Taguchi optimization of integrated flow microfluidic biosensor for COVID-19 detection

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

Microfluidic biosensors offer a promising solution for real-time analysis of coronaviruses with minimal sample volumes. This study optimizes a biochip for the rapid detection of SARS-CoV-2 using the Taguchi orthogonal table L9(34), which comprises nine groups of experiments varying four key parameters: Reynolds number (Re), Damköhler number (Da), Schmidt number (Sc), and the dimensionless position of the reaction surface (X). Signal-to-noise (S/N) ratios and analysis of variance (ANOVA) are employed to determine optimal parameters and assess their impact on binding kinetics and response time of the detection device. These obtained optimal parameters correspond to Re = 4.10-2, Da = 1000, Sc = 105, and X = 1. Additionally, results highlight Da as the most influential factor, accounting for 91%, while X has a minimal effect of 0.3%. Furthermore, an artificial neural network optimization technique, specifically particle swarm optimization (PSO), was utilized to predict biosensor performance. Derived from the Full L81(34) design experiment, the PSO model demonstrates its effectiveness compared to the conventional multi-layer perception (MLP) model, thus underlining its potential in this innovative optimization context.

Perspectives on systematic optimization of ultrasensitive biosensors through experimental design

Biosensors have demonstrated versatility across numerous applications; however, their systematic optimization remains a primary obstacle, limiting their widespread adoption as dependable point-of-care tests. Experimental design, a powerful chemometric tool, offers a solution by effectively guiding the development and optimization of ultrasensitive biosensors. This perspective review provides an overview of recent applications of experimental design in the deployment of optical and electrical ultrasensitive biosensors. Various experimental designs, including full factorial, central composite, and mixture designs, are examined as systematic methodologies for optimizing biosensor fabrication, accounting for both individual variable effects and their interactions. Illustrative examples showcasing the optimization of optical and electronic biosensors through design of experiments are presented and critically analyzed. Finally, the future prospects of experimental design in the biosensor community are outlined, highlighting its potential to expedite development and bolster the performance of biosensing devices for point-of-care diagnostics, thereby facilitating their sustainable and reliable integration.

Optimizing PCF-SPR sensor design through Taguchi approach, machine learning, and genetic algorithms

Designing Photonic Crystal Fibers incorporating the Surface Plasmon Resonance Phenomenon (PCF-SPR) has led to numerous interesting applications. This investigation presents an exceptionally responsive surface plasmon resonance sensor, seamlessly integrated into a dual-core photonic crystal fiber, specifically designed for low refractive index (RI) detection. The integration of a plasmonic material, namely silver (Ag), externally deposited on the fiber structure, facilitates real-time monitoring of variations in the refractive index of the surrounding medium. To ensure long-term functionality and prevent oxidation, a thin layer of titanium dioxide (TiO2) covers the silver coating. To optimize the sensor, five key design parameters, including pitch, air hole diameter, and silver thickness, are fine-tuned using the Taguchi L8(25) orthogonal array. The optimal results obtained present spectral and amplitude sensitivities that reach remarkable values of 10,000 nm/RIU and 235,882 RIU-1, respectively. In addition, Artificial Neural Network (ANN) optimization techniques, specifically Multi-Layer Perceptron (MLP) and Particle Swarm Optimization (PSO), are used to predict a critical optical property of the sensor confinement loss (αloss). These predictions are derived from the same input structure parameters that are present in the full L32(25) design experiment. A genetic algorithm (GA) is then applied for optimization with the goal of maximizing the confinement loss. Our results highlight the effectiveness of training PSO artificial neural networks and demonstrate their ability to quickly and accurately predict results for unknown geometric dimensions, demonstrating their significant potential in this innovative context. The proposed sensor design can be used for various applications including pharmaceutical inspection and detection of low refractive index analytes.

