Taguchi method: artificial neural network approach for the optimization of high-efficiency microfluidic biosensor for...

Area of Science:

• Biomedical Engineering
• Microfluidics
• Biosensor Technology

Background:

• The COVID-19 pandemic necessitates rapid diagnostic solutions.
• Microfluidic biosensors offer a promising avenue for quick detection of SARS-CoV-2.
• Optimizing microfluidic designs is key to enhancing biosensor performance.

Purpose of the Study:

• To optimize a microchip flow confinement method for microfluidic biosensors.
• To investigate the impact of confinement flow parameters on biosensor response time.
• To develop predictive models for microfluidic biosensor response time.

Main Methods:

• Numerical simulations using two-dimensional Navier-Stokes equations.
• Taguchi's L9(33) orthogonal array for experimental design.
• Analysis of signal-to-noise ratios, ANOVA, multiple linear regression (MLR), and artificial neural networks (ANN).

Main Results:

• The optimal combination of control factors was determined for reduced response time.
• Confinement channel position was identified as the most significant factor (62% contribution) in reducing response time.
• The ANN model demonstrated higher prediction accuracy compared to the MLR model.

Conclusions:

• The study successfully optimized microfluidic biosensor parameters for faster detection.
• Confinement channel positioning is critical for improving biosensor speed.
• ANN models provide a reliable method for predicting microfluidic biosensor performance.