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Optimizing microfluidic chip for rapid SARS-CoV-2 detection using Taguchi method and artificial neural network PSO.

Sameh Kaziz1, Fraj Echouchene2,3, Mohamed Hichem Gazzah4

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Summary

This study optimized microfluidic biosensors for rapid SARS-CoV-2 detection. The Damköhler number significantly influenced performance, with particle swarm optimization enhancing prediction accuracy.

Keywords:
ANOVABiosensorParticle swarm optimizationSARS-CoV-2Taguchi method

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Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Nanotechnology

Background:

  • Microfluidic biosensors enable real-time analysis of viruses like SARS-CoV-2 using small sample volumes.
  • Optimizing these biosensors is crucial for rapid and accurate diagnostic tools.

Purpose of the Study:

  • To optimize a microfluidic biochip for rapid SARS-CoV-2 detection.
  • To identify optimal operating parameters and assess their impact on biosensor performance.

Main Methods:

  • Utilized Taguchi orthogonal array L9(3^4) to vary Reynolds number (Re), Damköhler number (Da), Schmidt number (Sc), and reaction surface position (X).
  • Employed signal-to-noise (S/N) ratios and analysis of variance (ANOVA) to determine optimal parameters.
  • Applied particle swarm optimization (PSO) for predicting biosensor performance based on L81(3^4) experimental design.

Main Results:

  • Optimal parameters identified as Re=4.10^-2, Da=1000, Sc=10^5, and X=1.
  • Damköhler number (Da) was the most significant factor (91% influence), while reaction surface position (X) had minimal impact (0.3%).
  • The PSO model showed superior predictive performance compared to the conventional multi-layer perception (MLP) model.

Conclusions:

  • The optimized microfluidic biosensor design enhances rapid SARS-CoV-2 detection capabilities.
  • Parameter optimization, particularly focusing on the Damköhler number, is key to improving biosensor efficiency.
  • Particle swarm optimization presents a powerful approach for predicting and refining biosensor performance.