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Bacterial Detection & Identification Using Electrochemical Sensors
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Dual enzyme induced colorimetric sensor for simultaneous identifying multiple pathogens.

Weiwei Chen1, Ming Li2, Zikang Chen1

  • 1College of Material, Chemistry and Chemical Engineering, Key Laboratory of Organosilicon Chemistry and Material Technology, Ministry of Education, Hangzhou Normal University, Hangzhou, 311121, Zhejiang, China.

Biosensors & Bioelectronics
|May 3, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel colorimetric sensor for rapid foodborne pathogen detection. The sensor accurately identifies multiple bacteria using a single probe and double-enzyme-induced colorimetry, improving public health diagnostics.

Keywords:
ColorimetricDouble-enzymeGold nanorodsLinear discriminant analysisMultiple foodborne pathogens

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Area of Science:

  • Biotechnology
  • Analytical Chemistry
  • Food Science

Background:

  • Current methods for foodborne pathogen identification are often slow, complex, and susceptible to environmental variations.
  • Accurate and rapid detection is crucial for public health and preventing foodborne illnesses.

Purpose of the Study:

  • To develop a novel, rapid, and accurate colorimetric sensor for the simultaneous identification of multiple foodborne pathogens.
  • To overcome the limitations of existing diagnostic methods through a simplified and sensitive approach.

Main Methods:

  • Utilized a double-enzyme-induced colorimetric strategy based on alkaline phosphatase (ALP) activity.
  • Employed manganese dioxide flowers (MnO2 NFs) and gold nanorods (Au NRs) for signal amplification and color generation.
  • Integrated RGB signal conversion with linear discriminant analysis (LDA) for accurate bacterial identification.

Main Results:

  • Achieved 99.57% accuracy in identifying multiple bacteria, including five common foodborne pathogens.
  • Demonstrated successful simultaneous detection across diverse food matrices like shrimp, meat, and milk.
  • The sensor's colorimetric response correlated with bacterial ALP levels and Au NR plasmon resonance.

Conclusions:

  • The developed colorimetric sensor offers a rapid, simple, and highly accurate method for identifying foodborne pathogens.
  • This innovative approach holds significant potential for improving food safety and public health surveillance.
  • The sensor's versatility across different environments makes it a promising tool for real-world applications.