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Asefpour Vakilian, Keyvan, Moreau, Michel. (1404). An optimized machine learning-based unit for smart arsenic electrochemical biosensors. , 1(2), 127-132. doi: 10.22069/bere.2025.24166.1031
Keyvan Asefpour Vakilian; Michel Moreau. "An optimized machine learning-based unit for smart arsenic electrochemical biosensors". , 1, 2, 1404, 127-132. doi: 10.22069/bere.2025.24166.1031
Asefpour Vakilian, Keyvan, Moreau, Michel. (1404). 'An optimized machine learning-based unit for smart arsenic electrochemical biosensors', , 1(2), pp. 127-132. doi: 10.22069/bere.2025.24166.1031
Asefpour Vakilian, Keyvan, Moreau, Michel. An optimized machine learning-based unit for smart arsenic electrochemical biosensors. , 1404; 1(2): 127-132. doi: 10.22069/bere.2025.24166.1031

An optimized machine learning-based unit for smart arsenic electrochemical biosensors

Biosystems Engineering and Renewable Energies
دوره 1، شماره 2، آذر 2025، صفحه 127-132    اصل مقاله (330.68 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22069/bere.2025.24166.1031
نویسندگان
Keyvan Asefpour Vakilian* 1؛ Michel Moreau2
1Department of Biosystems Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
2Laboratory of Sensors Research, Agrocore Ltd. Company, Bordeaux, France
چکیده
Biosensing platforms are analytical devices that incorporate biological receptors to detect and quantify specific analytes with high precision. Despite their effectiveness, a major limitation arises from the gradual decline in the activity and stability of immobilized bioreceptors on the electrode surface over time. This degradation necessitates frequent replacement or recalibration, thereby increasing operational costs and hindering large-scale commercialization. In the present study, a three-electrode electrochemical biosensor modified with gold (Au) nanoparticles was developed for the selective detection of arsenite, the trivalent and highly toxic form of arsenic frequently found in contaminated water resources. To improve analytical performance and predictive accuracy, machine learning algorithms were integrated into the biosensor’s analytical framework. Electrochemical response data, solution pH, enzyme lifespan, and storage temperature were considered as input parameters for model development. To further enhance model efficiency, parameter optimization was performed using two metaheuristic algorithms: the firefly algorithm (FA) and the gravitational search algorithm (GSA). These were applied to tune the parameters of two predictive models: the adaptive neuro-fuzzy inference system (ANFIS) and the Random Forest (RF) model. Among the hybrid configurations tested, the FA-RF demonstrated superior performance, achieving a coefficient of determination (R²) of 0.93 and a mean squared error (MSE) of 0.007. These outcomes, derived from biosensor data collected over a 45-day operational period following enzyme and Au nanoparticle immobilization, highlight the potential of metaheuristic optimization and machine learning in enhancing the reliability, sensitivity, and lifespan of intelligent biosensing systems.

تازه های تحقیق
  • A three-electrode electrochemical arsenite biosensor modified with Au nanoparticles·        
  • Adaptive neuro-fuzzy inference system and random forest models were used·    
  • Electrochemical data, pH, enzyme lifespan and storage temperature were model inputs·       
  • The optimized RF showed superior arsenite prediction performance.
کلیدواژه‌ها
ANFIS؛ Artificial intelligence؛ Firefly algorithm؛ Gravitational search algorithm؛ Random forest
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