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

Kaziz, Sameh; Mariem, Ibrahim Ben; Echouchene, Fraj; Belkhiria, Maissa; Belmabrouk, Hafedh

doi:10.1140/epjp/s13360-022-03457-1

Cited by 19 publications

(8 citation statements)

References 40 publications

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“…The Taguchi method is a robust optimization technique that has gained widespread recognition in various fields for its ability to systematically optimize multiple parameters and their respective levels while minimizing the need for extensive experimentation 28 , 30 , 42 , 43 . When applied to the task of optimizing the structural parameters of the PCF sensor, which include the diameters of the air holes (d 1 , d 2 , d 3 ), the pitch (Λ), and the thickness of the silver (t Ag ), the Taguchi method provides an efficient approach.…”

Section: Resultsmentioning

confidence: 99%

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

Kaziz,

Echouchene,

Gazzah

2024

Sci Rep

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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.

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Section: Resultsmentioning

confidence: 99%

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

Kaziz,

Echouchene,

Gazzah

2024

Sci Rep

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“…Regression analysis is a statistical method for investigating and modeling the relationship between variables. 23,24 It is a frequently used statistical approach that functions by creating mathematical equations linking the answer (variable) to a group of independent or predictor variables. Using a linear connection with one or more predictors, linear regression is used to model the values of a dependent variable.…”

Section: Theory 21 | Mlr Modelmentioning

confidence: 99%

Multiple linear regression and artificial neural networks for highly selective cationic β‐diimine‐methallyl nickel (II) catalyst for styrene dimerization reaction to 1,3‐diphenyl‐1‐butene

Landolsi

Echouchene

Bajahzar

et al. 2023

Applied Organom Chemis

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The objective of the present study is to develop predictive models to input and output parameters for linear styrene dimerization reactions. These reactions involve the conversion of two styrene molecules into 1,3‐diphenyl‐1‐butene, which is a commonly used intermediate in the production of various industrial chemicals. Multiple linear regression (MLR) and artificial neural network based on multilayer perceptron (MLP) and radial basis function were used to model 1,3‐diphenyl‐1‐butne dimerization process in order to evaluate its performance. The neural network has been trained and tested by experimental data. The effect of various parameters (such as complex concentration) on styrene conversion (Conv%) and turnover of frequency (TOF) has been investigated in the proposed work. The results found by the proposed predictive models were analyzed and compared with the experimental results. Based on the comparison of the results, the radial basis function neural network (RBFNN) model outperformed the other models (MLR and MLP) with a correlation coefficient of (0.987 for Conv% and 0.947 for TOF) and lower root mean square errors for the output parameters. This result demonstrates that the RBFNN is an efficient technique to predict the styrene conversion and turnover frequency of the dimerization reaction. It was exposed that the control strategies learned are robust and can be transferred to similar dimerization reaction configurations.

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“…Several studies [ 28 , 29 ] have analyzed the effect of the reaction surface and electrodes shapes for a biosensor excited by an electrothermal force in order to improve the topology of the flow and thus decrease the detection time compared to the same biosensor having a rectangular binding surface. Recently, microfluidic biosensor have been proposed and numerically analyzed and optimized using the finite element method and Taguchi method [ 30 ]. Shahbazi et al [ 31 ] showed that the location of the reaction surface relative to the channel inlet has a significant effect on the microfluidic biosensor efficiency.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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(2 5 ) 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 ( ) 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.

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

Cited by 19 publications

References 40 publications

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

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

Multiple linear regression and artificial neural networks for highly selective cationic β‐diimine‐methallyl nickel (II) catalyst for styrene dimerization reaction to 1,3‐diphenyl‐1‐butene

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

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