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Related Experiment Videos

Integrated optical surface plasmon resonance immunoprobe for simazine detection.

R D Harris1, B J Luff, J S Wilkinson

  • 1Optoelectronics Research Centre, University of Southampton, UK. rdh@orc.soton.ac.uk

Biosensors & Bioelectronics
|July 28, 1999
PubMed
Summary

This study details a novel optical biosensor for detecting the simazine herbicide. The regenerable immunoprobe achieves sensitive simazine detection in water within a 20-minute test cycle.

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Area of Science:

  • Analytical Chemistry
  • Biosensor Technology
  • Environmental Science

Background:

  • Triazine herbicides, like simazine, pose environmental risks.
  • Accurate and rapid detection methods are crucial for monitoring herbicide contamination.
  • Surface Plasmon Resonance (SPR) offers high sensitivity for biomolecular detection.

Purpose of the Study:

  • To design and characterize a regenerable integrated optical surface plasmon resonance immunoprobe.
  • To develop a sensitive detector for the triazine herbicide simazine in aqueous environments.
  • To establish an efficient detection cycle for practical applications.

Main Methods:

  • Theoretical optimization of SPR sensor design for aqueous media.
  • Fabrication of the integrated optical immunoprobe.

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  • Experimental characterization including bulk refractive index sensitivity and non-specific binding assays.
  • Binding inhibition immunoassays for simazine detection using IgG and Fab antibodies.

    • Main Results:

    • The sensor demonstrated sensitivity to refractive index changes and minimal non-specific binding of ovalbumin.
    • Lower limits of detection for simazine were determined as 0.16 µg/L (IgG) and 0.11 µg/L (Fab fragments).
    • A rapid sample test cycle of 20 minutes was successfully established.

    Conclusions:

    • The developed SPR immunoprobe is a viable and sensitive tool for detecting simazine.
    • The regenerable nature and rapid testing cycle enhance its practical applicability for environmental monitoring.
    • Further optimization could lead to even lower detection limits and broader applicability for herbicide analysis.