Taguchi method: artificial neural network approach for the optimization of high-efficiency microfluidic biosensor for COVID-19

COVID-19 is a pandemic disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This virus is mainly spread by droplets, respiratory secretions, and direct contact. Caused by the huge spread of the COVID-19 epidemic, research is focused on the study of biosensors as it presents a rapid solution for reducing incidents and fatality rates. In this paper, a microchip flow confinement method for the rapid transport of small sample volumes to sensor surfaces is optimized in terms of the confinement coefficient β, the position of the confinement flow X, and its inclination α relative to the main channel. A numerical simulation based on two-dimensional Navier-Stokes equations has been used. Taguchi's L9(33) orthogonal array was adopted to design the numerical assays taking into account the confining flow parameters (α, β, and X) on the response time of microfluidic biosensors. Analyzing the signal-to-noise ratio allowed us to determine the most effective combinations of control parameters for reducing the response time. The contribution of the control factors to the detection time was determined via analysis of variance (ANOVA). Numerical predictive models using multiple linear regression (MLR) and an artificial neural network (ANN) were developed to accurately predict microfluidic biosensor response time. This study concludes that the best combination of control factors isα 3 β 3 X 2 that corresponds to α = 90 ∘ , β = 25 and X = 40 µm. Analysis of variance (ANOVA) shows that the position of the confinement channel (62% contribution) is the factor most responsible for the reduction in response time. Based on the correlation coefficient (R 2), and value adjustment factor (VAF), the ANN model performed better than the MLR model in terms of prediction accuracy.

Graphic abstract

Numerical optimization of microfluidic biosensor detection time for the SARS-CoV-2 using the Taguchi method

The performance of microfluidic biosensor of the SARS-Cov-2 was numerically analyzed through finite element method. The calculation results have been validated with comparison with experimental data reported in the literature. The novelty of this study is the use of the Taguchi method in the optimization analysis, and an L8(25) orthogonal table of five critical parameters-Reynolds number (Re), Damköhler number (Da), relative adsorption capacity (σ), equilibrium dissociation constant (KD), and Schmidt number (Sc), with two levels was designed. ANOVA methods are used to obtain the significance of key parameters. The optimal combination of the key parameters is Re = 10-2, Da = 1000, σ = 0.2, KD = 5, and Sc 104 to achieve the minimum response time (0.15). Among the selected key parameters, the relative adsorption capacity (σ) has the highest contribution (42.17%) to the reduction of the response time, while the Schmidt number (Sc) has the lowest contribution (5.19%). The presented simulation results are useful in designing microfluidic biosensors in order to reduce their response time.

Numerical simulation and optimization of AC electrothermal microfluidic biosensor for COVID-19 detection through Taguchi method and artificial network

Microfluidic biosensors have played an important and challenging role for the rapid detection of the new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Previous studies have shown that the kinetic binding reaction of the target antigen is strongly affected by process parameters. The purpose of this research was to optimize the performance of a microfluidic biosensor using two different approaches: Taguchi optimization and artificial neural network (ANN) optimization. Taguchi L8(2⁵) orthogonal array involving eight groups of experiments for five key parameters, which are microchannel shape, biosensor position, applied alternating current voltage, adsorption constant, and average inlet flow velocity, at two levels each, are performed to minimize the detection time of a biosensor excited by an alternating current electrothermal force. Signal to noise ratio ( S / N ) and analysis of variance were used to reach the optimal levels of process parameters and to demonstrate their percentage contributions, in terms of improved device response time. The principal results of this study showed that the Taguchi method was able to identify that the kinetic adsorption rate is the most influential parameter at 93% contribution, and the reaction surface position is the least influential parameter at 0.07% contribution. Also, the ANN model was able to accurately predict the optimal input values with a very low prediction error. Overall, the major conclusion of this study is both the Taguchi and ANN approaches can be effectively utilized to optimize the performance of a microfluidic biosensor. These advances have the potential to revolutionize the field of biosensing.