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Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins
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Sensor development for multiple simultaneous classifications using genetically engineered M13 bacteriophages.

Yujin Lee1, Sung-Jo Kim2, Ye-Ji Kim1

  • 1Department of Nano Fusion Technology, Pusan National University, 46241, Busan, Republic of Korea.

Biosensors & Bioelectronics
|September 13, 2023
PubMed
Summary
This summary is machine-generated.

Researchers developed a novel M13 bacteriophage sensor array for classifying apple varieties, fragrances, and aging. This colorimetric sensor offers a rapid, accurate method for complex environmental analysis in various industries.

Keywords:
Apple classificationColourimetric sensorEnsemble data miningGenetic engineeringM13 bacteriophageRipeness sensor

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Area of Science:

  • Biotechnology
  • Nanotechnology
  • Chemical Sensing

Background:

  • Chemical sensors are vital for identifying substances, food quality, and detecting hazards.
  • Current portable sensors lack sensitivity and rapid response in complex environments.
  • M13 bacteriophage offers a tunable platform for sensor development.

Purpose of the Study:

  • To optimize an M13 bacteriophage-based multi-array colorimetric sensor for simultaneous classification of multiple targets.
  • To demonstrate the sensor's efficacy in classifying apple varieties, fragrances, and aging status.

Main Methods:

  • Fabrication of a multi-array colorimetric sensor using genetically engineered M13 bacteriophages.
  • Utilizing a self-templating process and manipulation of the M13 coat protein p8.
  • Employing changes in photonic crystal-like nanostructures for colorimetric detection.

Main Results:

  • The optimized sensor array achieved 100% accuracy for apple variety classification.
  • Simultaneous classification of four standard fragrances reached 100% accuracy.
  • The sensor demonstrated 84.5% accuracy in determining apple aging status.

Conclusions:

  • The M13 bacteriophage sensor optimization technique enables rapid development of sensors for multiple simultaneous classifications.
  • This approach has potential applications in medical diagnostics, environmental monitoring, and the food industry.
  • The sensor's ability to respond to complex environments with diverse targets is a significant advancement.