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High-Throughput Recognition and Detection of Multiclass Antibiotics Based on Fluorescence-Ultraviolet Dual-Signal

Manjun Guan1, Guilong Wang1, Xinyi Liu1

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This study developed a novel sensor array using carbon quantum dots (CQDs) for accurate antibiotic detection. The CQD-based sensor array effectively identifies and quantifies various antibiotics in complex samples with high precision.

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Area of Science:

  • Analytical Chemistry
  • Materials Science
  • Environmental Science

Background:

  • Precise identification of antibiotics is challenging due to structural similarities and complex environmental matrices.
  • Carbon quantum dots (CQDs) offer unique fluorescent properties for sensing applications.

Purpose of the Study:

  • To develop a novel sensor array for the efficient and reliable detection and discrimination of antibiotics.
  • To utilize CQDs with distinct fluorescent properties for a multi-channel sensing platform.
  • To integrate machine learning for enhanced antibiotic analysis in complex samples.

Main Methods:

  • Synthesis of distinct carbon quantum dots (CQDs) via microwave-assisted pyrolysis.
  • Fabrication of single-component dual-channel and two-component four-channel sensor arrays using CQDs.
  • Application of fluorescence and ultraviolet (UV) spectroscopy for signal acquisition.
  • Utilizing pattern recognition methods like LDA, HCA, and PCA for data analysis.

Main Results:

  • A dual-signal sensor array successfully discriminated and detected fluoroquinolone antibiotics.
  • A four-channel sensor array accurately identified and quantified five antibiotics (tetracycline, cefixime, fluoroquinolones) and their mixtures.
  • Achieved 100% accuracy in antibiotic identification and quantification with low detection limits (3.9-14.0 nM).
  • Well-separated clusters in LDA score plots indicated high discrimination capability.

Conclusions:

  • The developed fluorescence/UV dual-signal sensor array strategy is effective for antibiotic analysis.
  • Machine learning-assisted pattern recognition significantly enhances the efficiency and reliability of antibiotic detection.
  • This approach offers a promising solution for analyzing antibiotics in complex environmental matrices.