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Ben Romdhane I, Jemmali A, Kaziz S, Echouchene F, Alshahrani T, Belmabrouk H. Taguchi method: artificial neural network approach for the optimization of high-efficiency microfluidic biosensor for COVID-19. EUROPEAN PHYSICAL JOURNAL PLUS 2023; 138:359. [PMID: 37131342 PMCID: PMC10132959 DOI: 10.1140/epjp/s13360-023-03988-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 04/12/2023] [Indexed: 05/04/2023]
Abstract
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 is α 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
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Affiliation(s)
- Imed Ben Romdhane
- Laboratory of Electronics and Microelectronics, Faculty of Science of Monastir, University of Monastir, 5019 Monastir, Tunisia
| | - Asma Jemmali
- Laboratory of Electronics and Microelectronics, Faculty of Science of Monastir, University of Monastir, 5019 Monastir, Tunisia
| | - Sameh Kaziz
- Quantum and Statistical Physics Laboratory, Faculty of Sciences of Monastir, University of Monastir, 5019 Monastir, Tunisia
- Higher National Engineering School of Tunis, Taha Hussein Montfleury Boulevard, University of Tunis, 1008 Tunis, Tunisia
| | - Fraj Echouchene
- Laboratory of Electronics and Microelectronics, Faculty of Science of Monastir, University of Monastir, 5019 Monastir, Tunisia
- Higher Institute of Applied Sciences and Technology of Sousse, Sousse, Tunisia
| | - Thamraa Alshahrani
- Department of Physics, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Hafedh Belmabrouk
- Laboratory of Electronics and Microelectronics, Faculty of Science of Monastir, University of Monastir, 5019 Monastir, Tunisia
- Department of Physics, College of Science, Majmaah University, Al Majma’ah, 11952 Saudi Arabia
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