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Multi-Layer Reflectivity Calculation Based Meta-Modeling of the Phase Mapping Function for Highly Reproducible
Tzu-Heng Wu1, Ching-Hsu Yang2, Chia-Chen Chang3,4
1Department of Biomedical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan.
Biosensors
|April 3, 2021
Summary
This study introduces a new method to improve the reproducibility of phase-sensitive surface plasmon resonance (SPR) biosensors. By using Bayesian optimization and multi-layer modeling, we enhance data mapping for more reliable biosensing, particularly for cancer exosome detection.
Area of Science:
- Biomedical Engineering
- Nanotechnology
- Analytical Chemistry
Background:
- Phase-sensitive surface plasmon resonance (SPR) biosensors offer high sensitivity but face reproducibility challenges due to complex signal data.
- Current methods for mapping SPR phase sensorgrams to refractive index units often lack accuracy or are difficult to optimize.
- Improving the reliability of SPR biosensing is crucial for accurate diagnostic applications.
Purpose of the Study:
- To develop a robust methodology for accurate phase sensorgram mapping in SPR biosensors.
- To address the limitations of existing fitting functions and numerical approaches for SPR data analysis.
- To enhance the reproducibility and practical utility of SPR biosensing technology.
Main Methods:
- Constructing mapping functions using Bayesian optimized multi-layer models of experimental SPR data.
- Employing meta-modeling via segmented polynomial approximation to handle complex multi-layer models in optimization.
- Utilizing Fresnel coefficients for precise multi-layer reflectivity calculations.
- Developing a visualization approach to assess the goodness-of-fit for optimized models.
Main Results:
- A novel methodology for constructing accurate SPR phase sensorgram mapping functions was established.
- The proposed method successfully addressed challenges in optimizing complex multi-layer models.
- Demonstrated improved data mapping and model assessment for enhanced SPR biosensor performance.
- Successfully applied the methodology to metastatic cancer exosome sensing, showcasing its practical utility.
Conclusions:
- The developed methodology significantly improves the reproducibility of phase-sensitive SPR biosensors.
- This work provides a pathway for more reliable and accurate plasmonic sensor applications.
- The approach is particularly promising for sensitive detection in complex biological samples, such as cancer exosomes.
