toxins on-site using surface-enhanced Raman scattering (SERS). This method combines extraction and detection for accurate mycotoxin analysis in resource-limited settings.

Keywords:
Food safetyMycotoxinsSERSSensor

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Area of Science:

  • Analytical Chemistry
  • Materials Science
  • Food Safety

Background:

  • On-site food toxin monitoring is crucial, especially in resource-limited areas.
  • Surface-enhanced Raman scattering (SERS) offers sensitive and rapid detection of mycotoxins.
  • Existing methods often require extensive sample pretreatment.

Purpose of the Study:

  • To develop a SERS-based sensor for simultaneous on-site extraction and detection of multiple mycotoxins.
  • To achieve an analytically relevant detection limit for mycotoxins in food samples.
  • To enable rapid identification of lethal mycotoxin contamination with minimal sample preparation.

Main Methods:

  • Fabrication of a sensor with a magnetic core, polydopamine shell, and gold nanoparticles.
  • Integration of extraction and SERS detection for multiple mycotoxin analysis.
  • Utilizing multiclass partite least squares analysis for spectral identification.

Main Results:

  • The sensor achieved a detection limit of approximately 1 ng/mL for multiple mycotoxins.
  • The assay demonstrated effective concentration and identification of mycotoxins.
  • Successful testing of the sensor in corn samples was performed.

Conclusions:

  • The developed SERS assay enables rapid, on-site analysis of multiple mycotoxins.
  • This approach significantly reduces sample pretreatment requirements.
  • The sensor shows great potential for real-time food safety monitoring in diverse environments.